We build RAG (retrieval-augmented generation) over your documents and structured data — with citations, an audit log, and hallucination guardrails. Not a conference demo, but a production deployment: guardrails against prompt injection, a cost gate against runaway spend, and observability on every LLM call.
You can build a RAG prototype in an afternoon with a YouTube tutorial. A production deployment that doesn't hallucinate, doesn't leak data it shouldn't, and doesn't bleed money is a different discipline entirely — and that's where most vendors stop.
Five test questions look fine. In production, with thousands of real queries, the model starts inventing what isn't in the data, or answers confidently wrong — and nobody catches it because there's no audit log or confidence monitoring.
Prompt injection from user input or an indexed document can bypass instructions and pull out data the answer should never touch. Without explicit guardrails and low temperature on factual queries, it's not a question of if, but when.
Without a cost gate and a deliberate choice of when to call an expensive model versus a cheaper one, LLM call costs scale linearly with traffic and nobody's watching — until an invoice arrives that hurts.
Retrieval, generation and operations are treated as a production system from line one — not a proof-of-concept that later has to be rebuilt just to be deployable.
Semantic search (embeddings + vector database) combined with classic keyword matching, so the answer surfaces what purely semantic search would miss — exact numbers, codes, names.
Where data is structured (databases, tables), we generate queries directly, with automatic repair when a query fails or returns an empty result — instead of the assistant guessing from a text description of the data.
Low temperature for factual accuracy, an explicit boundary between instructions and data, and human-in-the-loop on edge cases where the system is uncertain — the same principle behind our AI invoice data extraction.
Every LLM call is logged — what went in, what came out, which source the answer drew from. The user sees a citation; you get an audit trail for when you need to trace back exactly why the assistant answered the way it did.
We pick the model based on query complexity, use prompt caching (Anthropic) for repeating context, and watch costs so they don't scale unchecked with traffic — instead of one expensive model billed for everything.
For more complex multi-agent systems (like the municipal chatbot for Zlín), flows and agents are defined as rows in a database, not in code — logic can be changed without a redeploy, cutting the iteration cycle from months to weeks.
For the city of Zlín we built a multi-agent swarm engine with self-healing text-to-SQL queries — one of the first live citizen-facing AI chatbots of its kind in Czechia, which won a regional AI hackathon. For invoice processing we deployed AI data extraction with human-in-the-loop review on edge cases. For Continero (Robootec) we built self-hosted vector retrieval on OpenAI embeddings and pgvector, multi-provider LLM orchestration (Claude/OpenAI/Gemini/Grok) with Anthropic prompt caching, a custom MCP server, and local ONNX inference where cloud doesn't fit.
Tell us what data the assistant needs to know and what it's for — support, an internal knowledge base, or something like a citizen chatbot. We'll sketch the architecture (retrieval, guardrails, cost gate) and tell you honestly what's realistic within what time and budget.
Request a RAG assistant →