Deepify pressure-tests the decision before the money goes out — how each segment responds, who you're really up against, the economics the plan has to clear, and the exact conditions that break it. Same day, with the evidence attached.
Type the move and the engine is inferred. Prefix GTM, DEEP, SHELF, WINDTUNNEL, AUDIT or PLAN to force the route. Attach a document or a photograph and that run reads it as your evidence. Paste an evidence hash to verify one.
Six engines · live in production · evidence you can hand to model risk
The gap is tempo. The speed of the decision has outrun the speed of the evidence, and tooling alone does not close that.
One command bar, one evidence standard, one briefing format. Each engine widens the set of decisions that run through Deepify, and every run deepens the ledger underneath. Finish one and the platform offers the next: a market map leads to a shelf test, a shelf test to a stress test.
Segment-by-segment response to a launch, price change or feature, before you commit the spend. Every finding tagged measured or analogue.
Measured accuracy against your own published disclosures, assistant by assistant, with a 95% confidence interval and every answer verbatim.
Live offers, program economics, documented results, rival messaging and the local rules, in any jurisdiction. Claim themes scored crowded, contested or open.
What each feature is worth, what they'd pay for it, and the configuration that wins. The shelf test you can run before the shelf exists.
Your own assumptions played out 10,000 ways. How often it holds, how far each number can move before it doubles your failure rate, and the exact pair that breaks it.
Someone else's findings appraised for credibility, re-verified against your live pages, and sequenced into work your team can run.
Four finished briefings, exactly as the platform renders them — the verdict on page one, the working behind it, the appendix underneath. The data in these four is illustrative and labelled that way throughout. The structure, the arithmetic and the evidence chrome are the real ones.
Four components scored against a live market read, each with a named competitor set and a stated falsifier.
Open briefing →Measured accuracy with a Wilson 95% interval, per-intent breakdown, factual drift, and every response verbatim.
Open briefing →Tornado sensitivities, breakpoint rules, pair lift and headroom — from a seeded simulation anyone can reproduce.
Open briefing →Part-worth utilities, attribute importance, willingness to pay and preference share across modelled segments.
Open briefing →Seeded Monte Carlo, 10,000 draws over a calibrated first-order surrogate with one declared pairwise interaction. Sensitivity is the Pearson correlation of each sampled parameter against program ROI. Breakpoint rules were induced by threshold search at a minimum 8% support and ranked by failure lift. Headroom on match rate is 0.6 points; the next-tightest parameter has 2.5.
The tier wins on engagement and primary-account share, and the early-pay feature should lead the story. The match rate is the one number that decides whether the economics hold: at 4.4% the failure rate doubles, and the plan currently assumes 3.8%. Cap it in the product rules, not in the forecast.
Management proposes launching the tier. Independent testing supports the case and identifies a single failure mode: a match rate above 4% moves the program outside its return threshold. The recommendation is approval conditional on that cap being written into the product rules. The test is reproducible and its evidence record is verifiable outside Deepify.
One run, three registers. Every briefing carries all three, plus print-to-PDF and a self-contained HTML export that keeps its own navigation, its embedded run record and its evidence hash when it leaves your building.
Attach up to ten files to any run and Deepify reads them as your evidence: PDF, Word, PowerPoint, Excel, text or an image. Say what you want done with them in the same line.
Every upload is screened before anything leaves the platform. A document carrying a validated national identifier or a payment card is refused outright. The check reports counts, never the matched value.
Deepify holds no client transaction, customer or account data, and nothing is installed in your environment.
Say what you want watched in one line. Deepify finds the pages those companies publish themselves, records what they say today, re-checks them on your schedule, and emails you only when something has moved. Daily, weekly, monthly, or only when you ask.
Every number a client acts on is produced by deterministic code, never by a model asked to sound confident. That is the difference between something you can show a risk committee and something you can't.
One line. “Build a loyalty program to lift direct-investing unit sales in fast-starter segments.” No brief, no kickoff, no scoping call.
Live market read across parallel browsing models, the real competitor set, published terms and the local rules — every source opened and checked.
Seeded simulation, regression, confidence intervals. Same inputs, byte-identical results, every time.
What to do, what it has to clear, and the conditions that break it — with the working shown and the evidence attached.
Your team keeps the judgment. Deepify removes the two weeks of assembly that used to sit in front of it.
Tested: a loyalty program built to drive increased unit sales in direct investing across key fast-starter segments. Four components scored against live market reads and named analogues, each finding tagged MEASURED or ANALOGUE so nobody mistakes an inference for a measurement.
Every major banking regulator has landed on the same expectation: model-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.
Simulations are seeded. The same inputs return byte-identical results, so your second line can reproduce any figure exactly and get the same answer.
Every cited link is HTTP-checked in code and labelled live, blocked or dead. Each run is hash-stamped to a ledger before the document exists. Measured accuracy carries a Wilson 95% interval; what wasn't measured is reported as unmeasured, never as zero.
A dedicated instance per institution, deployed in the jurisdiction you choose, with tenant identity taken from a verified token and isolation enforced server-side. Authentication fails closed.
Every briefing carries a document record with an evidence hash, written to our ledger the moment the run executed and before anyone saw the result. Paste one here. The check runs with no account and returns whether the record exists and when, never its contents.
Opens the public check on platform.deepify.cloud. Nothing is sent from this page.
Directional work is labelled directional. Modelled respondents are described as modelled and never presented as human. Where a figure couldn't be sourced, the output says so.
Six engines feed one ledger. What was asked, what came back, and what happened next — held server-side against your workspace, so it survives the browser, the laptop and the person who ran it.
Every run writes itself to the ledger as it executes, with its figures, its verdict and a tamper-evident hash. Six months later the question “what did we know when we decided?” has an answer.
When a decision lands, your team reports what actually happened. That is what turns a set of briefings into a track record, and a projection engine into a calibrated one.
Hold rate carries a Wilson 95% interval at the same weight as the number itself, so a perfect score built on a handful of decisions cannot be read as a track record. Unreported outcomes stay unknown and are never counted as failures.
Pick the one on your calendar this quarter — the repricing, the program, the feature everyone is already sure about. We'll run it and show you where it breaks. Either you fix it before the market finds it, or you launch holding independent proof that it holds.
Scoped proof-of-concept under NDA. Days, not quarters.