Anyone can show you a confident number before launch. We do the part almost no one does: after a campaign goes live, we compare what we predicted to what actually happened — click-through, conversions, cost — and show you the gap.
Calibration is how a simulation earns the right to be trusted with your budget.
The trust card scores how closely each prediction matched live results over a window you choose. Switch between 7, 30, and 90 days and watch predicted line up against actual.
Prediction tools are everywhere and accountability for those predictions is nowhere. A score with no track record behind it is just a confident guess in a nicer font — and you're the one spending real money on it.
Most tools forecast a number, you spend against it, and no one ever circles back to ask whether it was right.
A precise-looking estimate feels trustworthy even when it's systematically wrong in the same direction every time.
If you can't measure accuracy, you can't tell whether a fancier (and pricier) model is actually any better.
Every workspace gets a living trust card that scores how closely our simulations matched live results across your own campaigns — not a vendor case study, your account.
CTR, conversion rate, and CPA forecasts laid side by side with the real outcome after the campaign ran.
The gap rolls up into one calibration score, so you can see at a glance how much to trust the next prediction.
Drill into any individual simulation to see exactly which concepts it called right and where it drifted.
When a segment's predictions run hot, calibration can automatically pull future estimates toward reality.
Compare LLM presets head-to-head on calibration quality and pick the one that's actually most accurate for you.
Every launched campaign feeds back in, so the simulator's accuracy on your business compounds with use.
Approve before spend, set hard caps, and audit every agent decision.
Learn more →Connect read-only and get a scored audit of your Google Ads account in minutes.
Learn more →Reverse-engineer your product into a high-intent semantic graph.
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