AI Knowledge & Retrieval Systems
Build multi-agent retrieval-augmented generation systems for enterprise use — from architecture design through production deployment, including on-device and edge-first configurations.
Service Overview
If your team has already tried a basic RAG implementation and hit a wall — stale retrieval, hallucinated citations, agents that don't know when to defer — this is the next layer: production-grade, multi-agent retrieval architecture built for real enterprise data, not a demo notebook.
What's Included
- architecture design
- retrieval pipeline engineering
- multi-agent orchestration
- evaluation framework
- and production deployment support (including edge-first configurations where data can't leave the device).
Tools & Technologies
Frequently Asked Questions
What's the difference between RAG and agentic RAG?
Standard RAG retrieves and answers in one pass. Agentic RAG adds decision-making — the system can re-query, verify, or route to a different agent when the first retrieval isn't good enough.
Can this run without sending data to a third-party API?
Yes — on-device and edge-first configurations are part of the offering, specifically for teams that can't send data externally.
Do you build on existing vector DB infrastructure or start from scratch?
Either — most engagements start with an audit of what you already have.
Is this for you?
- You need custom AI development that goes beyond a vector-DB-plus-prompt starter kit
- Your RAG implementation works in testing but breaks down on real document volume or query complexity
- You want an agentic AI consultant who can design multi-agent handoff logic, not just single-shot retrieval