SAGE

The company had been in business for decades and had never seen an AI system. Hundreds of employees, mountains of paper, and processes built for a world that moved on. The brief wasn't 'build AI.' The brief was 'make this work better.' AI happened to be the answer.

SAGE is a full-stack enterprise intelligence platform built to process the overwhelming volume of corporate email across every mailbox in the company. It transforms raw communications into structured, actionable business insights through a dual-pipeline architecture: parallel processing pipelines, coordinated AI agents, and a backend built entirely on Convex functions.

The Email Intelligence Pipeline starts with a self-learned exclusion filter, discovered through continuous analysis, that strips the bulk of the noise before any processing begins. Specialized AI agents then work in sequence: an Extraction Agent identifies entities and relationships, an Analysis Agent powered by a custom fine-tuned local model performs conversation-level intelligence, and a Reviewer Agent validates every output before it reaches decision-makers.

Running in parallel, the HR Data Hub automatically syncs employee records from Microsoft Graph, cleans and normalizes the data, and constructs the organizational hierarchy. This living org structure powers intelligent routing, ensuring analyzed communications reach the right department heads without manual intervention.

The entire system runs on a dedicated Mac Studio via Docker, with Convex self-hosted on PostgreSQL as the complete backend. The AI stack uses Ollama for local inference with Z.AI as a cloud fallback, protected by a circuit breaker pattern. A layered reinforced learning memory system continuously improves analysis accuracy across sessions. Zero data leaves the premises and zero cloud AI costs.

The frontend delivers real-time dashboards that stay smooth even under heavy data load, built on TanStack Virtual for large tables. The backend orchestrates scheduled jobs, parallel work pools, durable workflows, and a self-healing watchdog that monitors system health continuously.

At its core, SAGE builds lasting institutional intelligence through a layered reinforced learning memory system. Decision Transformer, Q-Learning, Actor-Critic, and several other algorithms work together to continuously improve analysis accuracy. A vector search engine powered by HNSW graphs with GNN enhancement improves retrieval quality, while WASM-powered ONNX embeddings run directly in-browser with zero overhead, enabling semantic understanding of every communication without external API calls.