ROVQIX provides AI engineers who build production LLM features: retrieval-augmented generation over your data, evaluation harnesses that catch quality regressions, streaming interfaces, cost controls and safety guardrails — integrated into your existing web product.
The gap between a working demo and a production AI feature is mostly unglamorous: evaluation, caching, cost limits, error handling and deciding what happens when the model is confidently wrong.
Quality measured
Not locked in
Per user and org
Shipped, not prototyped
Chunking, embeddings, hybrid search and reranking — where the quality of a RAG system is actually determined.
A graded question set run in CI, so prompt and model changes are measured rather than eyeballed.
Token streaming with cancellation, retry and clear error states, so latency feels acceptable to users.
Caching, model routing, prompt caching and hard per-user limits, instrumented from the first day.
Input validation, output constraints, refusal handling and escalation to a human where stakes are real.
Built so switching between Claude, GPT and open models is a configuration change, not a rewrite.
With a fixed set of representative inputs, expected characteristics for each, and automated scoring plus human review on a sample.
We build application-layer AI features into web products. That boundary is deliberate, and stating it upfront saves everyone a wasted call.
No. They build application-layer features on top of hosted and open models — retrieval, evaluation, integration and interface. Model training is a different discipline and we will refer you to specialists.
Anthropic's Claude, OpenAI, and open models where self-hosting is required. Work is built behind an abstraction so provider choice stays a configuration decision.
Yes. We configure retention settings explicitly, document what leaves your infrastructure, and can architect for self-hosted models where compliance demands it.
The evaluation harness gives you a number that moves when quality moves. That is the deliverable we insist on, because without it nobody can tell whether last week's prompt change helped.
Indicative ranges in USD. Every engagement is quoted to a written scope before work starts, so the number you approve is the number you pay.
$2,500 / month
20 hours per week
Best for: Iterating on an existing AI feature
$4,500 – $7,000 / month
40 hours per week
Best for: Building AI features into your product
$2,500 – $5,000
1–2 weeks
Best for: Finding out if it will work
How we think about this work, in more depth.
Hire developers
A 30-minute call, then a written proposal with scope, price and timeline within two to three working days. No retainer required to get a real number, and no obligation if the answer is that we are not the right fit.