Deepify runs your product, pricing, and program decisions against a synthetic market before you commit a dollar. Your competitors find out where their strategy breaks from the market. You'll find out from us.
Every decision moves through the same calibrated pipeline. Each run sharpens the next — which means the engine compounds. Six months in, it knows your market better than any research vendor you've ever hired.
Synthetic segments modelled on real retail banking behaviour — savers, borrowers, switchers, pre-retirement decumulators — each one responding the way your market actually responds. Not a focus group's opinion. The market's behaviour.
powered by Synthetic AudiencePut the pricing change, the feature, the program through the market before the market gets a vote. Vary rates, timing, and competitive response. Find the exact segment and the exact month where it fails — while fixing it costs a meeting, not a quarter.
powered by Liquid GTMEvery run produces a documented challenge trail: assumptions, model behaviour, outcomes. When the risk committee asks who independently challenged this, you slide the pack across the table. Meeting over.
powered by LLM AuditKnow which segments adopt, which ignore, and which walk — before the build is funded. Kill the losers on a screen instead of in the market.
Model the rate change against every cohort and every likely competitor response. Find the break point while the decision is still yours to change.
Walk into model risk review with independent challenge already documented. You're not defending the decision — you're presenting the evidence.
A new high-growth mortgage: sharpened rate, flexible prepayment, built to win volume in a competitive market. Strong concept. Board-ready deck. Every internal review passed it.
The engine found what the reviews couldn't: the volume showed up — but it skewed to rate-chasers, the segment most likely to walk at renewal. In a down-rate path, margin broke below hurdle. One rate-feature rebalance, one re-run: volume held, margin held, and growth tilted toward segments that stay. The failure cost an afternoon. In market, you'd have found it at first renewal — years later, holding a book of thin-margin loans.
Every major banking regulator has landed on the same expectation: models and AI-driven decisions need effective challenge, independent of the teams that built them. Deepify sits outside your development pipeline — which is exactly where that challenge is supposed to come from. Your internal teams can't independently challenge their own work. We can. That's the product.
Scenario runs are structured as documented, repeatable model challenge — mapped to the model risk and AI governance expectations of the major supervisory regimes:
Aligned, as required, with Canadian regulators for financial services, financial services consumers, and insurance. Built to these expectations from day one — not retrofitted after the exam letter arrives.
A dedicated instance per institution, deployed in the jurisdiction you choose. Your data stays in-country, in your instance, under agreements drafted for regulated FI procurement. Your vendor risk team will find nothing to escalate — we wrote the paper for them.
Every conclusion traces to a run your second line can reproduce and examine. No black-box scores. The challenge trail is the deliverable — in whatever format your supervisor expects to see it.
Pick the decision on your calendar this quarter — the pricing change, the program, the feature everyone's already sure about. We'll run it through the engine and show you where it breaks. Either you fix it before the market finds it, or you launch holding independent proof it holds. You win both ways.
Run one scenario →Scoped proof-of-concept under NDA. Days, not quarters.
