Deepify reads the live market and the competitive claim set, computes the arithmetic in code, and returns a board-ready briefing the same day, with every source opened and checked and a tamper-evident record of the run.
The separation in steps 2 and 3 is the architectural choice the category has not made. Language models assemble and structure evidence. They do not produce the numbers a client acts on.
Simulations are seeded, so the same inputs return byte-identical results. A colleague re-running your numbers sees exactly what you saw.
Every cited URL is opened and HTTP-checked at run time and labelled live, blocked or dead. "The sources resolve" is a network fact, never a model's claim about itself.
Measured accuracy is reported with a Wilson 95% confidence interval. Where a figure could not be sourced, the output says so. Unmeasured is reported as unmeasured.
Each run writes a tamper-evident hash to a ledger before the document exists, verifiable at a public endpoint by someone who does not work for Deepify.
| Question | Answer |
|---|---|
| What gets installed | Nothing. Browser access with enterprise sign-on. No connector, no pipeline, no agent, no change to your stack. |
| What data you provide | The decision itself, and any documents your team chooses to attach to a run. Deepify holds no copy of your transaction, customer or loyalty data, because it never receives any. |
| Where it runs | Production in AWS Canada (ca-central-1). The stack is region-portable and deploys where your examiners require. |
| Tenant isolation | Per-institution workspaces enforced server-side, fail-closed authentication, per-tenant branding and access control. |
| Upload screening | Documents are screened before processing. A file carrying personal identifiers is refused, never quietly ingested. |
| Time to first run | Same day. A 30-day evaluation consumes no engineering time on your side. |
Deepify uses external language-model providers for the assembly step. This is intrinsic to the product and every provider is named in the vendor register, which is available under NDA alongside the policy set, supervisory self-assessment and data-flow map.
Data processing agreements and zero-retention terms are under execution and are not all countersigned today. If your standard requires them executed before an evaluation begins, say so early and we will sequence around it.
Deepify is a member of the NVIDIA Inception programme and an AWS Partner. There is no GPU training, DGX deployment or NIM integration in production, and we claim none.
Projection work is directional by design and labelled that way. Modelled respondents are described as modelled and never presented as human. Deepify is at design-partner stage, so any run shown to you is a demonstration from public information unless it was commissioned by your organisation.