“We shipped an AI-built prototype. Now it has to hold up.”
It works and customers are in it — but the permissions, data model and edge cases were never actually designed.
Two Senior Product Engineers
We build new products and turn AI-built prototypes into production systems — owning product, UX, architecture and engineering end to end.
It works and customers are in it — but the permissions, data model and edge cases were never actually designed.
Funding is there, the roadmap is there, hiring four people first is not the plan.
The AI feature, the data model, the migration — the work the team keeps deferring.
Private repos, so: the systems, not the screenshots.
01 · Automotive platform
A Lovable/Supabase app was already holding customer data on a model that couldn’t support it. We rebuilt the schema and permissions, then automated the operations on top.
React · TypeScript · Supabase · AI
02 · AI workflow platform
Operators needed to build and run reusable AI workflows without engineering in the loop. We designed the product model and built the engine underneath it.
Next.js · Node · Postgres · Queues
03 · Document intelligence
Extraction was accurate most of the time, which no audit accepts. We rebuilt retrieval around verifiable citations and hardened it for enterprise tenancy.
RAG · Postgres · AWS
Everything above is under NDA or in a private repo. On a call we’ll go through the architecture and the trade-offs in as much depth as you want — and put you in touch with the people we built it for.
You talk to the people making the decisions.
No junior work handed down the chain.
Two people delivering what used to need a team.
Both of us hold the whole product, not tickets.
idea → production
From vague requirements to architecture, UX, implementation and shipping. You bring the direction; we turn it into working software.
prototype → production
Permissions, tenancy, data modelling, testing, observability, infrastructure — the edge cases demos don’t encounter.
hard problem → shipped
Agents, automation, data pipelines, integrations, migrations — the work your team keeps deferring.
We use coding agents heavily throughout development. Architecture, product decisions, security, review and production reliability stay with us.
Technology is selected around the product rather than the other way around.
Senior Product Engineer
Full-stack engineer with a product, UX and frontend-architecture bias. Turns a vague business problem into screens and system boundaries that still hold six months later.
product · ux · frontend architecture
Senior Product Engineer
Full-stack engineer with a backend, data and infrastructure bias. Finds the authorization hole and the query that falls over at ten times the data.
backend · systems · integrations · infra
Own a new product or a substantial area of one.
We take ownership of a difficult product or technical area inside your team.
Audit and assume ownership of an inherited codebase.
Short engagement on the biggest technical risks — starts with a two-week audit you keep either way.
Product, users, existing system.
Scope, UX, architecture, approach.
Short iterations, frequent releases.
Deploy, monitor, keep improving.
We can’t show most of what we’ve built, so the start of an engagement is small, concrete and easy to walk away from.
Day 1–2
A call on your codebase or your problem, in whatever depth you want. Architecture questions, not sales questions.
Week 1
Risks, priorities and a plan you own — useful even if you hand it to someone else.
Week 2
One real fix or feature in production, so you can judge the code rather than the pitch.
Tell us what you’re building, where it is today, and what’s in the way. If there’s a codebase involved, we’ll sign your NDA before we look at it.
Start here
A few questions about what you’re building and where it stands. Takes about two minutes.
or email hello@stajics.com