Detect fake receipts, forged PDFs, and manipulated documents before your agent 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, 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.
ENGINE_DEMO
Watch the DocVerify agent analyze a suspected earnings statement — heatmap, verdict, and forensic reasoning — exactly as your AI assistant would see it.
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, and fraud systems.
Expose reusable document verification skills inside agent platforms and orchestration layers.
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.
Document verification for AI agents means checking whether a receipt, PDF, bank statement, employment letter, proof of income, or other uploaded file is authentic before the agent relies on it.
Yes. DocVerify helps agents detect fake receipts, forged PDFs, manipulated screenshots, and altered official-looking files using compression artifact analysis, metadata inspection, font consistency checks, and vision models.
Yes. DocVerify is available as an MCP so models can call document authenticity checks natively inside agent workflows.
Yes. DocVerify is also available as a validation API for product, backend, and enterprise integrations.
Yes. DocVerify Skills are available for agent platforms that support reusable tool and workflow components.
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