Make your team more productive with AI.

Show us a week of AI coding. We'll help your team turn it into shared workflows, reusable context, and a rollout plan.

Start with the AI work already happening.

The first step is concrete: read recent sessions, connect the surrounding work, and identify what should become a team practice.

Review recent agent sessions and the surrounding PRs, reviews, issues, incidents, docs, setup files, and spend.

Turn the strongest local practices into shared skills, hooks, commands, docs, and task-ready context.

Define sync policy, BYOK, review paths, success metrics, and the operating model for team-wide AI use.

The audit is the starting point. The service is helping the team deploy better AI coding practices.

See who is using AI heavily, lightly, or not at all, and what tools, prompts, hooks, skills, and MCP servers show up in real work.

Tie agent sessions to PRs, reviews, incidents, issues, docs, product signals, reverts, and repeated follow-up work.

Turn the best local practices into shared skills, hooks, commands, docs, and task context the rest of the team can reuse.

Help teams adopt AI coding with local-first capture, explicit sync, access policy, BYOK, and a clear operating model.

Useful AI deployment work should leave behind more than a report. It should give the next engineer and the next agent a better starting point.

Which engineers and teams use AI, what they use, and where setup gaps remain.

Which sessions became shipped PRs, incident fixes, docs, review fixes, or rework.

Skills, hooks, rules, docs, prior PRs, and repeated constraints worth injecting before the next agent run.

Token-heavy loops, repeated repo discovery, missing context, and workflows that should be standardized.

A practical path for moving from scattered local agent use to shared AI engineering practice.

Keep the strongest parts of enterprise deployment: local control, provider control, and visibility into what AI changed.

Session data starts on each developer machine. Teams decide what syncs into the shared record.

Keep model traffic under your provider accounts. FML does not need to proxy or store API traffic.

Understand what AI touched, what context it had, what changed, and which decisions shaped the result.

We'll review the sessions, map the patterns, and help your team deploy the practices worth keeping.