The leading AI document verification API and KYC verification software to detect fake receipts, forged PDFs, and manipulated documents before your system acts.
DocVerify helps AI agents verify whether a document is authentic before they trust it. Receipts, payslips, employment letters, bank statements, proof of income, invoices, screenshots, and PDFs can be hand-edited, recompressed, or GenAI-modified while still looking legitimate.
LLMs and OCR engines parse text at face value. If a receipt says $9,420.00, they believe it. Zero pixel inspection occurs.
DocVerify locks onto the mathematically suspicious total. The digits exhibit anti-aliasing patterns inconsistent with the surrounding font — a telltale sign of post-production editing.
As we zoom past the character boundary, the smooth typography dissolves into raw raster data. Each cell is a single pixel with an RGB color value captured from the receipt surface.
R,G,B triplets reveal the true color of each pixel. Normal receipt pixels show uniform paper tones (230+). The highlighted anomaly region shows shifted warmth — evidence of re-rendered glyphs.
We re-compress at JPEG Q95 and measure the delta. Authentic regions: Δ2–18. The anomaly cluster: Δ180–255. This 10× variance is the mathematical fingerprint of tampering.
Each pixel's height now represents its compression error score. The anomaly region literally rises above the baseline — a 3D topographic map of manipulation that no human eye could detect.
Three independent forensic signals converge: compression delta variance, font edge anti-alias mismatch, and DCT block boundary shift. Mathematical certainty: 99.8%.
LLMs and OCR engines parse text at face value. If a receipt says $9,420.00, they believe it. Zero pixel inspection occurs.
DocVerify locks onto the mathematically suspicious total. The digits exhibit anti-aliasing patterns inconsistent with the surrounding font — a telltale sign of post-production editing.
As we zoom past the character boundary, the smooth typography dissolves into raw raster data. Each cell is a single pixel with an RGB color value captured from the receipt surface.
R,G,B triplets reveal the true color of each pixel. Normal receipt pixels show uniform paper tones (230+). The highlighted anomaly region shows shifted warmth — evidence of re-rendered glyphs.
We re-compress at JPEG Q95 and measure the delta. Authentic regions: Δ2–18. The anomaly cluster: Δ180–255. This 10× variance is the mathematical fingerprint of tampering.
Each pixel's height now represents its compression error score. The anomaly region literally rises above the baseline — a 3D topographic map of manipulation that no human eye could detect.
Three independent forensic signals converge: compression delta variance, font edge anti-alias mismatch, and DCT block boundary shift. Mathematical certainty: 99.8%.
Document Rejected
Confidence: 99.8% • 3 independent signals
Agents can read documents.
DocVerify helps them decide whether to trust them.
AI agents increasingly review uploaded receipts, onboarding documents, digital KYC verification files, proof of income, bank statements, invoices, claims files, and employment records. But document parsing is not document trust.
A fake receipt can still produce clean OCR.
A forged PDF can still look official.
A manipulated bank statement can still be readable by a model.
DocVerify gives agents a dedicated document authenticity check before they approve, reimburse, verify, or escalate. This helps reduce fraud risk in expense review, compliance workflows, onboarding, underwriting, and claims automation.
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.
DocVerify is built natively for AI agents and tool-calling models.
Let models natively call document verification inside agent workflows.
Integrate document authenticity checks into apps, products, backends, fraud systems, and digital KYC verification flows.
Expose reusable document verification skills inside agent platforms and orchestration layers.
Describe your business validation logic in plain English. Deploy an autonomous verification agent as a REST API in seconds — forgery detection, policy checks, and structured results without standing up infrastructure.
Describe verification requirements in plain English.
Neural forensics and Pixel Guard analysis detect tampering before your LLM sees the text.
The Intelligence Engine cross-references, calculates, and maps verified data.
Provisioned as a REST API endpoint and A2A tool automatically.
Start free, then scale with one-time request packs or auto-replenishing subscriptions.
10 scans / month
$0.00
Sign up and verify 10 documents per month — no credit card required.
500 scans / month
$40.00
Monthly capacity for consistent document review with a lower blended cost.
2,500 scans / month
$120.00
The cleanest option for teams that need reliable headroom without manual top-ups.
50,000 scans / month
$1500.00
Designed for enterprise-grade throughput, automation pipelines, and large review queues.
AI document verification is the process of using machine learning and computer vision to determine whether an uploaded document — such as a receipt, bank statement, pay stub, or ID — is authentic or has been tampered with. Unlike OCR, which only reads what a document says, AI document verification analyzes compression artifacts, metadata, font rendering, and pixel-level anomalies to detect forgery before any automated action is taken.
KYC (Know Your Customer) verification is the process businesses use to confirm a customer's identity during onboarding. Digital KYC verification typically involves uploading identity documents such as passports, utility bills, or bank statements. DocVerify acts as a KYC verification software layer that screens those uploaded documents for forgery and manipulation before they are accepted — catching AI-generated IDs, edited bank statements, and fabricated proof-of-address documents.
Yes. DocVerify is available as a REST document verification API. Send a file via a POST request and receive a forensic authenticity score, tampered region heatmap, and metadata analysis in response. The document verification API is used in KYC onboarding flows, expense automation, lending underwriting, and AI agent pipelines.
Yes. DocVerify uses transformer-based vision models trained to detect synthetic generation patterns, pixel manipulation, and the subtle artifacts left by generative AI tools when they produce or modify document images.
Yes. DocVerify is available as an MCP so models can call AI document verification natively inside agent workflows, without any manual integration steps.
DocVerify supports JPEG, PNG, WebP, HEIC, TIFF, BMP, and GIF. Common use cases include receipt verification, bank statement fraud detection, pay stub authentication, ID document screening, invoice forgery detection, and proof-of-address validation. PDFs should be exported as images before submission.
Yes. DocVerify Skills are available for agent platforms that support reusable tool and workflow components, allowing teams to expose document verification as a callable skill inside their orchestration layer.
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