Roll out AI coding with context and control

FML gives engineering organizations a live record of agent work, repo history, reviews, and setup patterns, then turns that evidence into task-ready context for every change.

AI coding is becoming
the default way software
gets built.

The enterprise problem is no longer whether engineers will use AI. They already are: different tools, different prompts, different local setup, and different context in every session.

FML makes that work visible and reusable. It captures what happened in each session, connects it to repo history and reviews, compares how teams configure their agents, and packages the right evidence into context for future work.

The context system

Everything your team needs to make AI coding visible, reusable, and grounded in the codebase.

Agent work record

Every AI coding session becomes a searchable timeline and summary. See what changed, which tools were used, what it cost, and where agents got stuck.

Repo memory

Git history, GitHub PRs, reviews, reverts, ownership, and session outcomes become a living memory of how the codebase evolved and why it works the way it does.

Setup patterns

Compare skills, hooks, permission rules, MCP servers, local docs, models, and repeated workflows. Turn the best local setups into reviewed team standards.

Task-ready context

Package the right summaries, facts, files, constraints, tests, hooks, and skills through CLI, Slack, and MCP before the next agent starts editing.

Outcome learning

PR reviews, rejected changes, incidents, and recurring fixes flow back into the knowledge base. The system learns from outcomes, not just prompts.

Rollout controls

Bring your own keys, keep local-first capture, choose what syncs, define access policy, and roll out AI coding context without giving up operational control.

Built for the way
engineering teams
actually roll out AI.

Start with local capture for individual developers. Add org sync when you want a shared view of sessions, costs, summaries, and team setup. Layer in repo memory when you want agents to understand not just the current files, but the history that made those files important.

Enterprise rollout is about turning scattered local agent use into a shared operating model: what happened, what the codebase history says, what context each task needs, and what should be reviewed.

Built for security

Your code, your keys, your data. Local-first capture with explicit sync and access controls.

Local-first

Session data stays on each developer’s machine. No data leaves the device unless you explicitly configure sync.

Bring your own keys

Your own API keys for every model provider. We never proxy, store, or see your API traffic.

Full audit trail

Every session is timestamped and queryable. Know exactly what AI touched, what context it had, and which decisions shaped the outcome.

Let's talk about your team.

Tell us about your engineering org and we'll show you how FML fits.