Why it matters
AI for the checkbox burns tokens and leaks hallucinations into customer channels. Integration is worth paying for when a human minute costs more than a model call — and you need a predictable result in production, not a pretty slide.
Services / AI tool integrations
Moscow · Worldwide
AI in the product: save hours, not tokens
We embed LLMs where team minutes beat API cost: assistants, classification, RAG, generation — with quality, limits and spend control.
What we do
In this engagement:
- Scenarios and autonomy bounds — where AI acts alone, drafts only, or escalates to a human
- Prompts, RAG, knowledge base — answers from your data, not generic model filler
- Guards: toxicity, leaks, hallucinations — PII filters, blocked topics, pre-send checks
- Token accounting and limits — cost per request visible; caps by role and scenario
- Logs and quality eval — what was answered, from which data, what to fix in prompts and KB
How we work
One painful scenario → pilot: data, prompts, guards, metrics for speed, accuracy and cost. Run the numbers 1–2 weeks; scale only what pays back. No “GPT on everything”.
FAQ
How long does a pilot take?
Typically from two weeks for one scenario: data access, prompts, guards, baseline metrics. We lock the timeline after scoping the task and your stack.
What drives the cost?
Number of scenarios, knowledge sources, security requirements, and whether we embed into an existing product or ship a separate service. We quote after a short discovery — no numbers from thin air.