We don't just measure who's behind on AI coding. We close the gap.

Accrete analyzes your team's real Claude Code sessions and builds a per-person map of which AI-coding practices each engineer actually uses — that map is the adoption matrix, and it shows exactly who's behind on what. But measuring the gap is only half a product. The training loop is the other half: each gap the matrix surfaces becomes a focused training built for that specific gap, then we re-measure the same practice to see whether it actually moved. Diagnosis, then training, then evidence it worked. Or evidence it didn't, which is just as useful.

The loop, in four steps

1. Measure

A small sync client parses your team's Claude Code session transcripts and classifies the practices in each one — plan mode, subagent delegation, memory and context, committing work, and the rest. It runs in the background, continuously. This is the engine everything else sits on; the full pipeline is on how it works.

2. Diagnose

The adoption matrix turns that raw signal into a read: which practices each engineer has and hasn't picked up, and exactly where the team plateaus. Not a vibe about who's "good with AI" — a per-person, per-practice map of what's actually showing up in real sessions.

The practices in the matrix aren't a fixed checklist of beginner habits. We build the catalog with you — the practices your team actually cares about, the standards you're trying to set, and the moves your own strong engineers already invented. The bar rises with you: if the fundamentals are already universal, we measure the harder things — context management, when to parallelize subagents, whether the right agentic workflows are being used in the right places.

3. Train

A single hands-on working session with your engineers, run against your team's real repo — not a generic "intro to AI coding" deck, and not a lecture. It targets the real gap the matrix found: if half the team has never planned before coding, that's the session. Built from your own team's session patterns, including the moves your strongest engineers already make. The matrix also surfaces your most AI-forward engineers. Nominate them as internal champions: their practices spread by example, so the rest of your team rises to your best performers' level.

4. Re-measure

The same engine watches the same practices a few weeks later. You see whether the behavior shows up more consistently — or it doesn't, and we adjust the next session. The measurement is how we hold ourselves accountable to the result, not just how we hold up a chart.

Built from your team's sessions, not a stock deck

The reason the training step works is that it isn't generic. Most "upskill your team on AI" programs are a stock curriculum delivered to a room — the same slides whether your team's real weakness is context management or never delegating to a subagent. The room nods, and a month later the gap is exactly where it was.

The matrix removes the guesswork about what to teach. Read down a column and you see the practice the whole team plateaus on; read across a row and you see one engineer's actual repertoire and their obvious next move. The training targets that — and some of the best material comes from your own people, the practices your strong engineers invented that the matrix lets you discover and spread.

Want the loop running on your team?

If you want it built around your team's real gaps, become a design partner — you work directly with us and help shape what we measure and how we train. Join the early access list to be first in line when the loop opens up. Early teams get early pricing and a real say and influence on the product.