// НАШИ КЛИЕНТЫ
We handle the part where most teams fail: production integrations, data quality, RAG retrieval accuracy, golden-set testing, and SLA commitments. The business metric is written into the contract. From $5,990, 3-4 weeks.
The pilot ran on exported CSV files and hand-crafted prompts. Production means live CRM data, messy formats, rate limits, API timeouts and a queue. The pilot never saw any of this.
The pilot was built to impress a demo, not to hit a business KPI. When accuracy on real data is 55%, there is no agreed target to push against. The project stalls.
The pilot used GPT-4o on every call with a 10k-token context. Production traffic at that cost exceeds the budget in week one. No one calculated it before starting.
Before writing a line of code, we lock a target business metric in the contract: support load down 40%, lead response time under 2 minutes. If prod does not hit it, we keep working at our cost.
50-200 real examples with expected outputs. Every model version runs against the set before deploying. Accuracy below 80%: no deploy. This is how you know the model works on your data, not on benchmarks.
Live CRM, document base, email, messengers. Real queues, rate limits, fallbacks, retries. Not a webhook that works in staging and breaks on the first 100 prod calls.
We count the LLM token cost per call and at projected volume before start. If the budget does not work, we redesign the architecture: smaller context, cheaper model for routing, caching frequent answers.
For critical decisions we build a review queue. The AI drafts, a human approves. Over time, as the model proves itself, the human review rate drops from 100% to 10%. Gradual trust-building.
Every prompt, every response, every tool call logged in Langfuse or Helicone. You see what the AI does in production. When something goes wrong, the debug takes minutes, not days.
Stripe invoice in USD or EUR. 50% upfront, 50% on go-live. If we miss the metric, we keep building at our cost.
Three reasons: weak integration with real systems, no one owns the business metric, and token cost or accuracy on real data breaks the business case. A pilot is built to demo, not to survive production traffic.
50-200 real examples with correct expected outputs. We run every model version against it before deploying. If accuracy drops, we do not deploy. This is how production AI differs from a demo.
Three things in the contract: (1) The target business metric is specified. If not met on prod, we keep working at our cost. (2) AI quality measured on golden set at 80%+ accuracy. (3) LLM token cost calculated and capped before start.
LLM: Claude (primary), OpenAI GPT-4o, local models via Ollama for data privacy. Stack: Python FastAPI, LangChain or LlamaIndex for agents, Qdrant or pgvector for RAG, Langfuse for logging, n8n for orchestration.
Three things: access to data (CRM, document base, support history), one decision-maker who can approve scenario changes and review AI quality, agreement on 2-3 rounds of prompt engineering after launch.
Yes. For sensitive data: local models via Ollama on your servers, anonymisation layer before sending to the API, full data-processing agreement. For enterprise: on-premise option with your cloud (AWS, GCP, Azure).
A 30-minute audit call. No charge, no obligations. We look at your architecture, data and target metric, and give you a clear next step.