AI in production
A demo on three examples works for everyone — fewer than one pilot in eight reaches production. The model is rarely the problem: what is missing is measurable answer quality, observability and safety rails. That is what we build, with the same operational experience we bring to high-load services.
What's included
- Evaluation suites for answer quality, running in CI on every prompt change
- Tracing of requests, tool calls and retrieval — you see where the agent went wrong
- Guardrails: input and output filtering, human approval on risky actions
- Token cost control, caching and model choice per task
Technologies
Platforms
How we work
- 01
Brief and estimate
We look at the task, your current system and constraints, then name the timeline and budget and suggest how to work together.
- 02
Specification
We capture requirements and acceptance criteria in a specification — it is what we build against and how the result is accepted.
- 03
Iterative development
You see the result after every iteration; we test and fix before anything goes into a release.
- 04
Launch and support
We ship with no downtime, monitor the product after launch and stay on for support if you need it.
Cost and timeline
Hourly Rates: from 1 800 ₽ to 3 500 ₽ / hour — depending on specialist qualification.
We give an exact timeline and budget after a brief: we break down the task, fix the scope and suggest a format — a fixed plan or hourly work.
See plansFAQ
We already have a working AI pilot. Can you take it further?
Yes, and a rewrite from scratch is usually not needed. We collect real requests, build an evaluation suite and add tracing to see exactly where answers break. Then we fix issues by priority and wire the evaluations into CI.
How do you measure the quality of model answers?
We build evaluation sets from real cases with expected results and run them automatically on every change to the prompt, model or retrieval. That way a regression shows up before release, not after user complaints. Ambiguous cases still get human review.
Can we cut token costs without losing quality?
Usually, yes. We break down which requests consume tokens, add caching, trim context and route simple tasks to a cheaper model. Every change is checked against the evaluation suite, and spending gets limits and alerts.
AI in production
We take AI pilots into live operation: answer quality evaluation, observability, guardrails and cost control.