Anyone can claim accuracy. We make ours reproducible. Parkvara tests on data we built ourselves — seeded with known errors — so the proof can be re-run by anyone, and no real client's numbers are ever on display.
A full synthetic data book across 137 fictional organisations — realistic enough to test against, owned entirely by us, safe to show anyone.
We plant a fixed set of errors — duplicates, mismatches, out-of-range charges — so "did it catch them?" has a yes/no answer, not an opinion.
The query engine is scored against a fixed set of questions with known-correct answers. Every release is graded the same way.
Every figure is reconciled to its source by a validator that replays the tool's own logic. Nothing ships with a mismatch.
Our dashboard rule: from opening a tool to a usable answer in under two seconds. If it's slower, it isn't finished.
Every build is grepped for network calls before it ships. Public demos run only on synthetic data — never a real client's.
Synthetic data is generated from a fixed seed, and a Python validator replicates each tool's logic — so any result can be reproduced from scratch.
Cloud AI generators are fast and fluent, but they ask you to upload your data and trust a number you can't trace. For a commercial team signing off on real figures, "confident" isn't the bar — "checkable" is. That's the gap Parkvara is built for.
Bring a sample report to a demo and we'll reconcile it in front of you — on your data, with the workings shown.