AI that summarizes earnings, drafts client notes, or explains transactions has one unforgivable failure: getting the numbers wrong. Financial AI evaluation grades every figure, every claim, and every disclosure against your source data.
We grade outputs. We grade your AI's outputs against your data and compliance rules. We do not provide licensed financial professionals, and our grading does not replace professional review.
Financial AI output evaluation is the strict grading of AI-generated financial content, summaries, client communications, internal analysis drafts, against your source data and compliance rules. Fintech and finance teams need it because a fluent paragraph with one wrong number reads as authoritative right up until someone acts on it. Good looks like every figure traceable to source data, calculations that check out, required disclosures present, and no invented market facts. Plainly: we grade outputs. We are not licensed advisors and our reports are not financial advice.
Financial AI fails at the exact point where precision matters. We grade for the patterns that cost the most.
The most dangerous financial AI error: a figure that is close enough to look right. Revenue of 4.2 million instead of 2.4. A rate quoted one decimal off. We grade every figure in the output against your source data, not just the narrative around it.
Models fill gaps in market knowledge with plausible fiction: a merger that never closed, a rate decision on the wrong date. In a client-facing summary, that fiction becomes your firm's statement. We check factual claims against your approved sources.
Financial content often needs specific disclosures, risk language, or disclaimers. Summarization and rewriting drop them silently. We grade outputs against your required-disclosure list, so a missing line is a flagged failure, not an accident you find later.
There is a line between explaining data and advising action, and models cross it casually. We grade whether outputs stayed on the analysis side of that line per your compliance rules, especially in client-facing content.
You send 20 to 50 outputs plus the source data and compliance rules they should reflect, or we help you write a rubric from them.
Trained reviewers grade every sample against your data and rules, backed by automated checks on figures and disclosures. Wrong numbers are P0s.
Sample-level grades, figure-level error tracking, the failure patterns across the batch, recommended fixes, and a walkthrough call.
Reviewers verify every figure in the output against your source data: the statements, the tables, the market data it was supposed to reflect. Automated checks back them up on totals and percentages. A single wrong number fails the sample no matter how good the prose around it is, because in finance the number is the product. Our severity scale treats a wrong figure as a critical failure, not a typo.
Required risk language and disclaimers do not get partial credit. They are either present and correct or they are a flagged failure. The report lists disclosure gaps separately from accuracy issues, so your compliance team can clear their half of the report without reading ours.
We also check the narrative around the numbers. A correct figure wrapped in a misleading explanation still misleads, and clients act on the explanation as much as the digit. Reviewers grade whether the commentary the AI adds is supported by the data, so a right number with a wrong story does not slip through on the strength of the digits alone. This is where many financial AI systems quietly fail: the table is perfect and the takeaway is wrong. The report treats narrative accuracy as its own graded dimension, separate from figure accuracy, because both have to hold.
No. We grade AI outputs against the data and rules you provide. We do not provide licensed financial professionals, and our reports are not financial advice and do not replace review by qualified professionals. What you get is a documented read on where your system's outputs break your own standards.
Against the source data you provide: the statements, the tables, the market data the output was supposed to reflect. Reviewers verify figures claim by claim, and automated checks back them up on things like totals and percentages. A wrong number fails the sample regardless of how good the surrounding prose is.
Earnings summaries, portfolio commentary drafts, client email drafts, transaction explanations, KYC and onboarding document summaries, internal research notes. If the output makes claims about money and your team can define correct, we can grade it.
Yes. Your compliance requirements become explicit rubric checks: required disclosures, forbidden claims, approved language, advice boundaries. The report shows compliance gaps separately from quality gaps, so your compliance team gets what it needs.
Analyst review is valuable and inconsistent as a measurement system. We give you the systematic layer: every sample graded the same way, figure errors counted, patterns named. Your analysts then spend their time on the judgment calls, not on re-checking the same wrong total.
Hallucination detection asks whether outputs contain invented facts. Financial AI evaluation adds the money-specific layer: figure-level accuracy against source data, disclosure compliance, and advice-boundary checks. For financial content, a right-sounding paragraph with one wrong number is the failure that matters most.
Send 20 to 50 outputs with your source data. We will grade them all.
Not sure where to start? Talk to us and we will point you at the right evaluation.
A fabricated case citation in a brief is not a typo, it is a sanction risk. Legal AI evaluation grades your system's outputs against your sources and standards, citation by citation.
We grade outputs. We grade your AI's outputs against your rubric and source materials. We do not provide lawyers or licensed professionals, and our grading does not replace professional legal review.
The failure modes we grade hardest in finance and legal.
Case law, statutes, or precedents that sound real but do not exist. In legal outputs this is an instant top-severity finding.
Wrong numbers in financial calculations or risk assessments that could mislead investors. We verify every figure against source data.
Advice that sidesteps KYC/AML rules, trading restrictions, or data privacy mandates. We test against frameworks like SEC, GDPR, and MiFID II.
Every case citation, statute, and docket number gets checked for existence and for actually supporting the claim made.
Legal AI output evaluation is the strict grading of AI-generated legal work, drafts, summaries, research memos, contract markups, against your sources and your standards. Legal tech teams need it because the failure mode is uniquely dangerous: a fluent memo with a fabricated citation looks exactly like good work until a judge checks it. Good looks like every citation verified against a real source, no invented holdings, and language that stays inside the system's approved role. Plainly: we grade outputs. We are not lawyers and our reports are not legal advice.
Legal AI fails in ways that look like competence. We grade for the patterns that matter.
The most reported legal AI failure: a real-looking citation to a case that does not exist, or a real case cited for a holding it never made. We verify citations against your source materials and flag every one that does not hold up.
Subtler than invention: the AI cites a real case but upgrades what it said. A passing remark becomes the rule of the case. We grade whether the output's claims match what the source actually supports, not just whether the source is real.
Legal answers are jurisdiction-specific, and models mix them freely. A memo about California procedure citing New York rules reads fine to anyone who is not checking. We grade jurisdiction consistency against the matter's actual venue.
Many legal AI tools are meant to assist, not advise. When the output crosses into direct legal advice without the right framing, that is a product risk. We grade whether outputs stayed inside their approved role.
You send 20 to 50 outputs plus your source materials and standards, or we help you write a rubric from them. Citations get verified against the sources you provide.
Trained reviewers grade every sample against your rubric, backed by automated checks. Fabricated citations and overstated authority are treated as the P0s they are.
Sample-level grades, citation verification results, the failure patterns across the batch, recommended fixes, and a walkthrough call.
Every citation in every sample is checked against the sources you provide. Does it exist. Does it say what the output claims. Does it support the point it is attached to. A real case cited for a holding it never made fails just as hard as an invented one, because in front of a judge the difference does not matter. Our human evaluation process is built for exactly this kind of careful, line-by-line work.
Legal writing constantly calibrates how strongly it states things: holdings versus dicta, majority versus concurrence, binding versus persuasive. We grade whether the output's confidence matches its sources. An output that upgrades a passing remark into the rule of the case is overstating authority, and the report flags it as its own pattern, distinct from outright invention.
We also grade consistency across the batch. If the same clause gets summarized three different ways in three samples, that variance is a pattern worth knowing before opposing counsel finds it. The report flags inconsistent handling of repeated task types so you can tighten the prompt or the template. Consistency matters in legal work because downstream readers assume the system treats like cases alike. When it does not, every output becomes suspect, even the correct ones. A system that is right in five different ways is harder to trust than one that is right the same way every time.
No. We grade AI outputs against the rubric and sources you provide. We are not lawyers, we do not employ licensed attorneys as graders, and our reports are not legal advice and do not replace review by qualified counsel. What you get is a documented, systematic read on where your system's outputs break your own standards.
Against the source materials you provide: your case database, your document set, your research corpus. We check that each citation exists, that it says what the output claims it says, and that it supports the point it is attached to. A real case cited for the wrong holding still fails.
Research memos, brief drafts, contract markup suggestions, clause summaries, deposition summaries, and similar text outputs. If your team can define what a correct output looks like and provide the sources, we can grade against them.
Yes, when you tell us the jurisdiction. The rubric includes a jurisdiction check: the output's rules, procedures, and citations must match the matter's actual venue. Mixed-jurisdiction answers are one of the most common failures we see in legal AI.
Attorney review is essential and expensive. We give you the systematic layer underneath it: every sample graded the same way, citation problems counted, failure patterns named. That lets your attorneys spend their time on judgment calls instead of catching the same fabricated citation repeatedly.
Hallucination detection asks whether outputs contain invented facts. Legal AI evaluation goes further: it checks whether real sources are used correctly, whether authority is overstated, and whether the output stayed in its approved role. For legal work, correct use of real sources matters as much as catching inventions.
Send 20 to 50 outputs with your sources. We will grade them all.
Not sure where to start? Talk to us and we will point you at the right evaluation.