The problem it solves

Traditional engineering organizations declare initiatives upfront in Jira, in roadmaps, in slide decks. Then they ask the team to manually map every piece of work back to those initiatives through tickets, labels, status updates, and weekly syncs.

The process is fragile, incomplete, and entirely dependent on human discipline that nobody maintains. For an AI-first team shipping at higher velocity than any planning ritual can keep up with, the gap between declared strategy and actual execution opens within a week and never closes.

Initiative Discovery takes the opposite approach. Instead of asking the team to declare initiatives, the system reads the code and tells you what the initiatives are.

How it works

The discovery surface clusters commits and pull requests using multiple signals: branch names, file paths, conventional-commit types, repository boundaries, contributor patterns, and semantic relationships. It builds a three-level hierarchy — themes, initiatives, workstreams — with the supporting commits attached to every node.

A reviewer confirms or corrects each proposed initiative. Each correction becomes labeled data that the system learns from. Over time, the discovery output sharpens toward the actual shape of how the organization works.

Why it matters for CTOs and PE operators

For a CTO or VP of Engineering inheriting an existing codebase, Initiative Discovery answers the first question of every new tenure: what is this team actually building? For private equity operators evaluating portfolio companies, it produces a working strategy from the codebase itself — not what the deck says, not what the founder claims, but what the commits prove.

Related terms

Autonomous Intelligence — the broader category that Initiative Discovery is part of
Atomic Code Linking — the attribution layer that keeps initiatives accurate after discovery
Execution Intelligence — the platform category InteliG occupies