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Staff hunt for answers in chats and outdated PDFs
- 02
Clients get different answers - no single source of truth
- 03
A “bare” GPT invents prices and terms
- 04
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
Cloud AI agent and widget
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
SaaS cost estimation 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 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).
