DSI Lab · AI agents
RAG chatbots & custom AI agents on your data
Generic chatbots hallucinate because they don't know your business. We build retrieval-augmented (RAG) assistants and AI agents grounded in your own documents, products and data — so answers are accurate, current and traceable.
The problem
Your team answers the same questions all day, your knowledge is buried across PDFs, emails and spreadsheets, and off-the-shelf chatbots make things up.
What you get
Capabilities
Retrieval-augmented generation
We index your documents into a vector store (PostgreSQL/PGVector) so the model retrieves real passages before it answers — grounded, with citations.
Agentic workflows
Agents that don't just chat — they look things up, call your APIs, draft replies and complete multi-step tasks under guardrails.
Accuracy & guardrails
Evaluation sets, retrieval tuning, source citations and fallback rules so the assistant stays on-topic and admits when it doesn't know.
On your data, privately
Your content stays yours. We design for data privacy and EU/GDPR expectations, with self-hostable components where it matters.
Use cases
Where this fits
- Internal knowledge assistant for staff (policies, SOPs, product specs)
- Customer-support bot grounded in your help docs and order data
- Document Q&A over contracts, manuals or research
- Sales/onboarding assistant that qualifies and routes leads
Stack
Built with
Proof
Related work
Automated Email Triage
An AI capability that reads inbound customer emails, classifies them, identifies the customer, extracts the details, and decides what can be handled fully automatically — introduced measurement-first.
Read case studyLegal-Knowledge RAG Assistant
A Greek-language assistant that answers questions over a large body of legislation — hybrid retrieval (keyword + vector), LLM reranking, and a multi-layer verifier so answers stay grounded in the actual text, with citations.
Read case studyNeorama
Built with DSI CMS
An accredited training center (25 years, 15,500+ students, 40+ courses) running on our DSI CMS — content and courses managed without code.
Read case studyFAQ
Questions, answered
How is this different from ChatGPT?
ChatGPT answers from general training data. A RAG assistant answers from your specific documents and data, retrieves the relevant passages first, and can cite its sources — which dramatically reduces hallucination for business use.
Will our data be used to train public models?
No. We architect the system so your content stays private to your assistant, using providers and configurations that don't train on your data, and self-hostable components where required.
How long does a first version take?
Most assistants start as a paid pilot delivered in a few weeks, so you can validate accuracy on your real data before committing to a full build.
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Ready to scope it?
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