Connected knowledge sources
Ingest and organise approved content from available document systems, databases and knowledge tools. Track ownership, updates and deletion requirements.
Build / Answers with a source behind them
Enterprise AI search & RAG.
Build internal search and knowledge assistants that retrieve relevant information from your approved sources. We connect documents, permissions and current context so people can find and verify what they need.
Discuss this with usThe opportunity
Make company knowledge easier to use.
The answer exists somewhere in shared drives, wikis or internal systems. People spend time locating it, checking whether it is current and working out which document to trust.
We begin with the questions people need answered and the sources they are allowed to use. Retrieval-augmented generation, or RAG, combines search with an answer-generation step. We evaluate retrieval and answer quality separately, and preserve access restrictions throughout.
What we can do.
Permission-aware retrieval
Connect retrieval to the user’s access rights. Design filtering, ranking and citations around the actual information need, including cases where no relevant source exists.
Grounded answers & evaluation
Present supporting sources and useful limitations. Test whether the right evidence is retrieved and whether the answer accurately reflects it.
Tangible outputs
What you receive.
The exact scope is agreed around your workflow. A typical engagement can include:
- A source inventory with ownership and access rules
- A search index and content-refresh pipeline
- A search or assistant interface with citations
- Retrieval and answer-quality evaluations
- Operating guidance for source updates and permissions
Illustrative workflow
A colleague asks about a customer process
Check their access → retrieve current policy and guidance → prepare an answer → show the relevant passages and document links.
An example of how this capability can fit a business process.Define what better looks like.
We agree useful measures before implementation and compare them with the current workflow.
- ↗Relevant-source retrieval on real questions
- ↗Answer correctness and citation usefulness
- ↗Time to find and verify information
Your questions, answered.
Does RAG mean training on our documents?
RAG retrieves relevant material when a question is asked and supplies it as context. It is different from changing a model’s parameters through training.
Can an assistant respect document permissions?
It should. Access rules must be applied to retrieval and tested with different user roles, including attempts to access restricted information.
What happens when a document changes?
The implementation needs a defined refresh and deletion process. We agree how quickly changes must appear and who owns the source content.
Useful before you start
Questions worth exploring.
AI customer service starts before the answer
The most useful first improvement may be finding context, summarising a case or helping an agent decide what to do next.
RAG or fine-tuning for company knowledge?
Finding current information and adapting model behaviour solve different problems. Identify which one your application actually has.
How to choose your first AI project
Start with work you can observe, data you can access and an outcome you can measure.
A useful next step
Bring us the work
that should work better.
One process, one product idea, or a question about where to start.