AI agentsscenario
RAG system and knowledge base for business
Documents → semantic search → answers with sources · no “hallucinations for show”
AI that relies on your data
RAG (Retrieval-Augmented Generation) is a contour where the model does not invent a price list from the internet — it first finds fragments in your base and then formulates the answer. It fits support, internal assistants, and sales with a thick document catalog.
The pain we close
- 01
Staff hunt for answers in chats and outdated PDFs
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Clients get different answers — no single source of truth
- 03
A “bare” GPT invents prices and terms
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A knowledge base exists, but nobody uses it in the moment of a dialog
What we deploy
Document indexing
Upload of policies, FAQ, contracts, catalogs; chunking, embeddings, updates when content changes.
Semantic search + LLM
Hybrid search, answers with sources, context limited to the job.
Delivery channels
Internal chat for staff, a support widget, a sales agent on the same base.
Quality control
Logs, answer scoring, topic bans, human escalation when confidence is low.
Cases
Cases for this scenario
ChatNeuron
ChatNeuron — cloud AI agent and widget · CIS-ready
We built NeuronChat as SaaS: portal with dashboard, agents, dialogs, leads, analytics, and knowledge base. Site widget, RAG training, multiple agents for different domains in one account. CIS-ready: multilingual AI and local CRM/1C integrations.
FAVORIT
Costbl — SaaS cost estimation for metal parts from drawings
More than 5 months, two stages. Stage 1 — prototype: AI chat and PDF parsing (screenshots in the stage 1 block). Stage 2 — current state: full calculation (video in the stage 2 block). Product: https://costbl.ru/
B2C NDA
AI Tutor: smart learning for kids
We built an MVP with AI for kids’ learning: assignment control, performance, question bank. The client did not develop the product further — the case stands as edtech-MVP launch experience.
Cost guides
Volume depends on document count, update frequency, and channels (internal chat / customer agent).
| Package | What’s included | Price | Timeline |
|---|---|---|---|
| RAG pilot | One document corpus, chat/widget, answers with sources | from $2,273 | 2–4 weeks |
| Knowledge base + support agent | Roles, knowledge-base updates, handoff, quality analytics | by scope | from 1–2 months |
| On-prem contour | Local models and a vector store with no cloud | by spec | from 2–3 months |
FAQ
Does RAG fully remove hallucinations?
No — it lowers the risk. The model can still be wrong. That is why we cite sources, cut context, and keep a human handoff on critical scenarios.
Which documents work?
FAQ, policies, offers, manuals, catalogs, internal wiki. We take scans and “noisy” PDFs too — with preprocessing and index-quality checks.
Can we do this without the cloud?
Yes. For a strict data contour we assemble on-prem: local LLMs, pgvector/another store, access only inside the perimeter. See also On-Premise AI.
See also
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All AI agents and AI integration
Service hub: pilot, products, stack, and FAQ
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On-Premise AI
Local GPU contour without the cloud
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AI video analytics
Computer vision for production lines
Discuss RAG and a knowledge base
Send a sample document corpus — we will estimate the pilot and contour (cloud / on-prem).
