ROVQIX integrates large language models into existing web products: retrieval over your own data, streaming interfaces, prompt and output evaluation, cost controls, caching and guardrails — built as a product feature with measurable quality rather than a demo.
The demo takes an afternoon. The work is everything after: what happens when the model is wrong, how you know quality changed, and what it costs at a thousand users.
Quality measured, not vibed
Responses feel instant
Per-user cost caps
Answers link their sources
Chunking, embeddings and hybrid search so answers come from your documents rather than the model's imagination.
Token streaming with proper loading, cancellation and error states, so a slow model still feels responsive.
A test set with graded outputs, run in CI, so a prompt or model change cannot silently degrade quality.
Caching, model routing, token budgets and per-user limits — with a dashboard showing spend before the invoice does.
Input validation, output constraints, refusal handling and human escalation paths for anything consequential.
Citations, confidence signals and clear labelling, so users know what is generated and can check it.
Retrieval-augmented generation retrieves relevant passages from your own content and gives them to the model as context, so answers are grounded in your data.
| Lever | Effect |
|---|---|
| Cache identical and near-identical requests | Large, immediate |
| Route simple requests to a smaller model | Substantial |
| Trim retrieved context to what is needed | Direct token saving |
| Per-user and per-org rate limits | Caps worst-case spend |
| Prompt caching for stable system prompts | Meaningful at volume |
| Stream and allow cancellation | Stops paying for unread output |
We instrument cost per request and per user from day one. Teams that add this later usually discover a small number of users generating most of the spend.
We will tell you when the honest answer is that a form, a filter or a query serves your users better. That conversation happens during scoping, not after the build.
Anthropic's Claude, OpenAI and open models depending on the task, cost profile and any data residency constraints. We build behind an abstraction so switching providers does not mean rewriting the feature.
Not on standard business API tiers from the major providers, which do not train on API inputs by default. We configure retention settings explicitly and document exactly what leaves your infrastructure.
A fixed evaluation set of representative questions with graded expected outputs, scored automatically and reviewed by a human on a sample. Without that, prompt changes are guesswork.
Yes, where compliance requires it. We will be honest that self-hosted open models typically trade some quality for control, and help you judge whether that trade is worth it for your use case.
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 – $5,000
1–2 weeks
Best for: Testing whether the idea works at all
$8,000 – $30,000
5–12 weeks
Best for: A production AI feature in your product
from $2,500 / month
Ongoing
Best for: Features needing continuous tuning
How we think about this work, in more depth.
AI integration
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.