Centralize unstructured data.
Pipe company conversations into a unified, shared memory repository refreshed every ten minutes.
All case studiesKnowledge-intensive enterprise services firm
Defensible Institutional Knowledge: LLM-Driven Querying with Robust RBAC
Critical insights were trapped in unstructured conversations, risking uninformed decision-making and data exposure. We engineered a shared memory architecture with rigorous role-based access, ensuring every AI-generated answer is instantly verifiable and tied to explicit source evidence.
Company profileKnowledge-intensive enterprise services firm.
Systems in playInternal communication channels, shared company memory repositories.
EngagementShared memory architecture and secure LLM deployment.
How the build ran.
The same stepped process we run on every engagement, applied to this company.
Enforce strict governance.
Map role-based access controls into the AI’s core logic so it inherently refuses queries users are not authorized to see.
Build the evidence engine.
Mandate that the system meticulously preserves the original source and date for every data point it ingests.
Deploy the interrogation interface.
Roll out conversational agents that deliver hyper-accurate answers tracing back to explicit evidence.
The architecture, in one picture.
The Sources (Comms Channels, Documentation) flow into The Shared Company Layer (timestamped memory, gated by role-based access), which feeds the Employee Query Interface and the Citation Logs.
The sources
Comms Channels
Documentation
The shared company layer
Timestamped memory, RBAC-gated
What reads from it
Employee Query Interface
Citation Logs
What changed operationally.
Eliminated Operational Guesswork.
Every organizational answer traces back to original evidence, ensuring stakeholders never act on assumptions.
Automated Data Governance.
The system natively understands permissions, protecting sensitive portco data by default.
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