Source: Stack Overflow 2025 AI Developer Survey — 65,000+ professional developers surveyed.
You stopped writing the code. Now you manage the agent.
The bottleneck is no longer generation quality. It is the supervision it takes to turn an AI attempt into an accepted engineering result.
The work in between — specifying the task, supplying repository context, re-explaining past decisions, noticing the agent misunderstood, checking every part was finished, catching the unrelated change, running the tests, describing the repair, verifying the repair, deciding it is actually done — is the work we automate.
It compiles. The tests pass. The diff looks fine. It is still wrong.
Valid code generation is not an accepted outcome. These are the failures a human currently has to catch by reading.
Human interventions per accepted task
Accepted is the load-bearing word. A system that stops asking questions while shipping bad results has not improved. We count every human message that corrects, clarifies, redirects or verifies the work — and drive that number down without giving up acceptance.
A control loop around the agent you already use
Assign, observe, inspect, correct, test, inspect again, approve. You run that loop by hand today. We run it for you and hand back a result with evidence attached.
Not a prompt enhancer. An intent compiler.
A compiler does not make its input prettier. It turns a human-friendly representation into a machine-executable one with explicit semantics. The expensive model gets a smaller, clearer, better-grounded unit of work.
The builder does not grade its own work.
An agent that reports completion has told you what it believes, not what is true. Verification is a separate layer with its own model, its own context and its own evidence.
Cheap, fast and not up for debate. We exhaust this before spending a token on judgment.
The genuinely semantic questions a test suite cannot answer for you.
When it fails, it repairs itself first.
A failed criterion is not a reason to interrupt you. It is a reason to write a precise instruction and send it back.
The goal is not zero humans. It is human attention spent only where human judgment adds value.
Your own before and after
Every run is instrumented, so the product can tell you what it actually did for you. Not a dashboard you have to visit — one command.
We do not compete with the model. We orchestrate it.
Every time a base model gets better, this layer gets more valuable: implementations succeed more often, repair loops shrink, verification gets cheaper. Your engineering history should not belong to whichever vendor you happened to pick this year.
We are recruiting 10–20 developers.
You use Claude Code, Cursor or Codex daily on a non-trivial repository. You delegate real multi-file work. You can tell the difference between a task that compiles and a task that is actually done.
We measure your baseline first, then run the same work through the runtime, and show you the delta on your own tasks. Anonymized metrics only, and we publish nothing without your say-so.