VERITROOPER is an accuracy engine for any AI working over your own data — it measures how accurately the model answers, proves it, and tells you exactly what to fix. It doesn’t replace your AI or change how it serves; it grades it against ground truth and hands back the evidence. You hand it a data set — tax code, safety regulations, drug labeling, financial filings, gaming rules, anything written down — and it generates a question set with verified ground truth, then runs the model two ways: once on realistic retrieval (the baseline) and once with the correct source evidence in front of it (the audit). The gap between them is the accuracy your model is leaving on the table. Failures are routed to specialist diagnostic modules, and every contested verdict is confirmed by an independent cross-vendor verifier that can override it — the model under test never gets the final say on its own answers. The run ends in a plain-English report on what the model got wrong, why, and exactly what to fix to recover it.
On regulated material it does what naive retrieval can’t: when a question depends on a cross-referenced section, the pipeline pulls that referenced section’s text into the evidence — multi-hop resolution the baseline can’t do. For financial filings it parses label/value/period tables and checks every calculation against the source numbers with a built-in financial calculator, so a fabricated figure or a wrong-year value gets caught.
The failure mode it's built to catch is hallucination — when an LLM confidently produces a wrong answer. Models don't crash when they hallucinate; there's no flag, no warning, no error code. They just sound certain about something that isn't true. That's what breaks naked LLM deployment in any setting where the answer actually matters. VERITROOPER catches it across any domain, with any model.
What you get out the other side: a final verified accuracy score against ground truth — with a confidence interval and a clear PASS / CONDITIONAL / FAIL disposition against an acceptance standard you set, not a naked percentage — a per-question categorized list of every failure (with patterns and clusters identified), a per-category and per-data-source accuracy breakdown, a failure-recovery rate (the share of the model’s baseline failures the audit recovered), and concrete engineering recommendations that would close the specific gaps the LLM showed. It all lands as one portable evidence package: role-specific documents plus a machine-readable record, sealed with a cryptographic signature and a trusted timestamp and shipped with a standalone verifier, so a reviewer can prove nothing changed after sign-off. Every verdict is reproducible from timestamped logs — no black box.
Deploying into Europe? One toggle adds the EU AI Act conformity evidence to the same run: declared accuracy and robustness testing (Article 15), a drop-in Annex IV technical-documentation record, recurring accuracy-drift monitoring (Article 72), a dated, signed human-review audit trail (Article 14), and a per-category gap diagnostic (Article 10). VERITROOPER produces the evidence a conformity file relies on — it does not replace the provider's conformity assessment or confer compliance.
Four regulated-domain data sets tested across seven different LLMs, phone-tier 7B to flagship frontier. Measured against the correct evidence with cross-vendor verification, every model — from a $2,000 RTX 4090 running a 7B up to flagship frontier APIs — lands in the 80.8–99.2% band on the same 1,000 IRS Tax Code questions. That's the punchline: this isn't a frontier-only luxury. Even a 7B on a laptop comes within shouting distance of a frontier model when grounding is solved — so the audit shows the gap on your data is mostly recoverable, and pinpoints exactly which failures to fix to close it.
