Hallucination is the reason legal AI adoption stalled for years, and it's still the first question every serious legal tech founder asks a development partner. Here's exactly how we control for it — not a marketing promise, the actual methodology.
Hallucination isn't a hypothetical risk in legal AI — it has already produced real, public consequences. In 2023, lawyers were sanctioned by a federal court after submitting a brief containing case citations fabricated by an AI tool, a widely reported incident that made 'unauthorized practice of law' and 'AI hallucination' live search concerns rather than abstract ones. Law firms rejected early legal AI tools specifically because hallucination rates were too high for professional use — and it's only as grounding techniques matured that production-grade legal AI became realistic.
That history is why we don't lead with a generic 'we use AI' pitch. We lead with the methodology that keeps outputs grounded, verifiable, and safe to put in front of a client or a court.
The same annotated product screenshot featured on our legal tech MVP page: a self-built RAG-based contract clause extractor that cites its exact source paragraph for every answer, with a confidence score attached. A full walkthrough video is planned — see the production spec at the end of this deck — and will replace this screenshot once it's filmed.
We're not going to put an unearned universal hallucination-rate percentage on this page — any legal AI vendor who quotes one flat number without publishing methodology should be treated skeptically, and we'd rather earn that scrutiny than avoid it. What we do instead: build a benchmark specific to your document types and use case, measure against it before launch, and share the methodology and results directly with you as part of the engagement. Accuracy is use-case-specific; a benchmark that isn't should raise questions, not confidence.
If you're comparing build-vs-buy-vs-outsource for a legal AI product, this methodology is the thing to press every vendor on, including us: ask what's grounded vs. generated, how citations are verified, what the evaluation benchmark actually measures, and where the human review gate sits. If a vendor can't answer those four questions concretely, that's the signal — not the sales deck.
