How a few of these systems were actually built — the problem, the decisions I made and the alternatives I rejected, how I knew it worked, and what I'd do differently. Tap any one to read it in full.
The platform is the daily operational backbone of a real B2B distribution business across three verticals, and the sales team runs it in the field as a mobile app. What made it trustworthy was refusing to let order state live in scattered booleans: it's an explicit 13-stage machine in Postgres, so the pipeline can't stall in a stage nobody owns or fire a side effect twice.
A salesperson asks me where an order is, and the honest answer is that it depends who you ask. One vertical priced a job in a spreadsheet that lived on someone's laptop. Another tracked status in a chat thread. A customer calls to ask if their order cleared customs, and we have to chase three people to find out. Every quote was assembled by hand, every order's state was something a human remembered, and as volume climbed the remembering started to fail. Orders sat in a stage nobody noticed. Two people would update the same job and disagree about what was true. The business wasn't broken because the work was hard; it was breaking because nothing was the single source of truth, and the cost of that grew with every new order.
MAS Group is a B2B distribution business running three verticals at once: auto parts, print, and logistics. Each vertical prices differently, each order moves through a different real-world sequence of steps, and three kinds of people touch the same data: clients checking their own orders, sales reps quoting and managing them, and admins who see everything. The real constraints: I was building it solo to start, with no margin for a back office of operators babysitting state; the people using it daily are non-technical and use it on their phones in the field; and the side effects it fires are not cosmetic. When the system sends an SMS or kicks off a customs step, a real customer is on the other end. It has to be correct on a Tuesday afternoon with twenty things in flight, not just in a demo.
My role. I designed and built the platform end to end. I own the Postgres data model and the security policies, which is the part that decides whether the whole thing is trustworthy. I hire and direct the developers who extend it, and I set the boundaries they work inside so a feature doesn't quietly break the state machine or the access rules. I also train the sales team that actually uses it, which means I feel every rough edge directly when someone in the field tells me the app did something they didn't expect. The contractors add surface area; the correctness-critical core, the schema and the row-level security, is mine.
Model the quote-to-order lifecycle as an explicit 13-stage state machine in Postgres, with constrained transitions, as the single source of truth.
Why The failure I was solving was order state living in people's heads and in scattered booleans that could contradict each other. If the database is the one place state lives and transitions are constrained, two people can't disagree about what's true, and a side effect can't fire from an ambiguous state.
Rejected Tracking status with a handful of boolean flags (is_quoted, is_confirmed, is_shipped). It's the obvious quick path and what most spreadsheets-turned-apps do.
Trade-off With 13 named stages, the order can only ever be in one of them, and it can only move along an edge I declared legal. Adding a stage or resequencing the flow is a real migration, not a flipped checkbox. I wanted exactly that stiffness: a customs SMS fires from one specific transition and nowhere else, so it can't go off twice or off the back of a stage that was never really reached.
Compute pricing per line item rather than per order.
Why Auto parts, print, and logistics have genuinely different cost structures. A single order can mix them and still must roll up into one total and one commission calculation. Per-line pricing is the only model that survives all three verticals without special-casing the order level.
Rejected A per-order pricing function with vertical-specific branches. Simpler at first, but every new pricing wrinkle becomes a fork in one tangled function.
Trade-off More moving parts at the line level and more careful roll-up logic into the order total and commission. In exchange, each vertical's pricing stays isolated and a mixed order just works.
Enforce the three personas (client, sales rep, admin) with Postgres row-level security at the database layer, not by filtering in the UI.
Why A client must never see another client's orders. If that rule lives in the UI, one forgotten query leaks data. In RLS it's enforced no matter which query runs.
Rejected Filtering visibility in the application/UI layer, which is easier to write and reason about in isolation.
Trade-off RLS is harder to author and the policies interact in non-obvious ways. That interaction is exactly what later bit me with an infinite-recursion bug. I still chose it, because UI filtering is one missed WHERE clause away from a leak and RLS is not.
Build on managed BaaS (Supabase, Vercel), push business logic into RLS and SQL, and wire the automated logistics flow with Twilio, Google Apps Script, and n8n.
Why Solo and shipping fast, I didn't want to operate servers or hand-roll auth. Putting logic close to the data let one person own correctness end to end, and the automation tools let the logistics flow (orders to SMS updates to customs to delivery) run without a person pushing each step.
Rejected A traditional separate backend service holding the business logic, with its own infrastructure to run and secure.
Trade-off Concentrating logic in RLS and SQL means correctness lives in policies that are hard to reason about. I traded operational simplicity and speed for that reasoning cost, and paid part of that bill with the recursion bug.
Everything else hangs off knowing exactly what stage an order is in, so the state model is where I started. Pricing roll-ups, access rules, the logistics automation: none of them mean anything until the 13 stages and their legal transitions exist in the database and are impossible to violate. So before a single screen got drawn, the stages and their constraints went into SQL migrations kept in the repo, the schema's history versioned rather than clicked into existence and forgotten.\n\nWith the state machine trustworthy, I layered pricing on top: per-line calculators for each vertical rolling up into one order total and one commission figure. Then the access model, the three personas enforced in row-level security, so visibility was correct at the data layer before I ever shaped a screen around it. Only after that did the client get built: React and TypeScript with TanStack Query handling server state, deployed on Vercel, used as a mobile app by the sales team in the field.\n\nThe automated logistics flow came last because it depends on all of the above being solid. On confirmation, an order moving into the right stage fires the downstream sequence: SMS updates to the customer over Twilio, the customs step, delivery. That orchestration is wired with Twilio for messaging, plus Google Apps Script and n8n handling the workflow glue and integrations between the moving parts. The ordering was deliberate: side effects that reach a real customer only got switched on once the state they fire from was something I trusted.
Success here isn't an offline metric, it's an operational one: can non-technical people run the full quote-to-order lifecycle all day without the system wedging, double-firing a customer SMS, or showing someone data they shouldn't see. I defined "working" against the single operation that mattered most, a quote progressing to a confirmed, shipped order, and I evaluated it the only honest way available: putting it in front of the actual sales team and watching what broke in the field.\n\nBefore, that operation was a manual assembly of a spreadsheet quote plus by-hand status tracking plus chasing people for state. After, it's one path through an enforced state machine where each transition either is legal or is rejected, and the right side effects fire exactly once at the right stage. The before/after that matters: previously a customer's status question meant a human investigation; now the stage is a fact the system holds and the customer gets SMS updates without anyone pushing them.\n\nThe hardest eval was negative, proving the access rules couldn't leak and couldn't deadlock. That's where the strongest signal came from, because it surfaced a real failure: a Postgres RLS infinite recursion. One table's policy referenced a second table, whose own policy referenced back, and policy evaluation looped on itself. I diagnosed it down to that circular reference and fixed it with a SECURITY DEFINER function that performs the needed check while bypassing the recursive policy evaluation, keeping the access guarantee intact without the loop. The verification was direct: exercise the personas, confirm each sees exactly its own slice and nothing more, and confirm the query no longer recurses. The team running it daily without status drift or visibility complaints is the standing eval that it holds.
Pushing security and business logic into RLS and SQL was the right call for shipping fast as one person, but it concentrates correctness in policies that are hard to reason about, and the infinite-recursion bug was a direct symptom of that. The policies interact, and those interactions aren't visible until they break. What I'd do differently: invest earlier in a test harness that exercises RLS per persona, so that policy interactions surface in CI instead of in production when a query starts recursing. Right now my strongest regression check is the team using it, which is real signal but a slow and human one. I'd also flag that the 13-stage model, while deliberately rigid, means changing the pipeline's shape is a migration every time; that rigidity has been worth it, but it's a real cost I chose, not a free win.
The platform is live at masgroup.is and is the operational backbone of a real B2B business across auto parts, print, and logistics. It replaced manual quoting and spreadsheet tracking, and the sales team runs it daily in the field as a mobile app. Order state used to live in four places at once: a spreadsheet, a chat thread, someone's memory, and whoever you happened to ask. It now lives in one, the database, and that collapse is the whole result. You can feel it most when a customer asks where their order is. Nobody starts an investigation anymore. The stage is a fact, and the customer has already been getting SMS updates without anyone remembering to send them. The part of the job that used to fail as volume grew is the part the system quietly absorbed.
Put the source of truth where it can't be contradicted, then make the rigidity earn its keep. An explicit state machine in the database is harder to change than a pile of booleans, and that's exactly why it's trustworthy: the constraint that annoys you on a slow day is the one that saves you when twenty orders are in flight. You don't get a system you can lean on by keeping your options open; you get it by closing the wrong ones off in the schema, where no busy afternoon can reopen them.
Scripted status calls and order SMS now run unattended inside the logistics flow; the two-way realtime agent is a working build still hardening. What makes it safe to point at real customers: the model only phrases and routes, and deterministic code performs every action it would otherwise take.
Every order update was a person picking up the phone or typing a message, one at a time. \"Your shipment cleared customs.\" \"It's out for delivery.\" The same handful of sentences, in two languages, all day. The repetitive ones aren't hard, they're just relentless, and they crowd out the calls that actually need a human. I wanted to take that load off a person. The part that kept me up was the obvious failure mode: an AI on a live call inventing a delivery date or agreeing to something we'd then have to call back and retract. A bad automated message to a real customer is worse than no automation at all. So the bar wasn't \"can it talk\" — it was \"can it talk without ever committing the business to something it shouldn't.\"
This sits inside MAS Group's logistics flow — orders come in, the customer gets messaged, the shipment moves through customs to delivery, and status updates fire along the way. The stakes are that these go to real customers, not a demo audience, so a wrong or invented message has a real cost. The constraints that shaped everything: it had to run against production traffic safely; it had to handle both English and Polish; live voice has a hard round-trip latency budget that a human ear notices instantly; and classic telephony (Twilio's webhook + media-stream model) speaks a fundamentally different language than a streaming LLM's bidirectional audio socket. I had to make those two meet without the call sounding broken.
My role. Sole engineer. I designed and built all of it: the realtime audio bridge between Twilio's media stream and the OpenAI Realtime API, the scripted one-way call path, the SMS automation woven into the order-to-delivery flow, the WhatsApp bot in the same family, and the dev tunnel for local iteration. There were no other devs on this — the architecture decisions, the reliability stance, and the trade-offs are mine.
Split the work into two call paths by cost and risk: simple one-way scripted TTS for status calls, and a hard two-way interactive agent only where conversation is actually needed.
Why Most of the load is one-directional — 'here's your status.' Forcing every call through a live LLM would burn latency budget and money on messages that don't need intelligence, and it would expand the surface where the model could go off-script. Tying the path to the risk of the message keeps the dangerous capability scoped to the few flows that earn it.
Rejected One unified interactive agent for every call. Rejected because it's more expensive per call, slower, and puts a generative model in the loop for messages where a fixed script is both cheaper and safer.
Trade-off Two code paths to maintain instead of one, and a routing decision up front about which path a given flow takes.
The model phrases and routes; it never executes. Deterministic code owns every order change and every send.
Why This is the whole reason it's trustworthy enough for production. The failure mode I cared about — an AI making a commitment the business has to retract — becomes structurally impossible if the model literally cannot perform an action. It produces language and picks a route; the actual order mutation and the actual message send go through deterministic systems with their own guards.
Rejected Letting the agent call tools that directly mutate orders or trigger sends. Rejected because one hallucinated tool call against a real customer's order is exactly the outcome that makes automation a liability instead of an asset.
Trade-off The agent is narrow on purpose. It can't improvise its way out of a situation the scripted flows don't cover — off-script callers fall back rather than being handled cleverly.
Build the realtime bridge directly against Twilio's media stream and the OpenAI Realtime WebSocket in Python, while using RetellAI for some flows.
Why Whether a call sounds like a conversation or a walkie-talkie is decided at the level of codecs, audio framing, and turn-taking, and going direct is the only way I get to own those. Barge-in and the audio buffering are mine to tune against the latency budget instead of a vendor's to approximate. RetellAI earned its place on the flows where its abstraction was good enough and quicker to stand up.
Rejected Routing everything through a managed voice-agent platform. For the hard interactive path that's a non-starter: the call-quality problem lives precisely in the low-level framing and barge-in control a managed platform hides from you.
Trade-off Owning the bridge means owning its failures — matching codecs and framing without buffer underruns, detecting when speech starts, cutting the agent off the instant it's interrupted. More control bought with more of the hard part landing on me when it breaks.
Expose the local realtime server to Twilio webhooks over an SSH tunnel during development.
Why The realtime bridge needs public webhooks to receive calls. A tunnel let me iterate against real Twilio traffic without a deploy every cycle, which is the difference between tightening turn-taking in minutes versus in deploy-length increments.
Rejected Deploying to a real environment on every iteration. Rejected purely for iteration speed — the feedback loop on audio timing is too tight to wait on deploys.
Trade-off The tunnel is a dev convenience, explicitly not a production posture. It's iteration speed, not how this is meant to run live, and I keep those two things separate in my head and in the setup.
The audio bridge was always the part that would decide whether this project lived or died, so I want to describe it first even though it wasn't first to ship. Twilio's telephony model (a webhook in, a media stream carrying the audio) and the LLM's bidirectional audio WebSocket are two different worlds, and the entire job is making them meet in real time. A small Python server holds the WebSocket open to the OpenAI Realtime API and pumps its audio across to Twilio's media stream, matching codecs and framing so nothing underruns the buffer mid-sentence. Sitting on top of that plumbing is the part a caller actually feels: latency and turn-taking. The agent has to stream audio out, notice the instant the caller starts talking, and barge-in — go silent the moment it's interrupted — all inside a live-call round-trip budget a human ear polices in milliseconds. Miss that and you've built a walkie-talkie. Hit it and you've built a conversation.\n\nWhat I actually shipped first, though, was the low-risk thing that delivered value on day one: the scripted one-way path. TwiML driving Amazon Polly TTS, multilingual EN/PL, for order-status calls. With no generative model anywhere in that loop it could go straight at the logistics flow, which let it prove out the telephony plumbing and the integration into the order-to-delivery sequence before I committed to anything harder. SMS slotted into the same flow — order events firing messages — and a WhatsApp bot grew in the same family.\n\nUnderneath both paths the reliability boundary stayed fixed: flows scoped so the model can't invent a commitment, deterministic code performing every action. The SSH tunnel is what made the turn-taking loop tight enough to actually tune against live calls.
Success here wasn't a benchmark score, it was an operational bar: does the repetitive on-script communication come off a person, and does the system never make a commitment the business has to retract? I'll be honest about what's measured and what's a posture. On the scripted-call and SMS paths, the eval is operational — they run unattended in the order-to-delivery flow, against real customers, in production. That's the before/after on one operation: status updates that used to be a person dialing or typing each one now fire from the flow without a human in the loop. The proxy for \"it works\" is that it runs daily inside live logistics traffic, in two languages, without supervision.\n\nThe two-way interactive agent is held to a stricter, structural eval rather than a metric: by design the model cannot execute an action, so the worst-case failure (a hallucinated commitment to a real customer) is prevented by architecture, not by hoping the model behaves. The way I tuned the conversational quality was iterative against real Twilio calls over the tunnel — rounds of tightening codec/framing handling and barge-in timing until interruptions felt natural. I'm not going to attach a latency number or a call-volume figure I can't stand behind; the verifiable facts are that the scripted and SMS paths are in production use and the realtime bridge is a working build still hardening.
The thing that makes it trustworthy is the same thing that makes it narrow, and I want to name that plainly. Because the flows are tightly scoped and the model can't improvise an action, an off-script caller doesn't get a clever save — they fall back. That's a deliberate trade, but it's a real limitation: this is not a general-purpose phone agent, it's a tightly-bounded one. The interactive realtime bridge is a working build still hardening, not a finished, battle-tested production service — the scripted TTS and SMS paths are the parts I'd call production-solid today. And the SSH tunnel is dev iteration speed, full stop; it is not a production deployment posture, and conflating the two would be the kind of mistake that bites you later. If I were hardening the realtime path for full production, the tunnel is the first thing that goes. What I would keep is the boundary — model phrases and routes, deterministic code acts. I'd keep that even though it's the source of the narrowness, because the alternative reintroduces exactly the failure I built the whole thing to avoid.
The scripted-call and SMS paths run unattended inside MAS Group's order-to-delivery logistics flow, in English and Polish, against real customers. The interactive realtime agent handles two-way calls and is still hardening. The payoff is plain to see in how a day now feels: the steady drip of status updates that used to eat a person's afternoon one call and one message at a time has come off that person entirely, and it came off without the failure mode that makes business automation dangerous — an AI committing to something the company then has to walk back. A whole category of work that was 100% manual now fires from the flow on its own, every day, in two languages. And the calls a person still answers are the ones that genuinely needed a person, not the fortieth \"your order shipped\" of the afternoon.
Scope is what makes a generative model trustworthy in production, and scope is also what makes it narrow — those are the same decision, not two. Let the model phrase and route; make deterministic code do every action. You trade cleverness on the edge cases for never having to call a customer back to retract what your AI promised. For a business talking to real people, that's the right trade, and I'd make it again before I'd make the call sound a fraction cleverer.
I have agents that draft outbound email overnight, watch repos, and log my own work sessions without me babysitting them. What made it real was drawing a hard line: facts and state live in code and data, and the model is left to assemble and phrase. That split has held up in daily use across several of my own ventures.
The first time I left an agent running overnight to draft outbound email, I came down in the morning to a job that had died around 3am on a rate limit and a half-finished queue with one duplicate already sent. Nothing had crashed loudly. It had just stopped, mid-loop, with no idea where it had been or whether the last action it took had actually gone through. I had assumed \"leave it running\" was a configuration problem. It was an architecture problem. An agent that forgets everything between runs, needs a human watching the window, and treats an API limit as a fatal error is not a worker. It is a demo that happens to use my API key. For any of this to earn a place in how I actually run my businesses, it had to remember, run while I sleep, and pick itself back up after the API tells it to wait.
This is my own infrastructure, not a product I shipped to a client. It is the runtime that a few of my ventures quietly lean on for the unglamorous recurring work. The real constraints shaped everything: I am one person, so anything that needs a human in the loop at 3am does not get built; I am orchestrating foundation models over an API I do not control, so rate limits are a fact of life rather than an edge case; and the agents touch things with real consequences, like sending mail and acting on repos, so a hallucinated argument or a re-sent email is not a cosmetic bug. The whole design is a response to those three pressures.
My role. Sole architect and engineer. There were no hired devs or contractors on this. I designed the runtime, wrote the Python orchestration and the custom MCP servers, set up the vector store and the per-domain namespacing, built the scheduler that fires tasks unattended, and built the checkpoint-and-resume recovery that survives rate limits. The judgement calls below — where to draw the line between what the model decides and what code decides, what to compress and what to keep verbatim, how to make a job idempotent — are mine.
Split responsibility: the model assembles and phrases, code and data own facts and state. Agents answer from retrieval over a vector store, not from open generation.
Why The consequential actions here are sending mail and acting on repos. If the model is the source of truth, a confident hallucination becomes a sent email. Tying every answer to retrieved facts and letting deterministic code commit every state change keeps the system auditable despite a probabilistic reasoning layer in the middle.
Rejected Letting a capable model free-generate with tools and trusting it to be right most of the time. It is faster to build and demos beautifully.
Trade-off More moving parts and more plumbing. The model is no longer allowed to be clever on its own; it has to cite, and code gets the final say. I gave up some fluency and ease of build to get answers I can trace back to a source.
Treat rate-limit survival as checkpoint-and-resume, with every step made idempotent, rather than retry-until-it-works.
Why The whole project started because a job hit a limit and left a duplicate behind. A naive retry walks back over side effects that already fired and sends the same email a second time. So the loop persists its progress to disk after each step, and every step is written to be safe to run again. When the next window opens, the job restarts from its last checkpoint and steps over anything it has already committed, so the email it already sent is never re-sent.
Rejected Exponential-backoff retries, or just sizing every job small enough to finish inside one rate-limit window. Backoff replays a side effect that already half-happened; sizing-down silently puts a ceiling on what an overnight job can ever accomplish.
Trade-off This forces the agent loop to be a resumable state machine, and I have to serialize enough state that a fresh process can land exactly where the old one stopped. It is real work, and it taxes every new action type, because for each one I have to define precisely what 'already sent' or 'already done' means before I can make it safe to resume.
Namespace the memory per knowledge domain and route each query to its namespace, instead of one shared index.
Why With everything in one pool, retrieval bleeds context across unrelated domains and the agent starts citing the wrong world. Partitioning by domain and routing the query keeps retrieval clean so an agent only pulls from the domain it is actually working in.
Rejected A single flat index with metadata filters bolted on at query time. Simpler to set up, one place to write to.
Trade-off I own the routing now. Every write and every read has to know which namespace it belongs to, and a misrouted query fails quietly rather than loudly. The discipline is the price of clean retrieval.
Put tools behind custom MCP servers with server-side validation, so a malformed or hallucinated argument fails loudly at the boundary.
Why The model will eventually produce a wrong-shaped argument. If that flows straight into an action, I find out by its consequences. A validated tool contract turns a bad call into a clean, visible failure instead of a silent misfire.
Rejected Calling tools directly from the agent loop with light or no validation, trusting the model to format arguments correctly.
Trade-off Every tool is now a small contract I have to define and maintain. More upfront ceremony per tool, in exchange for failures that surface at the door rather than three steps downstream.
The build order followed a single rule: prove the cheapest-to-get-wrong, most-consequential thing first, then only let the next layer on once the one beneath it was trustworthy. So I started with the grounding stance on a single agent: answer only from retrieval over the vector store, never from open generation, and cite where the answer came from. With retrieval honest, I added per-domain namespacing and query routing, because the instant a second domain existed the single agent began pulling the wrong context, exactly as I expected.\n\nThen I went after the thing that had actually burned me, the checkpoint-and-resume recovery. This is the piece I am proudest of and the one with the least margin for error. I made the long-job steps idempotent one action type at a time, beginning with email drafting, since that is where a duplicate is most visible and most embarrassing. Each step writes a checkpoint before it commits, and on restart the loop reads that checkpoint and skips anything already done. The test was blunt: I killed the process partway through an overnight queue and started it again, and watched whether it landed on the next step or re-sent an email it had already sent. Once a job could survive being killed, I let the scheduler fire it unattended, because scheduling a job that cannot recover just means failing on a timer instead of failing at random.\n\nThe rest hung off that spine. For long-running agents I added context compression: older history folds into running summaries that keep the load-bearing facts, so a run stays coherent inside the token window without me re-feeding the whole transcript. Custom MCP servers arrived as I wired up real tools, each one a validated contract so a bad argument dies at the boundary. In multi-agent flows the shape never changed: the model proposes, deterministic code validates and commits, with n8n as the glue between pieces. The artifacts are deliberately unglamorous, which is the whole point: an overnight email-drafting run, a repo watcher, a session logger that records my own work, all reading and writing the same namespaced memory.
I will be honest about where this stands, because the corrections matter more than a flattering number. There is no formal automated eval harness yet, and I am not going to invent a metric to pretend otherwise. Success was defined operationally, on one concrete behavior: can a long job survive a rate-limit boundary without re-running a side effect. I tested that the blunt way, by killing the process mid-run and restarting it, and watching whether it resumed on the correct step or re-sent something it had already sent. Before the resumable-state-machine work, a kill mid-run meant a dead job and a duplicate; after, the job picks up where it stopped. That before/after is the spine of why I trust it. The other bar is grounding: answers come from retrieval and they cite, so I can check a claim against its source rather than against the model's confidence. The honest proxies for \"it works\" are operational, not statistical: it runs scheduled and unattended, it has held across several of my own ventures, and the team runs the daily automations on it without me hovering. What holds the quality bar today is the combination of grounding, structured output, and human review, not an automated regression suite. That is a real gap, and I name it rather than paper over it.
The biggest honest limitation: this is applied orchestration of foundation models over an API. I am not training or fine-tuning anything. The intelligence is rented; what I built is the runtime, the memory, the recovery, and the guardrails around it. Second, and the thing I would fix first if I rebuilt it: there is no formal automated eval harness. I lean on grounding, structured output, and human review to hold the line, which works in practice but means a regression can slip in and I would only catch it by noticing bad output, not by a failing test. Third, namespaced routing puts the burden on me to route correctly, and a misrouted query fails quietly rather than loudly, which is the opposite of how I made the tool contracts behave. Fourth, idempotency is not free: every new action type forces me to define what \"already done\" means for it, so the system gets more expensive to extend exactly where it is most consequential. If I started over, the eval harness comes first, before features: a regression suite scoring grounding accuracy and resume correctness on every change, so I am measuring instead of trusting.
The morning after I finally got resume working, I came downstairs to a completed overnight queue and zero duplicates. That sounds small written down. It was the exact failure that had started the whole project, finally not happening: the job that used to die at 3am on a rate limit and leave a re-sent email behind had instead waited out the limit and picked back up on the right step. What I have now is a working personal agent platform that runs scheduled, memory-backed automations unattended: drafting outbound email overnight, watching repos, logging my own work sessions, and carrying context across runs instead of starting blank each time. The split between what the model phrases and what code commits has held in daily use across several of my own ventures, which for one-person infrastructure is the only validation that counts: it keeps running without me in the loop.
An autonomous agent is not a smarter model, it is a system that survives the model's worst moment. Decide up front what the model is allowed to be wrong about, give it the phrasing and nothing load-bearing, and let code and data own every fact and every committed action. The recovery work is where that belief gets tested: a job is only trustworthy once you can kill it mid-run and trust it to come back without repeating itself.
I shipped an assistant on my own portfolio that answers a recruiter's questions and does an honest role fit-check. The decision that made it safe: it speaks only from a closed, vetted world, so when it doesn't know a fact it says so and routes to me instead of inventing one. An adversarial review failed my first draft and caught four concrete defects before any recruiter saw them.
Here's the failure I was actually afraid of. A recruiter opens my site, types "does he have a C1 in German and five years of Kubernetes," and a chatbot wearing my name cheerfully says yes. I never claimed either. Now there's a transcript of my own website lying about my credentials, and the first thing the recruiter learns about me is that I ship things that overclaim. The machine meant to make me look trustworthy is the one thing best positioned to torch that trust. A public model that talks as if it were me isn't a feature by default. It can hallucinate a qualification, surface something I'd never put in writing, get talked into "developer mode," or gush so hard the whole thing reads like marketing nobody believes. I didn't want a demo of how clever I am with LLMs. I wanted something a hiring manager could rely on before deciding whether I'm worth an hour.
The system is my portfolio at kamiljan.com, with an assistant a recruiter can talk to before inviting me to interview. It answers questions about my work, gives an honest role fit-check, and routes to contact. The stakes are reputational, not financial, which is exactly what makes them unforgiving. There's no "mostly correct" here: one fabricated credential or one leaked private detail is the whole impression. The real constraints were that correctness is binary because the bot makes claims as a real person; it has to resist adversarial visitors, since anyone can type anything into a public box; it runs on a small, fast model at the edge, so I couldn't lean on a frontier model's judgment to save me; and it had to be honest in an uncomfortable way, surfacing my actual gaps and not only my highlights.
My role. Solo. I designed it, built it, hardened it, and shipped it. No contractors, no second engineer. The closed-world grounding, the security and prompt-injection rules, the server boundary that keeps the API key off the browser, the email lead funnel, the bilingual routing, the Three.js hero, the pre-commit build guard, and the adversarial review that failed my own first draft were all mine. I'm naming the scope because the trade-offs below were my judgment calls to own, not committee decisions.
Ground the bot in a closed world (a vetted system prompt) instead of running open RAG over my own documents.
Why For a bot that speaks as a real person, the dangerous failure isn't "can't answer," it's "answers wrong with confidence." A closed world means every fact the bot can state is one I deliberately wrote down and approved. If a question falls outside it, the bot says it doesn't have that and points the recruiter to me. This is downstream of the correctness-is-binary constraint: one fabricated credential poisons the whole impression.
Rejected Open RAG over my CV, notes, and project docs. It's the obvious move and it demos beautifully. I rejected it because retrieval plus a generative model is a fabrication-and-leak surface: it can stitch a plausible-but-false claim from two unrelated chunks, and it can surface a private line I forgot was in the corpus. For a bot wearing my name, that's the exact risk I was trying to remove, not add.
Trade-off The bot is genuinely narrower. It can't riff on anything I didn't pre-load, and sometimes it has to say "I don't have that, here's how to reach Kamil" where a RAG bot would have improvised. I traded coverage for the guarantee that it cannot make something up. For this use case that's the right side of the trade. For an internal docs-search bot it would be the wrong one.
Call the model only behind a server boundary, never from the browser.
Why The model is invoked through a TanStack `.server.ts` function running on Cloudflare Workers, so the API key lives at the edge and never ships to the client. A key in client code is a key that gets scraped and billed against by someone else within days.
Rejected A direct client-to-LLM call, which is simpler and removes a hop. Rejected outright: there's no way to keep a secret in code that runs in a stranger's browser. "Obfuscate it" is not a security model.
Trade-off Every model call now pays a round-trip through my Worker, and I own that function's reliability and its abuse surface. That's strictly more code to maintain than a direct call. It's the cost of the key never leaving my control, which wasn't negotiable here.
Treat visitor input as data, not instructions, and harden against jailbreaks explicitly.
Why It's a public text box, so people will try to break it. The bot refuses to reveal its prompt, refuses to role-play as me, refuses "developer mode," speaks in third person only, and hard-blocks a small set of topics (salary, start date, private clients, a small set of personal-logistics topics). Third person is a deliberate lever: if the bot never speaks as "I = Kamil," a whole class of impersonation and put-words-in-his-mouth attacks just doesn't land.
Rejected A lighter "be helpful and use good judgment" instruction, trusting the model to behave. On a small, fast model that's wishful thinking. The model's judgment is not the reliability layer here; the explicit rules are.
Trade-off The bot is more rigid and will occasionally refuse something innocent that happens to resemble a blocked topic. I'd rather it read as slightly stiff than be the website that leaked a private detail to whoever typed the right sentence.
Require honest gaps in the recruiter fit-check, not only upside.
Why An all-positive read of a candidate is indistinguishable from marketing, and recruiters discount it instantly. So in fit-check mode the bot has to name a real limitation against the role, not just strengths. The honesty is the feature; it's what makes the rest of the answers credible.
Rejected The flattering version that only sells. It tests better in a naive demo and it's worthless in practice, because the reader's trust collapses the moment they notice nothing it says is falsifiable.
Trade-off My own portfolio bot will, by design, tell a recruiter where I'm not a fit, and that can cost me a conversation. I decided a recruiter who self-selects out on accurate information is a better outcome than one who feels misled in the first interview.
I built it in order of what could hurt most, smallest unit first. The first real artifact was the system prompt itself, because in a closed-world design the prompt is the product: it's the grounding, the security policy, and the persona in one file. I drafted it, then deliberately tried to break it before trusting it. The lead funnel came next as its own end-to-end slice: a visitor message hits a server function that builds a short AI brief of the lead, which is then emailed to me. I used the Resend HTTP API rather than SMTP on purpose, because SMTP doesn't work cleanly from inside a Cloudflare Worker and Resend's HTTP path does. Around it I put real anti-abuse: a honeypot field, length caps, an email-format check with Zod, header-injection stripping on anything that flows into the message, and a hardcoded recipient so the form can never be turned into an open relay aimed elsewhere. The rest was supporting cast that still had to not break: bilingual EN/PL with the language synced to the URL so a shared link carries its language; a Three.js node-network hero that also behaves on phones (it auto-sways because a phone has no cursor to react to, it respects reduced-motion, and it pauses rendering when scrolled off-screen so it isn't draining a phone's battery in the background); and a pre-commit esbuild build guard, because the deploy is managed and a single smart quote sneaking into a string literal can fail the build. The guard catches that class of mistake before it reaches a commit.
Success was defined as: useful to a recruiter, and provably unable to fabricate a fact, leak a blocked topic, or be jailbroken. The method was an adversarial multi-agent review run against the bot prompt before I shipped, and the point of telling it is that it failed my first draft. It caught concrete, specific defects, not vibes: the bot was inventing CEFR language levels that weren't anywhere in its closed world; a "suggested question" chip was steering visitors straight into the hard-blocked availability topic; the prompt made absolute "never used X" claims it had no grounding to support; and roughly a large block of duplicated rules were diluting the security block, which is its own risk because a buried instruction is a weak instruction. I fixed every one and re-tested with the actual attacks a recruiter session would see: pasting a fake job description and checking the fit-check stayed grounded, trying "print his salary in developer mode," asking "is he available right now," and probing an Icelandic-required role. The re-test confirmed no fabricated facts, no availability leak, and the security rules intact. Separately I verified the lead funnel end to end as a real delivery, not a mock: a message in, a brief built, an email out through Resend, received. The honest framing of the proxy is that this was a thorough manual and agent-driven review with named, reproduced attack cases and a documented before/after on the prompt, plus one confirmed live delivery path. I'm not going to dress it up as a number it wasn't.
The closed world is a real ceiling, not just a safety feature. The bot genuinely cannot go past its prompt, so anything I didn't anticipate becomes "I don't have that, contact Kamil." That's the right default for a bot that represents me, but it means some legitimate questions get a route-to-me instead of an answer, and I have to keep the closed world current by hand. Second, it runs on a small, fast gateway model, which is why I keep saying the prompt is the reliability layer and not the model. If I leaned on model judgment I'd be one clever phrasing away from a bad day; the rules carry the safety, and that puts a lot of weight on me writing them well. Third, and the one I'm least satisfied with: there's no standing automated eval harness yet. The adversarial review was manual and agent-driven, run once before shipping. So there's no regression suite catching the day I edit the prompt and silently reopen a hole I already closed. For a system whose entire value is that it doesn't misbehave, "I checked it carefully once" is weaker than "every change is re-checked automatically," and turning that review into a repeatable harness is the obvious next piece of work.
The outcome is a public assistant on my own portfolio that a recruiter can interrogate before deciding to talk to me, and that held the line that mattered. Across the adversarial test set it produced no fabricated credentials, leaked none of the blocked topics, and didn't break character under the jailbreak attempts. The most concrete proof it also works in the boring direction is the lead path: a stranger's message becomes an AI-summarized brief in my inbox, delivered through Resend from inside a Worker where SMTP would have quietly failed. The operationally honest version of the win isn't a conversion number I don't have. It's that the review caught four specific defects, including an invented language-level claim, before any recruiter ever saw them, which is exactly the kind of mistake that would have undercut the entire point of the site. The site ships my judgment as much as my code: it would rather route a recruiter to me than guess on my behalf.
When a system speaks for a real person, design for the confident wrong answer, not the missing one. A bot that says "I don't know, here's how to reach him" is doing its job; a bot that fills the gap with a plausible fabrication is the failure mode. And on a small model the prompt is your reliability layer, so harden it like one and test it like an adversary before you trust it.
A SaaS backbone that turns a services agency into a repeatable multi-country pipeline: correct local pricing, a tracked lead-to-contract flow, and a VAT-aware e-signed billed agreement per market. The decision that drove it: treat a contract as a function of structured data, not a stack of templates, so adding a country is a data change rather than a code change.
The first time I tried to sell the same web package in a second country, the whole thing quietly fell apart. The price was wrong for that market. The tax line was wrong. And the contract I sent was, legally, the wrong document — it referenced the home market's terms and the home market's VAT, because that's the only contract I had. I caught it before it went out, but only because I happened to reread it. That's the moment it stopped being a copywriting problem and became a systems problem. I was running the path from cold enquiry to countersigned deal by hand: a quote in one place, a contract pasted together in another, a payment link improvised after signing. It worked while it was one market and a handful of deals. The instant I tried to systematize it across borders, every shortcut I'd been getting away with turned into a way to send a client the wrong, legally binding paper.
Reykjawwwik is a web and design agency, and this is the SaaS platform that runs it end to end: a multi-market pricing engine across 10 countries with geo-detection, a lead-to-contract pipeline with an admin CRM, server-side contract generation with per-country VAT logic, and push notifications, on React, TypeScript, Supabase, and Vercel. The stakes are specific: the output is not a marketing page, it's a binding agreement. Get the VAT treatment or a mandatory clause wrong and you've shipped a defective contract to a real client in a country whose rules you have to respect. The real constraints were three. It had to be correct per market, where "per market" means different tax handling and different mandatory contract language, not just a translated string. It had to be operable by a small team, not a back office. And it had to make adding the next country cheap, because the entire reason to build this instead of doing deals by hand was to make market number eleven nearly free.
My role. I'm the founder and system architect of the agency this platform runs. The architecture was mine: modeling the contract as data, choosing where the state machine lived, deciding what to build versus buy. I directed the developers who implemented it rather than writing every line myself, so my job was the decisions and the boundaries, and theirs was the build. I also ran sales personally, which is the part that mattered most for design, because I was the first and harshest user of my own pipeline. Every place the flow was annoying or produced a wrong document, I hit it myself on a live deal, and that fed straight back into what we changed.
Model the contract as a function of structured data, not a library of templates: country, package, and VAT in, a localized PDF/DOCX out, generated server-side.
Why The hard constraint was per-country legal correctness across 10 markets with the demand that the eleventh be cheap. Templates make the contract a copy you maintain per market, so correctness degrades every time the base terms change. Treating it as data keyed by country means a market is a row of rules, and adding one is a data change.
Rejected A folder of per-country contract templates with merge fields, which is the obvious first move and the fastest to ship for market one.
Trade-off Heavier upfront modeling of what actually varies between markets (tax treatment, mandatory clauses, formatting) before I could generate a single document. I paid that cost knowing it only pays back across many markets, not the first.
Make the lead-to-contract flow an explicit state machine in Postgres, and let the two external providers report into that state instead of being the state.
Why Generate, sign, and bill are irreversible steps that depend on systems I don't control. The e-sign provider and the billing provider each fire their own webhooks on their own schedule, and those two events have to add up to one outcome: deal done. If I let the providers' callbacks be the truth, a billing timeout in the seconds after a signature leaves a client signed but never invoiced, with nothing in my system that knows the deal is half-finished.
Rejected Inferring deal status from whatever the e-sign and billing vendors reported, stitching their two webhook streams together at read time and trusting whichever fired last.
Trade-off I carry a state model that has to be continuously reconciled against two independent external systems, which is more code than reading their dashboards. What it buys is that a deal which stalls between signing and billing sits in a known, named state I can resume from, instead of disappearing into the gap between two vendors.
Buy auth, payments, and e-signature; build only the pricing-and-contract engine in-house.
Why The constraint was a small team shipping fast. E-signature and billing are deep, compliance-laden problems where a vendor is years ahead. The pricing-and-VAT-and-contract logic is the actual product and the part nobody else can get right for my markets, so that's where my engineering went.
Rejected Building e-signature and billing in-house for full control over the data model and the webhook behavior.
Trade-off I inherited two vendors' data models and their webhook quirks, and real work went into reconciling their callbacks against my own state. For SME volume that was the right trade; it's the line item I'd revisit at high volume.
Detect the market at the edge, default to the inferred country to drive pricing through i18n, and always expose a sticky manual override.
Why Geo-detection has to be right often and never trap anyone. Wrong-market pricing with no escape hatch loses a real lead. Defaulting to the inferred market keeps the common path frictionless; the sticky switch covers the traveler, the VPN, and the expat the automation guesses wrong.
Rejected Hard geo-detection with no override, or an upfront country picker that interrupts every visitor before they see a price.
Trade-off More surface to maintain — detection plus override plus making the choice persist — versus a single forced behavior. Worth it because both failure modes of the simpler options cost actual deals.
The piece I trusted least was the premise itself, so it's where I started: that a real legal contract could be generated from a row of structured data instead of assembled from a template someone maintains. The smallest honest unit was one country, one package, one VAT rule, producing one correct server-side document. Until I could regenerate that single contract deterministically and trust both the VAT and the mandatory clauses, nothing downstream deserved to exist. Once the generator held, I widened it across the 10 markets by modeling what actually differs between them as structured rules keyed by country: tax treatment, required clauses, formatting. The test never changed — the same package in a different market has to produce a correct, market-appropriate document with no per-market code.\n\nThen the part that is genuinely this project's hard problem: making two external, webhook-driven providers — one for e-signature, one for billing — collapse into a single \"deal done\" outcome. These are async systems I don't own. The e-sign provider calls me back when a document is signed; the billing provider calls me back when an invoice is raised; neither knows about the other, and neither guarantees it fires once. So I wired both into the Postgres state machine and made every join idempotent. A signature webhook that arrives twice advances the deal exactly as far as a signature webhook that arrives once. A billing call that times out in the gap right after a signature doesn't strand the deal — the state records that it's signed-and-awaiting-billing, and the reconciliation closes it when billing confirms, or surfaces it for a human if it never does. The whole point of the explicit state is that there is no arrangement of duplicate, late, or missing callbacks that produces a client who is signed-but-unbilled or billed-but-unsigned and invisible. The happy path was never the risk; the risk was the two providers disagreeing about whether the deal happened.\n\nGeo-detection and the i18n-driven pricing came after that spine was solid, with the sticky manual override built in from the start rather than bolted on, plus the admin CRM to watch deals move and push notifications so the team knows when one needs a person. The proof that the backbone is real is what shipped on top of it: live client builds across three verticals — cars.reykjawwwik.is, tours.reykjawwwik.is, and beauty.reykjawwwik.is — all running on the same multi-market pricing and contract engine.
I defined success on one operation: take a single deal from enquiry to a countersigned, correctly billed contract in a chosen market, and check the document is the right legal document for that country. Before, that operation was manual and serial — I assembled the contract, checked the VAT line by eye, sent a signature request, and improvised billing after, with the wrong-market contract being a real, observed failure I'd caught on a live deal. After, the same operation runs from structured data: country and package in, correct VAT and mandatory clauses out, signature and billing reconciled against explicit state.\n\nThe method was adversarial replay against the failure modes I actually feared, not a synthetic metric. The ones I cared about most were the disagreements between the two providers, so I fired those at the pipeline deliberately: a signature webhook arriving twice, a billing call timing out in the seconds after signing. The eval was binary each time — did the deal land in one consistent state, or did it split into signed-but-unbilled or billed-but-unsigned. Idempotent joins meant the duplicate signature advanced nothing extra; explicit state meant the billing timeout parked the deal as resumable instead of losing it. The honest proxies, since this is a private system and I won't invent numbers: the engine spans 10 markets from one rule set; three client builds across distinct verticals ship on the same backbone; and the lead-to-contract flow is the path the team runs to actually close deals, not a demo. The relative delta that matters is per-market cost — generating a correct contract for an additional country went from bespoke manual work to a data change, which is the whole point of the architecture and the thing I was optimizing for.
The build-lean bet is the honest limitation. Buying e-signature and billing shipped the platform fast, but it imported two vendors' data models and their webhook behavior, and a real share of the engineering became reconciling their callbacks against my own state rather than building product. For SME volume that trade is correct and I'd make it again. At high volume the cost structure and the dependence on a vendor's webhook reliability change, and I'd reassess taking e-signature in-house. The geo-detection is right often, not always, which is exactly why the manual override is sticky and not optional — I treated detection as a helpful default, never as ground truth, and that's a design admission, not a bug I fixed. And the deeper constraint baked into the model: it encodes the markets I researched. The data-driven approach makes adding a similar country cheap, but a market with a genuinely different contracting or tax regime would force me back into the rule model itself, not just a new row. That's the boundary of "adding a market is a data change" and I'd rather name it than oversell it.
The outcome is operational, not a launch announcement: a SaaS backbone that turns a services business into a repeatable multi-country pipeline. Correct local pricing across 10 markets, a tracked lead-to-contract flow, and a VAT-aware, e-signed, billed agreement at the end of it. What proves the backbone is real is the work that runs on it: 10 country markets served from one rule set, with three live client sites — car rental, tours, and beauty — shipped on top across distinct verticals, which is the validation it holds under real builds and not just a demo. There's a quieter result that I value more, because it's the failure that started all this. A deal can no longer end up signed-but-unbilled or billed-but-unsigned: the moment a contract is signed and a billing call stalls, the pipeline holds the deal in a known state and resumes it, where before a hiccup like that would have been a client with a signature and no invoice and nobody the wiser. The agency went from "I can do this deal" to "the system does this deal, in any of ten markets," and it closes the deal completely or it tells me exactly where it stopped.
Model the binding artifact as data, not as a document you maintain. The moment a contract becomes a function of structured inputs, correctness stops degrading with every market you add and a new country becomes a row instead of a rewrite. And when a deal depends on two outside systems agreeing, make your own state the place they reconcile — idempotent at every join — so no sequence of duplicate or dropped callbacks can leave a client half-closed. Correctness you own beats correctness you hope two vendors deliver in the right order.
Field teams run their whole pipeline and generate every government-funding contract straight from the CRM, daily, on their phones. The decision that made it work: treat the contract as a deterministic artifact the server builds from structured order data, never a document a person fills in by hand.
Close an energy-audit job, then sit down and assemble its government-funding contract by hand: that was the workflow. The pipeline lived in spreadsheets, the contract lived in a template someone copied and edited per job, and the two stayed in sync only as far as whoever was typing that afternoon kept them so. A salesperson, an auditor, and an admin all touched the same job, each needing a different slice of it, and the spreadsheet showed all of them everything. Here is the part that kept me up. A blank or wrong field on a funding contract does not read as a draft. It reads as a real, signed answer on a document that unlocks public money. One mistyped value, the application gets bounced, and the money behind it stalls.
The system is a CRM for field-sales teams running government-funded energy-audit programs: a 9-stage order pipeline with three roles, automated contract generation, a map view, push notifications, and a performance leaderboard. What raises the stakes is that the output documents are government funding contracts, so \"mostly correct\" is a failed application rather than a typo. Three constraints shaped the build. Three roles (salesperson, auditor, admin) need the same underlying dataset but each may see only its slice. The people using it are on phones with flaky connectivity out in the field. And the contract output has to come out identical in shape every time, because a funding reviewer reads it as a legal document, not a form draft.
My role. Sole engineer. All of it was mine: the data model, the Postgres row-level security policies, the server-side document-generation service, and the React frontend the field teams actually use. There was no one to hand the \"why can't this user see this row\" question to, and no separate person owning the document layer, so the correctness of the funding contracts sat with me end to end. The client is not named here.
Model the order as one record moving through 9 explicit, named pipeline stages, and let that stage be the one fact that documents, notifications, and visibility all read from.
Why Multiple roles touch the same order over unreliable connections. If each feature carried its own idea of where an order was, those ideas would drift, and on a flaky phone drift means two people acting on stale state. So: one stage field, one place the state changes.
Rejected A looser status model with independent boolean flags (is_audited, is_approved, contract_sent) that each feature sets on its own.
Trade-off Nine fixed stages are rigid. A genuinely new step in the sales process means a real schema and logic change, not flipping a flag. I took that rigidity on purpose: it is exactly what keeps the derived features honest, because they have nothing to read but the stage.
Put access control in Postgres row-level security rather than the React frontend, and write every policy to fail closed.
Why With three personas sharing one dataset, the live question is which rows a given user may see, and the only safe place to answer it is the data layer. Authorization in the client means one bug or one direct query leaks the whole table. Failing closed means a missing policy hides data instead of exposing it.
Rejected Filtering by role in frontend queries, or in an API layer that trusts whatever role the caller claims to be.
Trade-off Every new feature that reads order data now has to be reasoned about against the policies, and debugging visibility means reading SQL predicates instead of stepping through JS. I want to be plain about something here: unlike a sibling project where composed RLS policies bit me with an infinite-recursion bug, this RLS work was the careful, uneventful kind. No dramatic failure, just a steady tax I pay writing and re-reading predicates on each feature.
Generate funding contracts server-side: map structured order fields into a templated DOCX with explicit field bindings, render to PDF, and treat any unmapped or null required field as a hard failure that refuses to emit the contract.
Why The contract is the deliverable that unlocks the money, and a blank on a funding application reads as a signed answer, not a gap someone will obviously catch. Explicit bindings plus hard-failing on nulls means the system stops and refuses rather than quietly shipping a half-empty document that looks complete.
Rejected A visual / WYSIWYG template editor so non-engineers could reword contracts without a deploy.
Trade-off Code-managed templates are slower to reword: changing a clause is a commit, not a click. I accepted that for determinism and reviewability. The template lives in version control, so every wording change is diffable and the layout cannot drift between two generated contracts.
Drive push notifications and the leaderboard off pipeline-stage events instead of wiring them into each individual action.
Why If every site that mutated an order also fired its own notification and recomputed standings, the side effects would tangle, and a retry from a phone that just lost signal would double-fire them. Deriving everything from stage transitions gives one trigger point and one thing to trust.
Rejected Imperative side effects scattered at each mutation site (send the push here, bump the leaderboard there).
Trade-off Everything routes through the stage model, so that model carries more weight and has to be the piece I trust most. Concentrating the risk there was the point, but it does mean a bug in a stage transition is a bug in three features at once.
I built the riskiest piece first, on purpose, so it could be wrong while it was still cheap to fix. That was the order record and its 9 stages, because everything else keys off stage and I wanted to find out early if that model was wrong, not late. With stage transitions established as the source of truth, RLS went on top. I wrote the policies persona by persona and composed them with the stage model: a salesperson sees their own orders, an auditor sees the stages relevant to the audit, an admin sees everything, and a missing policy defaults to no access rather than full access. Composing three personas across nine stages was the fiddly, slow part, because every policy has to agree about which stage a row is in and which persona is asking, and getting that agreement right took patience rather than heroics. Then the document service, which I treated as the highest-correctness unit in the whole system: structured fields into a templated DOCX with explicit bindings, rendered to PDF, hard-failing on any missing required field rather than emitting a contract with a hole in it. Only once the spine, the policies, and the documents were solid did I build the parts people see and feel: the mobile-first React frontend (React, TypeScript, TanStack Router, Supabase) the field teams use, the map view, push notifications, and the leaderboard, every one of them reading from the stage model instead of inventing its own state. The artifacts that matter are concrete: a versioned DOCX template with named field bindings, a set of fail-closed RLS policies, and a single stage enum the whole app routes through.
Success was defined narrowly: a generated funding contract has to be correct every time, because the failure mode is a rejected application, not a cosmetic glitch. So contract generation was the operation I held the line on. Before, the contract was hand-assembled per job, which means the error rate was whatever the human had left in them that day, and there was no single moment where correctness got checked. After, the check moved into the generator itself: explicit field bindings plus a hard-fail on any unmapped or null required field, so the system cannot emit a contract with a silent blank. The before/after on that one operation is the shift from \"a person validates each contract by re-reading it\" to \"the generator refuses to produce an invalid one.\" I won't claim a percentage I didn't measure. The verifiable proxies are that the manual assembly step was removed entirely, that the teams run contract generation themselves on their phones as part of daily work, and that the RLS policies were exercised across all three personas rather than one. The leaderboard and notifications I treated as derived correctness: if either ever disagreed with the order's actual stage, that was the signal the stage model had a bug, so they doubled as a cheap consistency check on the spine.
RLS was the right call for correctness, and it carries a named cost I'd be dishonest to hide: every new feature that reads order data has to be reasoned about against the policies, and when someone asks \"why can't this user see this row,\" the answer lives in SQL predicates, not in readable application code. For a small team and three personas, that tax is acceptable. Past three personas, or if the visibility rules turned more conditional, I'd pull authorization out of inline RLS and into an explicit, testable policy layer I could unit-test in isolation, because debugging composed predicates by hand does not scale. The code-managed templates are the other honest trade: when the client wants a clause reworded, it's a deploy, not a self-serve edit, and I chose that knowingly. If contract wording started changing often, that decision would need revisiting, probably toward a reviewed template-data layer rather than a free WYSIWYG editor, so I keep determinism without turning every reword into an engineering ticket.
It shipped as a working field CRM the teams use daily on their phones. The manual contract-assembly step is gone: instead of closing a job and then hand-building the funding contract, the contract comes out of the order data, the same shape every time, with the system refusing to emit one that has a blank where a signed answer belongs. The pipeline went from scattered spreadsheets where everyone saw everything to a single record where each role sees only its slice. An entire hand-assembly step that a person used to perform on every single job simply does not exist anymore, and the people in the field now generate compliant funding contracts themselves, without a desk in the loop. That is the result: not a benchmark number, but a whole human step that the system absorbed and a source of truth that stopped being everyone's spreadsheet and became one pipeline of record.
When the document is the deliverable that unlocks the money, make the system refuse to produce a wrong one rather than trusting a human to catch it. Push correctness down to the layer that can fail closed, and pay the tax that buys without pretending it's free.
Flyt is a freight and group-import marketplace for Iceland: a customer posts a delivery and verified carriers bid, and buyers pool into shared containers to cut import costs. I built a deposit-and-conditional-refund mechanic for pooling import buyers into shared containers, backed by an explicit Postgres state machine. If a campaign doesn't fill, everyone is refunded automatically, and nobody is ever double-charged. The decision that drove it: treat money events as state transitions, not side effects.
Importing a single item to Iceland quietly punishes you. You find the thing you want from an EU retailer, and then the shipping on one small parcel costs nearly as much as the item, customs and VAT land on top, and the price you actually pay bears no resemblance to the sticker. Everyone here knows the move. You wait until a few people want things from the same region, throw it all in one container, and split the freight. But the moment you try to organize that pooling for strangers, the problem stops being logistics and becomes money. Whose deposit are you holding? What happens to it if not enough people join and the container never ships? If someone backs out on day six, who eats the gap? I kept seeing the same failure in my head, the one that kills this kind of thing: a buyer pays to reserve a slot, the campaign quietly fizzles, and three weeks later they're emailing to ask where their money is. On a price-sensitive purchase, one stranded refund is the whole reputation gone.
Flyt (flyt.is) is my own venture, a freight and group-import marketplace for Iceland. This case study is about the pooled-container side: many buyers share one container with deposit and refund logic, plus on-demand cross-border VAT import quoting and an admin dashboard for live campaign tracking. The stakes are not cosmetic. This is real money held on behalf of real buyers in a market where freight is expensive and people are already nervous about import costs. A few constraints shaped everything. Correctness on the money flow is non-negotiable, because a double-charge or a stranded deposit is not a bug you apologize for, it is trust you do not get back. The landed cost has to be believable enough that a careful buyer commits. The data is messy in the real-world sense: campaigns partially fill, people cancel, and timing is awkward. And because it is a marketplace, it only works if campaigns actually reach the threshold to ship. The marketplace has two sides: a carrier-bidding side (post a delivery, verified carriers send prices within hours, the customer picks) and a group-import side (pooled container campaigns with deposit-and-refund). This study goes deep on the harder of the two, the money mechanics of pooling.
My role. Solo. This is my own venture and I built it end to end: the pooling mechanic, the deposit and refund state machine, the landed-cost quoting rules, the admin dashboard, and the data model underneath all of it. No hired devs, no contractors on the build. The decisions below are mine, including the ones I argue with myself about.
Run the freight side as a carrier-bidding marketplace: a customer posts a delivery, verified carriers bid, the customer picks.
Why Freight pricing in Iceland is opaque and quote-by-quote. Letting carriers compete on a posted delivery turns an ask-around process into a fast, comparable set of real prices, which is the reason a customer would use a marketplace instead of calling one carrier.
Rejected Publishing my own fixed freight rate card. Simpler for a buyer, but I'd be guessing every lane's true cost and either overcharging or eating the gap, owning pricing risk that isn't mine to own.
Trade-off A bidding marketplace needs enough verified carriers to produce competitive bids, so it carries a cold-start cost on the supply side. I accepted that because real competing bids are the product; a made-up rate card is not.
Use deposits with conditional refunds as the pooling mechanic: collect a deposit to reserve a slot, and refund automatically if the campaign doesn't fill.
Why Pooling only works if commitment is real. A reserved slot has to mean something, or the campaign math is fiction. The deposit is the commitment signal, and the automatic refund is the safety net that makes committing rational for a nervous buyer.
Rejected I rejected two alternatives. Charging only on confirmation (when the container is confirmed to ship) means nobody is actually committed, so campaigns never reach the threshold and you can't tell a real slot from a maybe. Full upfront payment flips the risk entirely onto the buyer for a container that might never leave, which on a price-sensitive import is exactly the friction that kills sign-ups.
Trade-off The deposit model only earns trust if the refund is flawless, so I bought myself a refund state machine that must never double-charge and must never strand a deposit. I traded a simple payment flow for a hard correctness problem, deliberately.
Compute landed cost (item + shipping + customs + VAT) from structured per-category rules, with VAT rate and customs treatment encoded per category, rather than a flat percentage markup.
Why The quote is the thing a buyer commits money against. On a price-sensitive purchase, a flat markup is wrong often enough that the gap between the quote and the real landed cost erodes trust precisely when you most need it. Per-category rules keep the number close enough to be believable.
Rejected A flat percentage on top of item price. It is trivial to build and it is wrong in both directions across categories: sometimes overquoting and scaring the buyer off, sometimes underquoting and leaving someone angry at delivery. Either failure costs trust.
Trade-off Per-category rules mean ongoing maintenance. Every category I encode is a rule I now own and have to keep correct as treatment changes. I accepted a maintenance burden in exchange for quotes a careful buyer will actually trust.
Make campaign, slot, and deposit state in Postgres the single source of truth, with money events driven by explicit state transitions.
Why On a money flow, the answer to 'what should happen to this deposit right now' has to be derivable from one authoritative place, not inferred from scattered flags or the timing of a webhook. If state is the source of truth, then 'refund on no-fill' is a transition I can define, test, and reason about, instead of an event I hope fires once.
Rejected Letting payment-provider events or ad-hoc booleans drive the money logic directly. That is the path where a retried webhook double-charges, or two flags disagree and a deposit ends up in limbo, owned by no state at all.
Trade-off More upfront modeling and stricter discipline about what counts as a valid transition. It is slower to add a feature when every money-touching change has to go through the state model, but that is the cost of being able to sleep while holding other people's deposits.
I built the money side smallest-unit-first, because the money side is the part you cannot ship hopeful. The first real artifact wasn't the marketplace UI. It was the campaign/slot/deposit model in Supabase Postgres: the explicit states a slot can be in, and the legal transitions between them. I treated refund and fill behavior as transitions on that model rather than as things that happen when a payment event arrives, so the question of what a deposit is owed at any moment is always answerable from the database instead of reconstructed from a log. From there I worked outward to the deposit-to-reserve flow, then the two paths that actually matter: the partial-fill path, where a campaign closes without reaching its threshold and every deposit refunds, and the cancellation path, where a buyer backs out before fill. The landed-cost quoter went in as its own piece, with structured per-category rules for VAT and customs treatment instead of a flat percentage, so the number a buyer commits against is computed, not guessed. The admin dashboard was the safety net I built for myself: live campaign tracking plus bulk notifications, so a campaign in a wrong state is visible to me immediately rather than discovered through a complaint. The stack stayed deliberately boring. React and TypeScript on the front, Supabase (Postgres) as the source of truth, Vercel for deploy, so my attention went to the state machine and not to infrastructure.
Success here had exactly one bar: the money logic is correct on the paths where it is tempting to be wrong. I didn't define success as 'the happy path works', because the happy path is never the problem. I defined it as the partial-fill path and the cancellation path behaving correctly, with no double-charge, no stranded deposit, and every refund owed actually issued. I checked the logic against those paths specifically: a campaign that fills, a campaign that doesn't, a buyer who cancels mid-campaign, and the awkward timing in between. The method was to drive those transitions through the state model and confirm the resulting money state matched what the model said it should be, with the admin dashboard as a live second check. Because it surfaces campaign and deposit state directly, a wrong state shows up as something visibly wrong on the dashboard the moment it happens, instead of weeks later in an email. I'm not going to quote a transaction count or a revenue number, because this is a live business holding real deposits. But the honest proxy is the one that matters: the bar was zero money-correctness failures on the fill and cancellation paths, and that is the bar I built and tested against rather than a throughput figure.
Three honest ones. First, the landed-cost quoter is built for the common import categories and it is good there, but it does not cover every exotic edge case. Certain unusual customs treatments will fall outside the encoded rules, and I would rather quote well for the cases people actually buy than quote vaguely for all of them. That is a deliberate choice, not an oversight, but it is a real limit. Second, it is built for SME-scale volume. The design assumptions are about that scale, and I have not stress-proven the money state machine under genuinely high concurrency on a single campaign. Third, the one I am least able to engineer my way out of: it is a marketplace, so the cold-start problem is baked in. The whole mechanic depends on enough buyers showing up to fill a campaign, and no amount of correctness in the refund logic creates demand. If I were doing it again I would think harder about seeding the first campaigns rather than assuming a clean refund experience alone would pull liquidity.
What I actually have is a pooling mechanic where the failure mode that kills these schemes can't happen quietly. If a campaign doesn't fill, the deposits refund automatically as a consequence of the state model, not because someone remembered to process them. The operational value is concrete. Working out who is owed what when a campaign collapses, going down a list and issuing refunds by hand while hoping you don't pay someone twice or miss someone entirely, is exactly the kind of manual reconciliation that goes wrong. Here it is a defined transition the system performs. A multi-row manual refund pass becomes an automatic consequence of the campaign's state. And because the admin dashboard surfaces live campaign and deposit state, the human anecdote is the absence of one. Instead of finding out a campaign went wrong from a buyer asking where their money is, I see the wrong state on the dashboard first. For a one-person venture holding strangers' deposits, that ordering, where the system notices before the customer does, is the whole game.
On a money flow, model the states first and let the money follow them, never the other way around. If 'what is this person owed right now' can only be reconstructed from logs and timing, you don't have a refund feature, you have a future apology. The cold-start problem is the honest counterweight: no state machine solves liquidity for you, so don't let beautiful correctness convince you you've solved demand.
Shorter write-ups — same honesty, less depth.
Reykjawwwik now ships live client sites across several unrelated verticals on one shared engine, so a new brief reaches live fast because the machine does the work, not a heroic all-nighter. The decision that bought that: productize the stack into shared starters and a common component system, and accept less per-site flexibility to get it.
I built an end-to-end lead engine to fill my own ventures' pipelines. The decision that shaped everything: treat deliverability as the hard constraint, so every contact gets verified and scored before it can enter the queue, and the domain ships on a warmed, throttled cadence instead of one big blast. The payoff is not a vanity send count. It is a clean, scored queue and a sending domain that stayed healthy past send one.
I stopped prompting the image model cold and put brand-and-audience research in front of generation, so the drafts came out usable instead of obviously AI. The decision that mattered wasn't a better model. It was matching the photography style to what a specific audience reads as credible, with a human still picking the final frame.
I co-built a glacier-tour operation from nothing into a a well-reviewed operator with 1,000+ five-star guests during my time, then handed it off so it kept running after I left. The decision that drove it: rent the booking and distribution stack instead of building it, so we could sell before the season's runway ran out.
Want the depth behind any of these? hello@kamiljan.com
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