The highest-impact workflows
Where agents do real, repeated work on your codebase — mapped to your repo, not a generic checklist.
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Loading.The honest on-ramp: a scoped, time-boxed look at your real repo. Where agents help most, what's in the way, and a prioritized plan from copilot to operator. Not a readiness questionnaire; real work on your actual codebase.
not sure yet? take the free 2-minute readiness check first.
We build WRAI.TH, trovex and yoru, and run agentic development in production with Clareo Systems and Compagnie Immobilière du Léman.
Where agents do real, repeated work on your codebase — mapped to your repo, not a generic checklist.
What's actually in the way, and what AI won't fix. We'd rather tell you it's not worth it than sell you a year.
A prioritized route from copilot to operator — concrete enough that you could run it yourself if you wanted.
What you leave the assessment with: a short, specific plan in your repo's terms, yours to keep. Below is a representative brief built from a common case, a scale-up using AI as fancy autocomplete and ready for more. Yours is built inside your repo, so your findings will differ. The shape is the same.
Your team uses assistants well for line-level work: autocomplete, a function here, a test there. That is the copilot stage, and most teams are stuck in it. The repeated work that eats the week, triage, doc lookups across services, the same refactor in twelve files, still runs on people. The gap to operator-grade is not a better model; it is the structure around it: context an agent can trust, boundaries that make its actions safe to run unattended, and a record of what it did.
A ranked shortlist, not everything that could be automated. First, cross-service doc lookup: high frequency, low risk, mostly a context fix. Then repo-wide refactors behind a review gate, then first-pass incident triage with a human deciding. Honest note: one workflow on the list touches money movement, where a confidently wrong agent is the worst outcome. We keep a human firmly in that loop until the cheaper wins earn the trust to revisit it.
Start at assisted on the first two workflows. The cheap proof, a context layer plus doc lookup, has to earn its keep before operator-grade. The point is your developers end up running the agents, so the capability stays after we leave.
Representative sample, not a real client or a guaranteed outcome. If your repo is already tidy and your doc set small, the real readout will say the upside is small. We would rather tell you that than sell you a plan.
You tell us your stack and where AI keeps stalling. If it's not a fit, you'll hear that.
We assess on your actual codebase, in production — not a questionnaire, not a sandbox.
You get a prioritized agent-ops plan you keep, whether or not we work together after.
then: consulting · engagements
Pick a time that works. We'll confirm scope and what we need from your repo before the call.
Tell us your stack and where agents keep stalling. If it's not a fit, you'll hear that.
No commitment. You keep the prioritized plan even if we don't work together.
or email hello@tsukumo.ch