Upload intake
Screen the original file first
Run verification before OCR, agent extraction, reimbursement logic, underwriting, or KYC review starts trusting fields from the upload.
Verify uploaded receipts, bank statements, PDFs, IDs, and payment screenshots with an API that checks for tampering before your workflow trusts the extracted text.
Verification gate
The practical growth wedge for DocVerify is not another parser. It is the decision point before OCR, agents, AP, KYC, or underwriting systems turn uploaded files into trusted data.
Run verification before OCR, agent extraction, reimbursement logic, underwriting, or KYC review starts trusting fields from the upload.
Check compression traces, suspicious rendering, metadata anomalies, synthetic regions, and edits that are easy to miss in a normal preview.
Let low-risk uploads continue automatically, while suspicious files move to a smaller review queue with evidence attached.
Legacy KYC tools rely on basic OCR and template matching. DocVerify uses advanced AI document verification to analyze compression artifacts, metadata anomalies, and font rendering inconsistencies to catch sophisticated fraud that humans and standard OCR miss.
For a full walkthrough of the KYC verification process and how document checks fit in, see our guide to KYC verification.
Want to see AI document verification in action?
Our homepage features the live forensic scanner, scroll-driven explainer, and hero animation—so you can experience how DocVerify surfaces tampering before you integrate the API.
See the live scanner on the homepageDocument fraud detection is not OCR. It is pixel-level and metadata-level analysis to catch edits, synthetic regions, and recompression—before your workflow trusts the text.
DocVerify focuses primarily on image-based compression artifact analysis. Edited, patched, regenerated, or recompressed files often leave subtle traces in the image itself. DocVerify analyzes those patterns to identify hidden manipulation that may not be visible at normal zoom.
Inserted text, regenerated content, or altered regions can behave differently from native content in the way characters render, align, and blend into surrounding document structure. DocVerify checks these consistency signals to identify suspicious regions.
In parallel, DocVerify inspects metadata and editing traces to determine whether a file appears to have been modified by an editor, passed through a synthetic pipeline, or altered after original creation.
On top of forensic signals, DocVerify uses transformer-based vision models to detect image manipulation, document forgery, GenAI document detection signals, and suspicious edits in scans, screenshots, images, and rendered PDFs.
Commercial use cases
Connect the AI document verification category page to the pages already earning search visibility, then route visitors toward the API and product.
Zelle, Cash App, and Venmo screenshot uploads can look convincing while hiding edits, regenerated balances, or fake payment states.
Expense receipts, invoice PDFs, and ERP approval packets need a trust gate before policy checks inherit confidence from the attachment.
Agentic workflows should verify a document before a model summarizes it, approves it, or moves money based on extracted text.
KYC verification software buyers, platform engineers, and AI agent builders all plug the same forensic engine into different workflows.
Digital KYC verification, onboarding, and AML-adjacent checks need more than template OCR. Screen uploads for forgery before decisions.
Read the KYC compliance guideShip the document verification API behind your product: REST, API keys, and predictable JSON for CI/CD and backend fraud checks.
Explore the API pageMCP, Skills, and tool-calling models need a trust layer for receipts and PDFs—fake receipt detection before the LLM acts.
See agent UX on the homepageEvery stage of the KYC verification process relies on uploads—IDs, proof of address, bank statement verification, and proof of income. Digital KYC verification only works if those files are genuine. DocVerify checks the image, not just the extracted text.
Supported inputs include JPEG, PNG, WebP, HEIC, TIFF, BMP, GIF, and PDF. Use the same pipeline whether the file came from a scanner, a phone photo, a screenshot, or a PDF upload.
# Authenticate with an API key OR an OAuth bearer token curl -X POST \ -H "X-API-Key: YOUR_API_KEY" \ -F "file=@passport_scan.jpg" \ -F "models=core_forensics" \ -F "include_heatmap=true" \ https://docverify.app/api/analyze
{
"status": "success",
"forensic_analysis": {
"is_authentic": false,
"confidence_score": 0.08,
"tampered_regions": [
{"type": "text_insertion", "box": [120, 450, 300, 480]}
]
}
}Integrate AI document verification directly into your backend, mobile app, or agent workflow in minutes. The REST API returns structured forensic results for real-time KYC, underwriting, expense, and agent workflows.
Simple REST endpoints with comprehensive documentation.
Zero-retention policies available for sensitive PII data.
Answers below are mirrored in structured data for search engines.
AI document verification is the process of using machine learning and computer vision to determine whether an uploaded file is authentic or has been tampered with. Unlike OCR alone, it analyzes compression artifacts, metadata, font rendering, and pixel-level anomalies for document fraud detection before automated systems act on the content.
You send a document image to DocVerify via HTTPS (multipart upload or JSON with base64). The document verification API returns a forensic authenticity assessment, optional heatmaps of suspicious regions, and metadata signals. Integrate it into onboarding, expense review, or agent pipelines in minutes.
No. OCR reads text from a file, but it does not prove the file is genuine. A manipulated receipt, statement, ID, or PDF can still parse cleanly. DocVerify checks whether the upload itself deserves trust before downstream systems use the extracted data.
Digital KYC verification is identity and document checks performed online—customers upload IDs, proof of address, or bank statements instead of visiting a branch. DocVerify adds a forgery-detection layer on top of those uploads so digital KYC verification is not fooled by edited or AI-generated documents.
Yes. DocVerify combines traditional forensic signals with vision models trained to spot synthetic generation patterns and GenAI-modified regions, alongside metadata and compression analysis.
DocVerify accepts common image formats (JPEG, PNG, WebP, HEIC, TIFF, BMP, GIF) and PDFs. Typical use cases include receipts, bank statements, payslips, IDs, invoices, and onboarding uploads.
Place it immediately after upload and before OCR, approval rules, agent actions, underwriting, KYC review, or reimbursement logic. That keeps automation fast for clean documents while routing suspicious files to review before trust spreads downstream.
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