Seven tests to run on any AI answer before you sign it off
Asking a business question in plain English now takes seconds. Deciding whether you can put your name under the answer takes longer, and most teams have no routine for it. These seven tests do not need a data scientist or a long evaluation. Each takes about ten minutes, and each can be run on any tool, including ours.
Trinetro Labs · Published 27 October 2026 · 4 min read
Start with the standard, not the tool
The usual question is whether an AI tool is usually right. For a draft email, that is enough. For a number that moves money or goes into a filing, the better question is whether you could hand it to an auditor and explain, without improvising, where it came from and why it would come out the same tomorrow. That standard turns a vague worry into seven things you can check. Write down five figures you already trust from your ledger before you begin, because several of the tests need them.
The seven tests
- Repeat. Ask the same question three times, then once in different words, then have a colleague ask it from their own login. Identical answers pass. Figures that drift, with no explanation, are the most common reason a trial should end.
- Trace. Ask to see how an answer was produced. You want plain steps from source to total, naming the tables, filters and definitions used. An explanation you cannot inspect is a claim, not a record.
- Define. Ask what the tool counted as revenue, or as an active customer. A good tool applies one stored definition, shows it, and says who owns it. A tool that quietly picks a meaning is making a finance policy decision for you.
- Scope. Sign in as a limited user and ask for something outside their access, by name and then indirectly. A new member should see nothing until someone grants it, and the limit should hold on every answer, not just in the menu.
- Reconcile. Ask for the five closed figures you already trust. They should tie to the rupee, or the tool should explain the gap with a stated rule. "Close enough" is a fail.
- Doubt. Ask a question with two possible meanings, a period with no year, and a measure the data does not contain. A trustworthy tool asks one precise question, states its assumption, or says it cannot answer.
- Recover. Change a definition, then ask for last quarter's figure as it was reported at the time. Definitions need a visible history, so that a decision made in March cannot rewrite the numbers you reported in February.
How to score what you find
Give each test 0, 1 or 2: zero for a fail, one for a pass with manual effort, two for a clean pass you could show someone else. Set the bar before you start. A reasonable one for any tool that touches money is no zero on repeat, trace, scope or reconcile, and at least 11 points out of 14. When something fails, ask whether it is a setup issue or a fundamental one. A definition not yet entered is setup, and can be fixed this week. A figure that changes between runs, or an answer with no trace, is fundamental, and no amount of configuration will change it.
Where Indian finance data needs extra care
Several quiet errors are specific to the way Indian businesses report. The financial year runs April to March, so Q3 of FY 2026-27 is October to December 2026, not July to September. GST collected is a pass-through and should not be in revenue. Credit notes can be ignored or netted twice. A figure shown in lakhs read as rupees is out by five zeros. Put at least one of these into your ten test questions. These tests are written up in more detail, with a printable scorecard and a 30-minute bake-off you can run on your own data, in our free eBook, Numbers You Can Defend.
Frequently asked questions
Why not just check whether an AI tool is usually accurate?
Usually accurate is a weak standard for numbers that move money or go into filings. What matters is whether you can repeat, explain and defend each figure, which is what the seven tests check.
How long do the seven tests take?
About ten minutes each, so around an hour for all seven on one tool. The eBook sets out a 30-minute bake-off that combines them using ten questions you already know the answers to.
Do these tests only apply to Trinetro?
No. They apply to any AI tool that answers questions about business data, and we encourage you to run them on every tool you are considering, including ours.
What should I do if a tool fails the repeat test?
Treat it as a fundamental failure for any use that involves money. If the same question on the same data gives different figures with no explanation, configuration is unlikely to fix it.
Keep reading
- ChatGPT vs a Deterministic Data Analysis Tool: Which Do You Trust With Your Numbers?
- Data Access Control for Growing Teams: A Practical Checklist
- How to read a cloud incident report: eight questions for finance and data leaders
- 20 Questions a CFO Should Be Able to Ask Their Data Without Waiting for an Analyst