PLATFORM DECISIONS
Keep the platform. Add the proof.
You bought an AI platform, or you're deciding between them, and the honest pattern is the same everywhere: out of the box they are generically good. Fine at search, fuzzy on your specifics, and adoption stalls. We are stack-agnostic: whatever passes the evals, we keep and make dependable. What we add is the layer no platform ships.
THE PATTERN
Generically good, specifically stuck.
Platforms demo beautifully on general questions and blur on the ones your business actually runs on: your entities, your quality bar, your edge cases, your systems of record. People try it, get a fuzzy answer about their own work, and quietly go back to the old way. The license renews; the adoption never arrives. The industry numbers explain it: in a 2025 survey of more than 1,300 agent-engineering teams, 89 percent had observability in place while only 52 percent ran evals. Everyone watches. Few measure.
STACK-AGNOSTIC BY DESIGN
We fix it where it lives.
If your data lives in a platform that passes the evals, we keep it and build on it. We structure your domain data where it already sits, in your warehouse, in your tools, or in a knowledge graph when you need one. Everything we build is yours, engineered for handover, with no platform to move into and nothing to migrate out of later.
WHAT WE ADD
The layer no platform ships.
Custom evals that define what a good answer means for your work, and the red-teaming that finds where the platform breaks on your risk surface. Domain data made legible, so answers about your business stop being fuzzy. Governed write-back, so the platform acts in your systems of record and every action is auditable. Training inside the real work, so adoption is measured in changed workflows. The platform you bought becomes a system your people can rely on, and can prove.
Still deciding between platforms? The diagnostic doubles as a bake-off: we define your standard once and test every candidate against it, so you choose with evidence about your own work.
COMMON QUESTIONS
Asked by platform owners, answered plainly.
Should we buy Glean, Copilot, or Cortex at all?
Run the bake-off before the contract: define your standard once, test every candidate against it, and choose on evidence about your own work. The diagnostic produces exactly that, at a fraction of the cost of a year of the wrong platform's seats.
We already bought one and nobody uses it. Is it salvageable?
Usually. Platforms stall at generically good, and adoption arrives when answers about your specific work become dependable. We add the layer that gets them there: custom evals, legible domain data, governed write-back, and training inside real workflows, all on top of the platform you already own.
How are you different from LangSmith, Arize, and the other eval tools?
They are excellent instruments; we are the practice that plays them. We operate whichever eval and observability stack you already own, or stand one up in your cloud. There is no license on this page and nothing to migrate into.
Will this lock us into you?
Everything we build is yours and runs in your stack, engineered for handover as your team gets fluent. The durable layer stays only for as long as you want us watching.
LET'S TALK
Bring us the hard problem.
We'll bring the team that ships.