Every compliance officer, audit director, and internal investigator who's asked to adopt an AI tool is really being asked to take on a specific kind of professional risk. Six months from now, when a regulator, opposing counsel, a board member, or a hostile online commenter asks "did a person actually check this, or did you just trust the machine?" — you need a real answer. Not a good-faith one. A documented one.
Most AI tools built for this kind of work don't have an answer to that question. They have a hope.
That hope has already cost the legal industry a public black eye. A 2024–2025 Stanford RegLab/HAI study tested the leading AI-assisted legal research tools — Lexis+ AI and Westlaw's AI-Assisted Research — against real legal queries, hand-scored by experts. Lexis+ AI hallucinated on about 17% of queries. Westlaw's AI-Assisted Research did worse — about a third, which the authors describe as hallucinating nearly twice as often as the other tools they tested. Read the other way round: Lexis+ AI was accurate 65% of the time, Westlaw 42%. These aren't fringe products. They're the default research tools at large firms, tested on the exact kind of factual, citation-bound work they were built for.
The lesson isn't "AI is bad at this." It's narrower and more useful than that: fluent, confident, well-cited-looking output is not the same thing as output a professional has actually verified. And most AI tools — in legal research, in compliance, in investigations — are built to produce the former and simply assume the latter happens somewhere downstream, in some reviewer's head, off the record.
The part everyone skips
We build software for investigators — the people who turn a pile of documents into a finding that has to survive scrutiny: a peer review, a fraud examination, a board inquiry, a court. When we set out to build the AI-assisted part of that workflow, we kept running into the same design question competitors seem to walk past: what happens at the moment a human is supposed to check the machine's work?
In almost every tool we looked at, the answer is: nothing happens. The AI produces an answer. The answer has citations, maybe a confidence-sounding phrase. The human reads it, or skims it, or doesn't, and clicks "accept." There is no record of which one occurred.
So we built the opposite of that, and it makes the product measurably more annoying to use — on purpose.
The analysis grades itself before you see it. Every finding Inqura produces is independently scored for faithfulness to the evidence and coverage of the question, before it's shown to you. You're not the first line of defense against a made-up citation; the system already flagged it.
Every finding gets a real confidence level, and "insufficient" is an allowed answer. Established, Probable, Possible, Insufficient — computed from the evidence, not softened for user experience. A tool that answers everything with the same fluent confidence is a tool that's optimized for feeling helpful, not for being right.
You cannot sign off on a finding without opening the evidence behind it. This is the part people notice first, usually with mild annoyance. The "Agree" button is disabled until you've actually opened at least one piece of cited evidence. We didn't do this to be difficult. We did it because "I read the AI's summary and it sounded right" is exactly the failure mode that produces a 17-to-33% hallucination rate on tools that assumed a human was checking.
Your judgment goes on the record — agree, disagree, unsure, and why, timestamped. Not as a hidden audit log for us. As a workpaper artifact for you. When someone asks "did a person actually verify this," the honest answer is sitting in the file, not reconstructed from memory eighteen months later.
We call this bundle Review on the Record. It's not a feature we added. It's the structural answer to the question every one of our buyers is actually asking, whether or not they say it out loud.
What we are not claiming
We are not claiming Inqura doesn't make mistakes. It does — it's built on the same underlying language models as everything else in this category, and language models are not oracles. We are not claiming to be "hallucination-free," and we'd treat any AI vendor who makes that claim with the skepticism it deserves. Stanford's researchers didn't test us, and we're not going to borrow the credibility of a study we weren't part of by implying otherwise.
What we're claiming is narrower and, we think, more defensible: we make the AI's failure modes visible instead of invisible, and we gate professional sign-off on the reviewer actually catching them. Based on the product materials we've reviewed as of July 2026, we haven't found another investigation or compliance tool that combines self-scored faithfulness, evidentiary confidence tiers, a forced-engagement review gate, and a timestamped judgment record into one workflow. If that's changed, or if we've missed something, we'd genuinely like to know — email us.
The trade we're asking you to make
Friction, in this one specific place, is the feature. We've made the sales conversation as low-friction as we can — you can watch a five-minute walkthrough before you ever talk to us, and the free trial has no sales call attached to it. But inside the product, at the one moment that matters — the moment a human's professional judgment is supposed to attach to an AI's output — we made it slightly harder to skip that step. That's the whole idea.
If your investigations ever have to survive someone else's scrutiny, we think that trade is worth making.
Inqura was founded by a 25+ year veteran of investigative work, and built as a platform for structured, defensible investigations. Free 14-day trial at inqura.ai.