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Secure generative AI with retrieval-augmented generation
An AI assistant that answers from your own documents and data, respects permissions, cites sources and is measured for accuracy.
The challenge
Employees spend hours searching policies, contracts and tickets. Public AI tools can't see company knowledge, and pasting sensitive data into them is a risk.
Our approach
An indexing pipeline cleans, chunks and embeds approved sources into a vector store with access labels. At question time we retrieve only what the user may see, ground the language model in those passages, and return answers with citations, guarded and evaluated.
Architecture
How the pieces fit together. Scroll sideways on small screens.
Key components
- Azure OpenAI, Amazon Bedrock or Google Vertex AI
- Vector search with keyword hybrid retrieval and reranking
- Identity-aware filtering using existing permissions
- Guardrails for PII, content safety and prompt injection
- Evaluation sets, feedback capture and usage monitoring
What you can expect
- Answers grounded in company knowledge, with sources
- No exposure of data a user isn't allowed to see
- Accuracy tracked with a repeatable test set
- A reusable foundation for further AI use cases
How we deliver it
Assess
Review current systems, data and goals, and agree success measures.
Design
Target architecture, security model, plan and fixed quote.
Build and test
Agile sprints with demos, automated tests and documentation.
Launch and support
Production rollout, knowledge transfer and optional managed support.
Have a data, AI or finance challenge?
Tell us what you need. Our team will set up a no-cost discovery call.