Connected knowledge
Documents, policies and system data are organised around how people actually look for answers.
Capability / 02
Answers with evidence behind them.
Retrieval-augmented systems that make internal knowledge usable: locate the right source, retrieve the relevant passage, and give people an answer they can trace.
Built for the real work
Designed around how your team makes decisions.
Knowledge is rarely stored in one neat place. A durable RAG system has to account for document quality, access permissions, changing source material and the difference between finding a document and answering a question.
We build retrieval around the evidence people need to see. The result is a system that can make a useful answer quickly while preserving a clear path back to the source.
Better retrieval
Search combines semantic relevance, metadata and reranking so the right evidence reaches the model.
Source-aware answers
Every response can show where it came from, making it easier to verify and trust.
How we approach it
Understand the source estate
We assess where information lives, how it changes and which documents are authoritative for each type of question.
Build retrieval that earns trust
Content is chunked, embedded and indexed with metadata, then tested with hybrid retrieval and reranking against real questions.
Keep the answer accountable
Citations, access controls and feedback loops make it clear what the system knows, where it found it and where it needs help.
Technology approach
The tools are selected for the work, not the other way around.
Retrieval layer
- Milvus vector database
- Embeddings
- Hybrid search
- Reranking
Knowledge sources
- Documents and PDFs
- Wikis
- Databases
- Connected business systems
Answer layer
- OpenAI
- Anthropic
- Google Gemini
- Open-source models
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