RAG / Enterprise Knowledge Assistants
Enterprise Knowledge Assistants Built on RAG
Your company's knowledge is spread across PDFs, drives, wikis, tickets and databases. We build retrieval-augmented (RAG) assistants that find the right information, answer in plain language, cite their sources and respect who is allowed to see what.
- Free consultation
- NDA available
- Milestone-based delivery
What it changes for your business
- 01
Answers in seconds
Staff stop hunting through folders and chat threads for the latest version.
- 02
Trustworthy by design
Every answer links to the source passage so people can verify it.
- 03
Faster onboarding
New team members get expert-level answers from day one.
- 04
Access respected
The assistant only retrieves documents the user is permitted to see.
What goes into a production RAG system
Good retrieval is engineering, not a demo notebook.
- Document ingestion and sync pipelines
- Chunking, embeddings and hybrid search
- Vector databases and re-ranking
- Citations and source previews
- Permission-aware retrieval
- Evaluation on real questions
- Structured data + document answers
- Admin tools to manage content
How it works in practice
Policy assistant
- Employee asks a question
- Relevant policy retrieved
- Answer with citation
- Feedback captured
Technical support search
- Engineer describes issue
- Past tickets and manuals searched
- Likely fix suggested
RAG vs a traditional chatbot
A traditional chatbot follows scripts or relies on what a model memorised. RAG retrieves your current documents at question time and answers from them — so answers stay up to date, cite sources and can be restricted by permission.
How we deliver
Milestones with a working demo at each step. You always know what’s done, what’s next and what it costs.
- 1Discover
- 2Design
- 3Build
- 4Test
- 5Deploy
- 6Support
Technology
Chosen per project for quality, cost and control.
- LlamaIndex
- LangChain
- pgvector
- Qdrant / Pinecone
- OpenAI
- Claude
- Open-source embeddings
- Python
- FastAPI
Frequently asked questions
Something else on your mind? Ask us directly.
Which document types can a RAG assistant use?
PDFs, Word and Excel files, web pages, wikis, helpdesk tickets, emails and database records. Scanned documents can be processed with OCR first.
Can we host it privately?
Yes. The assistant, vector database and even the language model can run inside your own cloud account when required.
How do you measure answer quality?
We build an evaluation set from real questions and expected answers, and track retrieval accuracy and answer correctness before and after every change.
Related services and guides
- AI ChatbotsWebsite, WhatsApp and internal chatbots that answer from your own content.Learn more
- AI DevelopmentCustom AI applications, LLM integration and AI search built around a business goal.Learn more
- AI IntegrationLLMs, AI search and agents added to your existing ERP, CRM or product.Learn more
- AI Agent DevelopmentCustom AI agents scoped to one job, your tools and a clear handoff.Learn more
Turn scattered documents into instant answers
Tell us where your knowledge lives and who needs it. We'll outline a secure assistant for your team.
Usually responds within one business day.