Uncertainty, surfaced
Unsure results go to a person.
In developmentDetect forged documents, fake IDs and deepfakes before you fund a loan or pay a claim. Built for mortgage, HELOC and insurance teams.
curl https://demo.trustable.ai/v1/documents \
-H "Authorization: Bearer $TRUSTABLE_API_KEY" \
-F file=@statement.pdfimport os, requests
key = os.environ["TRUSTABLE_API_KEY"]
r = requests.post("https://demo.trustable.ai/v1/documents",
headers={"Authorization": f"Bearer {key}"},
files={"file": open("statement.pdf", "rb")})
print(r.json()["verdict"])import fs from "node:fs";
const url = "https://demo.trustable.ai/v1/documents";
const body = new FormData();
body.append("file", await fs.openAsBlob("statement.pdf"));
const res = await fetch(url, { method: "POST", body, headers: {
Authorization: `Bearer ${process.env.TRUSTABLE_API_KEY}` } });
console.log((await res.json()).verdict);Let AI agents run Trustable checks through MCP.
In developmentAdd Trustable document checks to this app. For each uploaded bank statement, pay stub, W-2 or ID, POST the file to https://demo.trustable.ai/v1/documents as the multipart field "file", with the header "Authorization: Bearer $TRUSTABLE_API_KEY". Show the returned verdict and verdict_reason to a reviewer. Never reject a file on the verdict alone: a person decides.What you get
Every finding points to the field, says how it was changed, and how strong the signal is.
J. Example · Checking ···· 4821
| Date | Description | Amount | Balance |
|---|---|---|---|
| Sep 1 | Opening balance | 3,418.60 | |
| Sep 5 | Payroll · Lakeside Logistics | +2,940.00 | 6,358.60 |
| Sep 8 | Rent · autopay | −1,850.00 | 4,508.60 |
| Sep 12 | Card purchases | −1,275.33 | 3,233.27 |
| Sep 19 | Payroll · Lakeside Logistics | +2,940.00 | 6,173.27 |
| Sep 30 | Closing balance | 14,173.27 |
Closing balance re-rendered
Digits drawn over the original. The transactions add up to 6,173.27, not 14,173.27.
Deposit line overlaid
The Sep 19 text layer doesn’t match the rendered page. Font and spacing differ from the rest of the column.
Metadata gap
Last saved by a PDF editor three days after the statement date. Not proof on its own.
Each finding says where it is, how the file was changed and how strong the signal is. Your team decides.
Mission
The trust layer we’re building
01
Spot fakes.
02
Confirm source and identity.
03
Set agent limits.
04
Keep the evidence.
05
Meet AI rules.
06
Prove who owns it.
Detection
Forged statements, pay stubs and IDs.
AI-generated or edited photos.
Face swaps and other deepfakes.
Where a file came from.
What was flagged, and where.
A trust check before agents act. In development.
[ How it checks ]
Edits to text, pixels and layers.
Marks AI generators leave.
Camera data and file history.
Every flag, shown on the file.
What we’re building around every check.
Unsure results go to a person.
In developmentIncome compared across pay stubs, W-2s and deposits.
In developmentAgents check a file before they act.
In developmentPass, review or hold.
In developmentAll the evidence in one place.
In developmentWhere trust breaks
Altered statements and pay stubs inflate income.
Document
Edited income documents slip past lighter checks.
Document
Fake damage photos and invoices inflate payouts.
Image · Document
Synthetic selfies and edited IDs.
ID · Video
Edited receipts and screenshots.
Image · Document
Agents acting on fake files.
Any file
46%
of Fannie Mae’s 2024 misrepresentation findings involved income. National Mortgage News
$5.38
cost to US lenders for every $1 lost to fraud. LexisNexis, 2025
44%
of North American financial institutions rely mainly on manual fraud processes. LexisNexis, 2025
We agree how files are handled before you share any.
Every flag comes with its evidence.
Security
Host in the US, EU or APAC, deploy in your own VPC or on-prem, or keep files in your cloud while Trustable runs the checks.
Independently audited controls, with personal data handled under GDPR.
FAQ
Yes. Trustable flags edited text, font and layout mismatches, metadata gaps and totals that don’t add up, and marks where each one is on the page.
Each pay stub and W-2 is checked for edited figures, font mismatches and rebuilt templates. Cross-checking income across a whole loan file is in development.
HELOCs often close with lighter checks than a first mortgage, so an edited pay stub or statement is easier to miss.
Yes. It looks for edits, AI-generation traces and missing camera data in damage photos, and checks repair invoices and receipts for edits.
No. Messaging apps, screenshots and re-saves often strip it, so it’s one signal among several, never proof on its own.
Yes. Trustable is SOC 2 Type II audited and handles personal data under GDPR. It runs in our cloud with US, EU or APAC hosting, in your own VPC or on-prem.
No. No AI is 100% accurate. Results can be wrong in either direction, flagging a genuine file or missing a fake, so a person should review each flag before acting on it.
Trustable is a product of Reality Inc., based in San Francisco and Toronto. About the company.
Next step
Twenty minutes and one sample file. You see every finding with its evidence.