A financial model should earn your confidence. It should not depend on whether a workbook happens to look polished, whether a formula was copied one column too far, or whether someone caught a broken assumption in a late-night review.
That is why Convexent treats a model more like a program than a document. We give it structure, run deterministic validation against that structure, and return specific failures an agent or a finance team can investigate. The point is not to claim that a model is infallible. The point is to make the important ways it can be wrong visible, testable, and fixable.
Validation is how the model tells you when not to trust it
A spreadsheet usually gives you a number even when the logic underneath it has drifted. A driver can invert a sign, a schedule can stop tying out, or a copied formula can create a dependency loop. The output still renders. Someone has to notice.
Convexent runs checks as the model is built and updated. A failure is not a generic warning: it identifies the category of issue and the part of the model that needs attention. That gives both people and agents a concrete next step instead of a vague instruction to “review the model.”
A few of the validator categories
Balance sheet balance
Checks that assets, liabilities, and equity reconcile. When they do not, the model should fail loudly instead of carrying an invisible gap forward.
Sign conventions
Catches expressions that reverse the economic meaning of a cost, liability, or cash-flow line before a negative sign turns into a plausible-looking answer.
Circular dependencies
Finds formulas that depend on themselves through another line, so a forecast does not become a circular spreadsheet calculation no one can explain.
Source provenance
Keeps sourced market-data tables tied to the inputs they came from, making it easier to ask where an assumption originated and whether it is still current.
What that looks like in a real model
To make this inspectable, we publish a complete synthetic operating model for Acme Corp. It is not customer data. It is a deliberately invented company, which means we can share the inputs, the model specification, and the exported workbook together rather than asking you to infer quality from a screenshot.
70 / 70
checks passing
Invariants declared inside the model and evaluated on every build. Zero errors, zero failures.
5,610
live Excel formulas
Against 476 literal values — and those sit only on Inputs and the actuals block. Every statement sheet is formula, end to end.
32
quarters modelled
22 quarters of actuals (2021-Q1 to 2026-Q2), then 10 forecast quarters driven by the assumptions and the pipeline.
That reference model contains 70 declared checks: seven assertions evaluated across ten forecast quarters. They are the kinds of invariants a reviewer would otherwise need to reconstruct by hand every time the model changes.
- Balance sheet balances. Assets less liabilities less equity, bounded to ±$1. A model that does not balance fails the build.
- Ending cash stays positive. Cash never goes negative across the forecast — the failure a plan is most often quietly wrong about.
- Revenue positive and plausible. Bounded above as well as below, so a runaway driver is caught rather than admired.
- Blended gross margin at or above target. The margin the plan is built to defend, asserted per quarter rather than checked once by eye.
- Implied fixed-price margin at or above floor. Solving fixed-price COGS to hit a blended target can silently push one segment negative. This catches it.
- Implied fixed-price margin between 0% and 100%. A sanity bound. Cheap to state, and it fails loudly the day an upstream assumption inverts.
- T&M share of revenue within bounds. Holds the revenue mix inside the range the unit economics were derived from.
Confidence is a loop, not a badge
Passing checks does not make a forecast true. It does make the model more accountable: a balance sheet has to balance, cash constraints have to hold, and the assumptions that produce a margin need to remain inside the bounds the plan actually relies on. When a check fails, the model has a precise problem to solve before the output reaches an operating decision.
That is the difference we care about. Instead of treating AI-generated financial work as a black box, we build a feedback loop around it. The model can improve because it has structured evidence about what broke. Your team can review it because the rules are explicit. And a result is easier to trust because it has survived more than a glance at the final number.
Inspect the reference model yourself
The files below are the actual artifacts behind the example above. Open the workbook, follow a formula, examine the input pipeline, or diff the model specification. The goal is not to ask you to take our word for it; it is to give you something checkable.