AI that
survives
production.
Retep Technologies is a senior-led engineering studio. We build AI features, retrieval systems and agents into products that already have users — with evals, guardrails and cost budgets, not demos.
Illustrative trace of a grounded answer pipeline — the shape of what we ship.
A model call is a weekend project. Making it correct, fast, cheap and safe enough to put in front of customers is the engineering.
Grounded. Answers tied to your data and sources, so the output can be checked rather than trusted.
Measured. Eval suites and traces on every release, so quality is a number and not a hunch.
Contained. Spend limits, rate limits and human checkpoints on anything that writes or spends.
What we build
AI engineering and development
Most AI work dies between the demo and the deploy. We take a use case with a measurable payoff, prove it against your real data, and run it in production with evals, guardrails and a cost ceiling.
AI product features
Assistants, copilots, semantic search, document extraction and classification — built into the product you already run, not bolted on beside it.
Retrieval and RAG systems
Chunking, embeddings, hybrid search and reranking, so answers stay grounded in your data and cite where they came from.
Agents and tool use
Models that call your APIs safely: typed tools, retries, spend limits, and human checkpoints on anything that writes.
Evals, guardrails, observability
A test suite for model behaviour, traces on every request, and cost and latency budgets you can watch in production.
Backend and API engineering
The services, queues and data models your AI features depend on — built to hold up under real traffic.
System architecture
Distributed systems and cloud design, sized to the load you actually have and the one you are heading for.
Platform scaling
Finding what is slow, expensive or fragile in a growing system, then fixing it in priority order.
Technical advisory
Build-or-buy calls, model selection, architecture review, and hiring plans for founders without a CTO yet.
How an engagement runs
Scope
We map the workflow, pick the use case with a payoff you can measure, and agree in writing what a good answer looks like. If AI is the wrong tool here, you hear it in week one.
Prototype and eval
A working slice against your real data, plus an eval suite that scores it. You see pass rates, failure cases, latency and cost per request before anyone commits to a launch.
Production
Ship it behind guardrails, tracing and spend limits, wired into your auth, your data and your deploy pipeline. Handover includes the evals and the runbook.
Iterate
Traces show where it breaks in the real world. We tighten retrieval and prompts, raise pass rates, and cut cost per request as usage grows.
What we work in
Built by the engineer you talk to.
Retep Technologies was founded by Peter Sowah after years building and running production software as an engineer.
The studio stays small on purpose: senior people, few clients at a time, and honest calls about what is worth building.
One senior engineer leads your work
The person scoping the project is the person writing the code. Nothing is handed down to a bench of juniors after the pitch.
AI on top of real systems engineering
A model call is the easy part. The value sits in the data, the retrieval, the queues and the failure handling around it.
Evals before opinions
We argue about prompts with numbers. Every change gets scored against a suite, so improvements are demonstrable, not felt.
Cost and latency are features
Budgets are set at design time and tracked per request. You never find out what inference costs from the invoice.
Your team owns it afterwards
Documented, tested code in your repositories, with the eval suite and runbook that keep it maintainable without us.
Tell us what you want the AI to do.
Send the workflow you have in mind and the data behind it. You get a straight answer on whether it is worth building, what it would take, and what it would cost to run.