AI-native back-office software for organizations that live or die by their reporting. It keeps an always-current record of everything you have to prove, so grant applications, audits, and filings become cheap to produce instead of a quarterly fire-drill.
56% to near-certain. In a 30-year simulation of a hard, grant-funded venture, automating the compliance and back-office work was the one lever that moved its odds of surviving from a coin-flip to near-certain. Simulated, pending a first pilot.
Mission-driven and heavily regulated organizations carry an enormous, invisible tax: the work of proving they are compliant. Grant terms, the 990, reimbursement rules, board governance, impact reporting. For a small nonprofit it can run to a third of the budget, and the expensive part is not the filing itself. It is the scramble, every cycle, to re-gather the evidence that each obligation was met. That scramble is where thin organizations quietly run out of money.
Compliance is usually treated as documents produced at a deadline, so the same evidence gets hunted down again and again, under pressure, by the most expensive people in the building. One bad reporting quarter can cost an organization a grant. The cost of all that re-gathering never shows up as a line item. It just shows up as an organization that is permanently behind.
First the honest test, then the software. Before writing a product, I built a 30-year simulation of the hardest case I could find: a grant-heavy, thin-margin nonprofit where compliance overhead is make-or-break. I compared two versions that were identical except for one thing, whether the back-office was run by staff or by the software, so the result isolates that single lever and is not a strawman. The software itself models the organization once, lists every obligation as a specific checkable requirement, defines exactly what evidence proves each one, and runs agents that keep that evidence current from the systems the organization already uses. A grant application or a 990 then becomes a query against an always-current record, produced on demand, rather than assembled from scratch under deadline.
In the simulation, the automated version’s odds of surviving 30 years moved from a 56% coin-flip to near-certain, and it carried the organization through a funding cliff that killed the manual version outright. It pays for itself on overhead alone: run on a single organization, with nothing sold to anyone else, it saves about $144,000 a year, roughly a third of overhead, handed back to the mission. Selling it to other organizations is upside, not the reason it works. This is a simulated result, and I want to be straight about what it rests on: one assumption, that automation cuts the compliance workload by roughly threefold. Proving it with a first real deployment is the next step, not a footnote.
What this shows about working with me
Two things, depending on who is reading. If you run a regulated or grant-funded organization, this is what AI-native software can do for the work that is quietly bleeding you. If you are a founder weighing whether to trust me with your own build, this is how I work: I stress-test the load-bearing assumption before I write the product, I name the one number the whole case rests on, and I would rather show you a thesis I can be proven wrong about than a finished-looking promise.
