Carpe Vision
Carrier-Submitted Photos Are No Longer a Source of Truth
Carpe Vision is AI-powered image and document forensics for insurance claims. Eleven detection engines, claim-aware context, and courtroom-ready visual evidence, in under a minute.
Forensic Engines
Run in parallel on every image
Verdict Time
Single image, fast enough for FNOL
AI-Detection Engines
Built for the synthetic-media era
Visual Evidence
Heatmaps and overlays on every flag
One Verdict, Every Engine, on the Same Screen
The photos that arrive with a claim are no longer reliable on their own. A claimant can alter damage, change a date stamp, or generate a synthetic injury photo in seconds, and most legacy fraud tools only look at pixels in isolation. The loss leakage is hidden inside the evidence carriers already trust.
Carpe Vision reconciles every signal on a claim photo to one screen. Eleven forensic engines run in parallel on the same image, three of them dedicated to AI-generated and AI-edited content. The claim photo and the claim facts sit side by side. The verdict, with its confidence score, fires at the top. Per-engine findings explain why, and a manipulation heatmap shows exactly which regions of the image have been edited.
That single screen is what a claims handler, an SIU investigator, or defense counsel attaches to a file. No tab-switching across point tools, no separate forensics report to interpret, and no waiting on a manual review queue.
How Carpe Vision Works
Eleven forensic engines run in parallel on every image, then fuse into a single verdict. Hover any callout to read what that engine is looking at on a real claim photo.
Pixel manipulation analysis
Clone-stamp, splice, and copy-move detection on the damage zone.
Compression artifact analysis
JPEG quantization mismatches around edited regions like the windshield.
Lighting and shadow consistency
Highlight direction on the hood vs. the scene sun angle.
Reverse-image matching
Has this photo, or any region of it, appeared online before?
AI-generation and AI-edit detection
Three engines tuned to synthetic content, including selective AI edits.
Metadata and provenance forensics
EXIF GPS, capture date, device, software, C2PA, against the reported claim.
Pixel manipulation analysis
Clone-stamp, splice, and copy-move detection on the damage zone.
Compression artifact analysis
JPEG quantization mismatches around edited regions like the windshield.
Lighting and shadow consistency
Highlight direction on the hood vs. the scene sun angle.
Reverse-image matching
Has this photo, or any region of it, appeared online before?
AI-generation and AI-edit detection
Three engines tuned to synthetic content, including selective AI edits.
Metadata and provenance forensics
EXIF GPS, capture date, device, software, C2PA, against the reported claim.
A Single Verdict, Backed by Eleven Engines
Pixel manipulation, copy-move, splicing, compression, lighting and shadow, metadata integrity, reverse-image matching, synthetic-media detection, AI-edit detection, document forensics, and provenance signals. One verdict, one confidence score, per-engine evidence underneath.
Claim-Aware Context
Hidden EXIF (GPS, capture date, device, software) is checked against the facts of the claim. A photo captured 200 miles from the reported loss, or three weeks before the reported date, is a fraud signal most tools never see.
Built for the AI Fraud Era
Three of the eleven engines target AI-generated and AI-edited imagery, including the emerging real-photo with selective AI edits category that legacy fraud tools cannot catch.
Where Carpe Vision Earns Its Keep
Vision is built for the loss types where image fraud is hardest to spot manually and most expensive to miss.
Commercial Auto and Heavy Trucking
Staged-collision photos, reused damage shots across multiple claims, and AI-edited vehicle damage are some of the fastest-growing fraud patterns in commercial auto. Vision flags reused images, manipulated regions, and metadata that conflicts with the reported loss location before reserves are set.
Commercial Property
Property loss photos are now routinely altered to expand damage areas, fabricate water lines, or insert synthetic debris. Vision returns pixel-level heatmaps that show exactly which regions of an image have been edited, and compares EXIF location and capture date against the reported event.
Workers Compensation
Comp claims rely on photo evidence of injuries, work environments, and medical equipment. Vision flags reused stock photos, AI-generated injury imagery, and documents that have been digitally altered after capture, with chain-of-custody evidence claims handlers can attach to a file.
DBA and Subcontractor Receipts
Fake receipts, doctored invoices, and overseas subcontractor documents are a known leakage vector. Vision evaluates document layout, font rendering, signature placement, and pixel-level edit traces to surface tampered paperwork before payments go out the door.
Purpose-Built for Insurance Teams
Plug Carpe Vision into the FNOL pipeline, SIU referral workflow, or batch claims review. Vision can score thousands of images at a time through the API, or one image at a time inside an investigator workflow, giving claims teams a faster path to confident, defensible decisions on every photo and document.
Testimonials
What Carriers Say About Carpe
“By integrating new data elements from Carpe Data into our insurance programs for small businesses, we can better understand the challenges faced by business owners and identify options to address their commercial insurance needs.”
Sharon Fernandez · Farmers Insurance
President of Business Insurance
“Carpe Data is the latest addition to Zurich’s ongoing innovation programs focused on transforming the future of insurance…[Carpe Data] supports Zurich’s ongoing efforts to improve the claimant experience by expediting low-risk claims, and by providing new insights to help our people make informed, accurate, and timely decisions.”
Scott Clayton · Zurich Insurance Company Ltd
Head of Claims Fraud, UK
“Carpe Data has been a great partner in helping us strengthen the claims handling process…Online Injury Alerts helps [us] continue to meet our goals of increased efficiency, reduced potential fraud and improved customer experiences.”
Douglas L. Kratzer · The Hanover Insurance Group
Vice President, Claims
Common Questions About Carpe Vision
What is Carpe Vision?+
Carpe Vision is an AI-powered image and document forensics product for insurance claims. It evaluates any photo or document a claim depends on, returns a single verdict (Clear, Review, High Risk, or Critical) with a confidence score, and produces visual evidence (heatmaps and per-engine findings) that claims handlers and SIU investigators can attach to a file.
Can Carpe Vision detect deepfakes and AI-generated images?+
Yes. Three of the eleven forensic engines are dedicated to AI-generated and AI-manipulated imagery, including the harder "real photo with selective AI edits" pattern that most fraud tools miss. Each engine is retrained continuously against the current generation of synthetic-media tools.
How does Carpe Vision use claim context?+
Vision compares hidden image metadata (GPS coordinates, capture timestamp, capture device, editing software) against the facts the claimant provided (reported date, reported location, claimant identity). Mismatched GPS, dates, or devices are some of the strongest fraud signals available, and Vision surfaces them as structured flags with the source evidence attached.
How fast is Carpe Vision?+
A single image returns a verdict in under one minute. Batch and API workflows are designed for the volume carriers need, from hundreds of images at FNOL through hundreds of thousands of images for portfolio review.
How does Carpe Vision fit with the rest of the Carpe Data suite?+
Vision can stand alone for any carrier, MGA, or platform that wants image and document forensics, or it can plug into Carpe Case Management for an end-to-end claims fraud workflow alongside Online Injury Alerts, Investigative Reports, and native open-source intelligence.
Why is Carpe Vision necessary for carriers?+
Generative AI has collapsed the cost of producing convincing fake imagery. A claimant with a phone can now insert damage that never happened, alter a date stamp, fabricate a receipt, or generate a synthetic injury photo in seconds, and most legacy fraud tools only look at pixels in isolation. The result is loss leakage hidden inside the photo evidence carriers already trust. Carpe Vision is built for that environment: eleven forensic engines run in parallel on every image, three of them dedicated specifically to AI-generated and AI-edited content, and the verdict returns with the visual evidence to back it up.
See Carpe Vision on Your Own Images
Our team can run Vision on a sample of your claim images, show you the verdicts and the per-engine evidence, and walk through how it plugs into FNOL, SIU referral, and batch review workflows.