An Organizational Second Brain: Building an AI That Learns From Experts

Summary

Organizations often retain specialist knowledge in experts’ heads, leaving routine compliance assessments slow, inconsistent, and difficult to scale. The proposed “second brain” combines a structured, auditable knowledge architecture with a separate reasoning layer and a self-improvement loop that converts expert feedback into verified, regression-tested text updates without model retraining. High-density organizational positions, taxonomies, routing indexes, and gateway files are maintained in a dependency-linked filesystem, while sparse supporting material remains available through semantic or lexical retrieval. Composable recipes encode expert procedures, and an evaluation pipeline proposes auditable pull requests, retries failed fixes, and expands regression coverage after approval. After six weeks, SMEs reportedly found outputs useful almost always, assessments fell from days to minutes, and improvement cycles produced zero regressions; the architecture is presented as applicable beyond compliance.

Takeaways

1. 1

   The knowledge system uses YAML frontmatter with depends_on and referenced_by fields to form a bidirectional dependency graph, enabling impact tracing when files change.

2. 2

   High-density, frequently used organizational knowledge is stored in curated wiki files, while sparse or situational material remains in semantic or lexical retrieval systems.

3. 3

   After three development sprints over six weeks, the system reported assessments reduced from days to minutes, useful outputs almost always, and zero regressions across improvement cycles.