AI
RAG
We build systems that answer from your documents and show where the answer came from.
The situation
The knowledge sits inside documents but finding it takes time. Locating the right document often takes longer than asking the question.
Our approach
We make the documents searchable. When a question comes in, the relevant passages are found and the answer is built from them.
What we do
- Document processing and chunking
- Semantic search infrastructure
- Source attribution and citation
- Document access by permission
- Measuring answer accuracy
Technologies we use
Backend
- Node.js
- Python
- .NET
Database
- PostgreSQL
- Supabase
Cloud
- AWS
- Azure
AI
- OpenAI
- Gemini
How we work
Discover
A map of the current state and a prioritised list of needs.
Define
A scope document, success criteria and a schedule.
Design
A clickable interface prototype and the design system.
Develop
Working builds delivered at regular intervals.
Deploy
The live system, technical documentation and a hand-over session.
Grow
A usage report and a prioritised improvement plan.
Common questions
- Which document formats are supported?
- PDF, Word, Excel and plain text are commonly supported. For scanned documents we add a text extraction step.
- Can everyone access every document?
- No. Permissions work at document level, so a user only gets answers from documents they are allowed to see.
Other services in this area
Let's talk about this
A short call to get clear on what you need. We will tell you plainly which approach fits.
Start your project