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AI Agents & RAG
A model that only knows its training data can't answer questions about your business. Retrieval-augmented generation (RAG) gives it your documents and databases at question time, and agents let it take actions through your tools. We build both so answers are grounded, traceable and testable.
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What we deliver
- Ingestion pipelines for documents, wikis, tickets, databases and APIs
- Chunking, embedding and hybrid search tuned on your own questions
- Answers with citations back to the source passages
- Tool-using agents with permissions, approvals and audit logs
- Retrieval and answer-quality evaluation, so changes can be measured
- Access control so people only retrieve what they're allowed to see
Where it's used
Support and sales knowledge assistants
Policy, legal and compliance Q&A
Research and analyst copilots
Workflow agents that file tickets or update records
How we work
- Discover. We look at your data, define success metrics and agree what "good" means before building anything.
- Build. We start with a baseline, iterate with measured evaluation, and keep you in the loop with working demos.
- Ship & monitor. We deploy with tests and rollback plans, then monitor quality, cost and drift.
Tools we use
pgvectorElasticsearchLlamaIndexLangChainFastAPIPostgresRedis
Questions
RAG or fine-tuning?
Use RAG when the system needs current or private knowledge, and fine-tuning when you need a consistent style or behaviour. Many systems combine both. We wrote a short guide on how to choose.
Can an agent act without human approval?
It can, but we usually start with approvals for anything irreversible, and relax them as the measured track record builds.