This is what "evidence attached" actually means. Not a report we write at the end — four artifacts that travel with the work, gating each release before it reaches your users.
Answers are tied to real, cited sources — not the model's imagination. You can trace every claim back to where it came from.
An eval suite scores each release against a threshold. If it doesn't clear the bar, it doesn't ship — no exceptions on a good feeling.
Policy and safety checks run on every output. The system refuses what it shouldn't do, and that refusal is logged.
When confidence is low or stakes are high, a person is in the loop before anything reaches a user — by design, not as a fallback.
Selected engagements across AI-native delivery, the foundations beneath it, and independent assurance.
A grounded LLM workflow with human review in the loop — shipped to production with its evidence attached.
Legacy PHP/JS refactored into a clean, AI-ready codebase, with the data tidied behind it.
Inherited code, infrastructure, and vendors audited and stabilized before any new build began.
The release record should let business, technology, and risk leaders see the same system from outcome through operation.
The Portfolio goes deep on a handful of engagements — discovery, written approvals, controlled rollout, and structured handover — with the delivery structure laid out end to end.
Bring the workflow and the questions your buying committee must answer. We will frame a delivery path with visible evidence and production controls.