The value is not in the model (it is the same one underneath) but in the guardrail, the evidence and the packaging. These are the arguments, strongest to weakest.
Every run produces a cryptographically signed audit trail, tied to a versioned procedure with validated frontmatter. An external evaluator (Common Criteria, national security schemes, fintech or healthtech audits) accepts it as a file annex. A chat session is not reproducible, not signed and references no procedure: the evaluator does not accept it.
A closed action vocabulary validated against a schema (everything else is rejected), protected paths in two layers, and hard budgets for actions, tokens and time. Exceeding them triggers an abort, never an infinite loop. You can ask a generic agent to respect this; TecnicaiT cannot violate it. That is half the guardrail. The other half does not protect: it steers.
Framing the AI does not limit it, it focuses it. More effective: no finding reaches you without being contrasted against the real contents of the file and against the captured screen, and whatever the system could not see is never declared a defect. More efficient: every task runs on the model at its level and the large one only steps in when the guardrail detects the small one is stuck; whatever can be checked without AI is checked without AI; and memory across runs avoids rediscovering the same thing. That is why a validation pass costs cents and does not need an engineer sitting next to it judging every step.
It runs the application the way real users do, through the browser and with roles coordinating (operator detects, command authorizes, admin audits). That catches defects code review never sees: the app works but the documented procedure is not executable, or the handoff between roles breaks.
Every captured screen goes through a triple contrast: code, data and image as independent signals compared per screen, with the verdict cryptographically signed. A code review does not catch that a table total does not add up or that a metric shows an impossible percentage; the image does, and here it becomes evidence.
The value is in validating every release, dozens of cycles, with a memory bank between iterations so it does not re-discover the same thing. It assigns the model to each task tier and reports the real cost per run. "Just ask an AI" does not scale to a catalog of procedures across releases across projects.
The generator analyzes your repository, walks your application authenticated, and proposes a catalog of domain procedures citing the evidence behind each one. Every draft is executed against the live app and rewritten with the feedback from that run before entering the catalog. The onboarding cost is not "document your operations first": it is a review by your team.
The user is a QA lead or an auditor: they open a web app, launch a run (or trigger it from a PR comment) and get an executive report with the findings and their evidence, or a mergeable PR with tests. No engineer needed to prompt and judge the result every session.
The product is measured by accepted PRs over proposed ones. It is a system with accountability, not an assistant handing over suggestions that someone has to review by hand.