Case study
Enterprise Multi-LLM RAG Agent Platform
Built a citation-grounded, multi-model AI platform for regulated enterprises, giving teams trustworthy answers across 125+ AI models without vendor lock-in.
Overview
The client's teams were already using AI heavily — pasting company documents into personal ChatGPT and Claude accounts to get work done faster. The engagement was to turn that ad-hoc habit into something the whole organization could actually run on.
The Challenge
Every useful answer a person got from AI lived and died inside a private chat window nobody else could see. Underneath that, the documents feeding those tools were quietly going stale, with old and current guidance sitting side by side, equally citable, equally confident. For a business serving regulated industries like healthcare and education, that wasn't a minor bug — an AI answer nobody could verify was a compliance risk.
The business impact:
- Nothing one person learned from AI ever reached a teammate — every new question started from zero.
- No way to catch a wrong or outdated answer until it was already in front of a client or an auditor.
- Every AI vendor choice was becoming permanent by accident — months of tuned prompts and workflows tied to one model, with no easy path to a better one.
- AI usage and spend were scattered across individual employee accounts with no central visibility or control.
Approach
How do you stop knowledge from getting lost in private AI chats?
Company knowledge lived scattered across private ChatGPT and Claude histories nobody else could see, so two people could ask the same question and get two different, unverifiable answers. We built a governed knowledge layer — structured, version-controlled, human-approved documents instead of a black-box vector index — so every AI tool reads from one shared source, and every answer traces back to the exact approved document it came from.
What happens when a team wants to switch AI tools?
Bringing in a new AI tool, or trying a better model, meant re-teaching it the business from scratch, and nothing carried over. We built native MCP connectivity, including a custom-built MCP server and a self-serve interface for teams to connect their own tools and models without engineering involvement, so knowledge stays portable across every AI a team uses.
How do you keep 125+ AI models under control?
API usage was scattered across individual accounts with no central visibility, and switching providers meant losing months of tuning. We built a centralized model gateway — API keys, role-based access, and spend tracking across every major provider — so leadership can see and control usage and cost without losing anything when a model changes.
Who's accountable when an AI agent takes action?
In a regulated environment, an AI agent quietly running a tool on its own isn't acceptable, and there was no record of what it had been told. We built approval-gated execution so sensitive actions route through a human before firing, plus a running timeline of what changed, when, and who approved it, so nothing acts alone and everything stays auditable.
The Result. Teams get citation-backed answers using 3× fewer tokens per query, a 97% answer-quality pass rate against 90–93% for standard retrieval, and a deployment posture that satisfies SOC 2, HIPAA, and GDPR requirements out of the box.
Tech Stack
Outcome
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