Multi-source knowledge retrieval for enterprise workflows
Retrieval systems that read across scattered internal sources, plus summarisation pipelines and assistants embedded where the work already happens.
Capability example. This describes the kind of work we are equipped to do. It is not a specific client project.
- Services involved
- AI development
The problem
Knowledge lives in document stores, ticketing systems and databases that never agree with each other, so answers depend on who you ask.
Where it applies
Applied AI & RAG
How we approach it
- 01
Ingest and normalise multiple source systems into a single embedding pipeline
- 02
Design chunking and metadata so retrieval respects permissions and recency
- 03
Ground responses with citations back to the source document
- 04
Embed the assistant into existing workflow tools rather than a separate app
What this is designed to change
- Answers traceable to a source document
- Summarisation pipelines running unattended
- Assistants integrated into enterprise workflows
Typical stack
- Python
- LangChain
- Vector DB
- PostgreSQL
- OpenAI API
Next
All work
AI reliability & evaluation
Benchmark frameworks for enterprise AI workloads
Evaluation harnesses measuring retrieval relevance, hallucination rate and latency, so AI changes ship on evidence rather than impressions.
See how we approach it