Client Intelligence OS

Problem

Important signals in ongoing client conversations can become fragmented across sessions, making it difficult to trace conclusions back to evidence, identify changes over time, and turn insights into accountable follow-up without over-relying on AI-generated interpretation.

Approach

Client Intelligence OS combines deterministic analysis with a provider-isolated LLM path, evidence verification, exact source references, persistent workspace-scoped data, and human-controlled review workflows. The system extends individual analyses into longitudinal intelligence and follow-up actions while using explicit validation, concurrency controls, rate limits, and bounded inference admission to keep AI-assisted operations reviewable and controlled.

Outcome / Learning

An actively developed AI product exploring how evidence-grounded analysis can be combined with persistent client context, longitudinal signals, and human-controlled actions without treating model output as an autonomous decision. The build has deepened my work in multi-tenant backend design, AI reliability, persistence, concurrency, security boundaries, and controlled inference.

Key Features

• Evidence-backed conversation analysis with exact source references
• Structured findings, risks, recommended actions, and missing-information signals
• Deterministic analysis baseline with optional LLM-assisted analysis and fallback
• Human-controlled approval and follow-up workflows
• Longitudinal intelligence across client interactions
• Workspace-scoped authentication, persistence, and data isolation
• Assignee, due-date, completion, and follow-up action workflows
• Optimistic concurrency and stale-update conflict handling
• Independent rate-control pools and bounded inference admission
• Responsive React review workspace with typed API integration