Document & data ingestion
PDFs, Office files, websites, databases and ERP / POS records indexed securely.
AI services
RAG implementation: AI that answers from your own data
Turn contracts, SOPs, product catalogues and ERP records into an assistant that answers accurately — and shows exactly where every answer came from.
What we offer
What's included
Permission-aware search
Users only retrieve what they are allowed to see — by role, branch or department.
Cited answers
Every answer links to its source passage so teams can verify and act.
Accuracy evaluation
Test sets built from your real questions; accuracy is measured, not assumed.
Why RAG instead of a generic chatbot?
Large language models write fluently, but on their own they do not know your prices, policies or stock levels — and they can confidently make things up. Retrieval-augmented generation fixes this: before answering, the system retrieves the most relevant passages from your data and the model answers only from them, with citations.
What we connect
- Policies, SOPs, HR handbooks and contracts (PDF, Word, Google Drive, SharePoint)
- Product catalogues, price lists and technical manuals
- ERP, POS and accounting records: sales, stock, customers and invoices
- Websites, help centres and ticket history
How it works
- Ingest: documents and records are cleaned, split into passages and indexed in a vector database.
- Retrieve: each question fetches the best-matching passages — filtered by the user's permissions.
- Generate: the model answers using only those passages and cites them, or says it does not know.
- Evaluate: we measure accuracy on real questions and keep improving retrieval.
Where it pays off
Sales teams quoting the right price in seconds, support agents answering from the latest policy, finance searching thousands of invoices in plain language, and new staff getting onboarded without waiting for a senior colleague.
FAQ
Frequently asked questions
What is RAG (retrieval-augmented generation)?
RAG is an AI technique where the system first searches your own documents for relevant passages and then answers using only those passages, with citations — so answers are accurate and verifiable.
How is RAG different from a normal chatbot?
A normal chatbot answers from general training data and can invent facts. A RAG assistant answers only from your approved content and says when it does not know.
Which documents can you connect?
PDFs, Word and Excel files, Google Drive, SharePoint, websites, help centres, databases and business systems such as your ERP or CRM.
Can different staff see different information?
Yes. Retrieval respects permissions, so each user only gets answers from documents they are allowed to access.
Is our data used to train public AI models?
No. Your documents stay in your private index and are never used to train public models.
Can it run on our own servers?
Yes. We can deploy on your cloud account or on-premise servers, using open-source models where required.
How do you measure accuracy?
We build a test set from your real questions, measure how often answers are correct and properly cited, and improve retrieval until targets are met.
How long does a RAG project take?
A first working assistant on a set of your documents typically takes 2–3 weeks; rolling out to more sources and users follows in phases.
Start with one process. Prove it in four weeks.
Pick the process that costs you the most time — we'll run a working pilot on your data.