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LLM Engineering

LLM application development

From prototype to production: API, agents, eval and integrations — not a Jupyter demo.

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LLM application development is engineering: prompt versions, eval on real data, structured output, fallbacks and observability. Bober AI Systems builds production services, not one-off scripts.

Stack: Python/TypeScript, LangChain/LangGraph, GigaChat or private LLM API, PostgreSQL for state, Redis for cache. Agents call CRM, 1C, search via MCP or REST.

Every LLM step covered by eval: input → expected JSON → regression in CI. Without it every model update is a lottery.

Examples: catalog-grounded proposal generator, ticket classifier, CRM copilot, multi-step document approval agent.

MVP timeline 3–5 weeks. NDA, on-prem inference standard. Budget from €4,000.

Discovery separates «need LLM» from «regex is enough» — saving client budget.

Why LLM projects stall

  • —Prompts work in demo but break on real data
  • —No structured output — unreliable parsing
  • —Latency and cost not modeled — system cannot scale
  • —No eval or prompt versioning

What we build

  • —LLM service with API, auth and monitoring
  • —Prompt/version management and eval pipeline
  • —LangGraph agents with tool calling
  • —CRM, document and messenger integrations

Development process

01

Spec & eval set

Scenarios, JSON schema, golden examples, quality metrics.

02

Prototype

Fast POC on sample data, latency and cost check.

03

Production code

API, retries, caching, logging, feature flags.

04

Handover

Documentation, runbook, client dev team training.

LLM app stack

  • —API layer: FastAPI / Next.js routes, OpenAI-compatible client
  • —Orchestration: LangGraph state machine, tool registry
  • —Storage: conversation state, audit log, prompt versions
  • —Eval: pytest + LLM judge / exact match on structured fields
  • —Deploy: Docker, K8s, CI/CD with eval gate

Results

3–5 wks

MVP in production

−60%

manual processing time in target scenario

CI eval

quality regression on every release

Related case studies

Автоматизация коммерческих предложений

Автоматизация коммерческих предложений

45 мин → 2 мин на КП

MySQL · CRM · Python · LLM

Один диалог вместо ручного прайса и Word. Цены и артикулы только из каталога — без выдуманных позиций. Таблица, НДС, условия и скачивание DOCX/PDF.

300 000 ₽

View case study →
Email AI Assistant — Kaspersky

Email AI Assistant — Kaspersky

−50% повторных обращений L1

LLM · RAG · JavaScript

Консультанты быстрее закрывают типовые обращения, единообразно отвечают на вопросы о продуктах и тратят больше времени на сложные инциденты и апсейл.

View case study →

FAQ

LangChain or custom?
LangGraph for agents. Thin logic in plain Python without over-abstraction.
Which model?
GigaChat for RU enterprise. Open-source for on-prem. Choice on Discovery.
Structured output?
JSON mode, Pydantic validation, repair loop on parse errors.
Cost control?
Caching, smaller models for simple steps, batch where possible.
Code handover?
Yes. Client repo, docs, pair programming on handover.
Budget?
From €4,000 MVP. Agents with integrations from €5,000.

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From prototype to production: API, agents, eval and integrations — not a Jupyter demo.