From a hard engineering problem with no margin for error — a legal product where wrong answers are liabilities — came a platform any business can deploy.
Five white-papers on what CaveauAI is, how it is secured, how it integrates, why the retrieval architecture is a measurable advantage, and what a full deployment looks like.
Building a legal-AI product where a wrong answer is a liability forces every hard problem at once: retrieval that is genuinely grounded, a credential vault that never leaks, a model layer that keeps sensitive data on controlled hardware, and a tool layer that can actually act on external systems. Solving those problems correctly produced an engine — and that engine is CaveauAI.
A multi-tenant AI platform that answers from your knowledge and acts on your systems, with every answer traceable to a real source. It was forged on a problem with no margin for error and generalised into infrastructure any business can deploy.
The prototype is 5% of the work. Grounding, tenant isolation, ingestion that self-heals, cost-aware model routing, a tool layer and observability are each a project on their own. Buying a proven platform turns an uncertain capital project into a predictable operating cost — value in weeks, not quarters.
A live, anonymised production build — the same pattern transfers.
Read →The case for CaveauAI in one page — share with decision-makers.
Read →The engineering foundation under all of this is documented in full in the thesis.
For most businesses the blocker to adopting AI is not capability — it is trust. Where does our data go? Who can see it? CaveauAI answers each question concretely.
Every request resolves to exactly one customer identity. Every search, every document fetch, every tool call is scoped at the data layer — not by a hopeful application-code filter, but as a condition baked into the query itself. Scoped tokens, per-customer retrieval profiles, and corpus separation enforce the wall.
AI that acts needs credentials. Each secret is sealed with AES-256-GCM — the same class of encryption used for classified material. The encryption key lives on a locked path, separate from the database. Secrets are decrypted only at the moment of use, then discarded. They are never logged, never indexed, and never reachable through search.
You decide where each workload runs. Sensitive, high-volume work runs on a GPU you control (Sovereign) — that data stays inside your own environment. Non-sensitive work can route to a frontier cloud model for maximum capability. The dial is set deliberately per workload, not left to chance.
| Tier | Where it runs | Data leaves? |
|---|---|---|
| Sovereign / local | GPU you control | Never |
| Hybrid | Your GPU (Sovereign) for sensitive, frontier for the rest | Only non-sensitive |
| Frontier | AWS Bedrock / OpenAI / Azure | Yes (non-sensitive) |
Every AI action is logged. Every grounded answer carries a retrievability trace — "why these sources" — so you can audit what the AI did on your behalf.
A platform is only useful if it reaches where your work actually happens. CaveauAI exposes its capabilities through four integration surfaces — deliberately spanning from "for engineers" to "for people who never touch code."
An open-standard (Model Context Protocol) tool layer for AI coding assistants, IDEs, and Copilot Studio. Point any MCP client at your endpoint and every tool you are entitled to appears. Your code repositories — on GitHub or your own git server — are first-class sources the AI can reason over, on your hosting, under your control.
Drag-and-drop automation provisioned per customer. One connector node imports all of a customer's CaveauAI tools at once — no manual wiring. Operations teams can assemble real automation: route a request, send an alert, chain a lookup into a notification.
A dedicated Flowise container per customer. Build grounded, on-brand conversational assistants by dragging components together — no code, your documents, your tools.
Because CaveauAI speaks the open MCP standard, its tools are consumable inside Microsoft Copilot Studio too — your copilot searches your knowledge and calls your tools without leaving the environment your staff already use.
Most AI coding tools find context with a single technique: semantic search. It is powerful and famously prone to returning the plausible-but-wrong result — the authenticate() in the logging library when you wanted the one in your auth service. CaveauAI combines two search techniques and fuses their results, then expands along a graph of how your code actually connects.
Keyword search (BM25) is exact where vector search is fuzzy. CaveauAI runs both and fuses the rankings with Reciprocal Rank Fusion — a result that both searches like rises to the top. The exact symbol you meant wins.
Hybrid search finds the right piece. But code is a web of relationships. CaveauAI builds a graph of your repositories — file summaries plus a relationship map — so a question like "what uses PaymentClient?" is one traversal instead of a manual grep across fifty repositories.
The same machinery powers the legal product, where a wrong answer is a liability. A single retrieval architecture that serves both "find the right function" and "ground a legal answer in cited law" is evidence of real engineering rather than a demo.
A telecom operator needed to let its customers help themselves — check a bill, place an order, raise a support ticket, find the right plan — through a conversational assistant instead of a call queue. Using CaveauAI we built it. It is live. This is what "we deployed CaveauAI" actually means in practice.
Strip out "telecom" and the architecture is industry-neutral. Any business with a system of record — billing, inventory, CRM, ticketing, an ERP — can get the same shape: tools over your systems, grounded knowledge from your documents, specialist agents, automated multi-step processes, your model tier.
A short conversation about your documents, systems, and risk tolerance.
One high-value workflow, on the right tier, proven on your material.
Integrate via the surface your teams use, secured and isolated.
Add workflows on the same hardened base — maintenance is shared, not re-bought.
Tell us your documents, systems, and risk requirements. We will map them to a deployment tier, an integration surface, and a scoped pilot.
Contact BlueNoteLogic Explore the portalThe retrieval architecture that makes AI coding trustworthy.
Read →A live, anonymised production build with a transferable pattern.
Read →The engineering foundation under all of this is documented in full in the thesis.