AI Solutions
RAG & Enterprise Knowledge Systems
The challenge
Critical knowledge is scattered across file shares, wikis, ticketing tools and inboxes. Generic chatbots either cannot see it or answer confidently without evidence. Retrieval-augmented generation connects language models to your verified sources so answers are grounded, cited and auditable.
Capabilities
What we deliver
Ingestion and document intelligence
Pipelines that parse PDFs, scans, spreadsheets and web content, preserve structure and keep indexes in sync with source systems.
Hybrid retrieval and re-ranking
Vector, keyword and metadata search combined and re-ranked so the model receives the most relevant context, not just the most similar text.
Permission-aware access
Retrieval filtered by the user's identity and group membership, integrated with your SSO and existing access controls.
Evaluation and observability
Golden question sets, faithfulness and relevance scoring, and production tracing so quality is measured continuously.
Typical deliverables
- Production RAG service with API and chat interface
- Ingestion connectors for agreed source systems
- Evaluation harness and quality dashboard
- Operational runbook and handover documentation
Technologies we use
- LangChain
- LlamaIndex
- pgvector
- Pinecone
- Elasticsearch
- Python
Relevant industries
RAG & Enterprise Knowledge Systems: common questions
How do you prevent the system from making things up?
We constrain answers to retrieved sources, require citations, decline when evidence is missing, and measure faithfulness with automated evaluations before and after launch.
Can it run inside our own cloud account?
Yes. We regularly deploy into customer-owned AWS, Azure or Google Cloud environments, using private model endpoints where required.
Talk to us about RAG & Enterprise Knowledge Systems
Share your goals and constraints. We'll reply within one business day with practical next steps.