We wrap your existing scripting surface, manuals and support history into a tested agent layer — deployed inside your product, benchmarked on your own cases, metered so you can sell it, owned by you.
00:00 task: design misses spec limit on the primary check
00:02 → load_project iot_node.prj
00:03 → search_docs "response peak near upper bound"
00:04 → run_solver full sweep
00:41 worst margin: −12% vs limit
00:42 → propose_fix two component values
00:43 → run_solver full sweep
01:19 worst margin: +4% vs limit · verdict: pass
No new UI to sell, no lock-in to our models. The harness runs against whatever LLM you or your customers choose.
Your TCL, Python, COM or batch interface becomes a set of typed, sandboxed tools an agent can call — exposed as an MCP server so any agent can drive your product.
Manuals, app notes, forum posts and ticket history become retrieval and reusable skills. Setup and convergence questions get answered before they reach support.
A golden-case runner scored against your experts' judgments, with regression tracking. Every agent action ends in a verdict you can show a customer.
Most vendors want to sell an AI tier and have no way to price or meter it. Every run the harness executes is metered, attributed and logged — so you can charge for it from the first pilot, before you know what a run is worth to your users.
One engineer embedded with your team, one deliverable per fortnight, and a benchmark you run yourself at the end.
We inventory your scripting interface, file formats and docs, and pick the 20 workflows your users ask about most.
Our agent writes and tests wrappers against a live install of your product. Your engineers review, not write.
Support tickets become labelled cases. Retrieval and skills are tuned until recall clears your bar.
You run the harness on held-out cases. If the numbers hold, we convert to a licence; if not, you keep the code anyway.
Built for a simulation software vendor whose users needed targeted fixes, not another chat window. The agent diagnoses from their solver output and proposes changes their own tool then verifies. The physics changes from vendor to vendor; the three layers do not. Case study on request.
A good fit
Not a fit
No scripting surface yet? We can add a minimal agent-facing one first — enough for the harness to drive your product, not a full public API. That is a shorter, separately scoped engagement that runs before the pilot, not a bolt-on to it.
A 30-minute call: you describe your product's scripting surface and the question your users ask most, we scope the pilot.