ZSure

Company profile:

Startup name: ZSure

Tagline: Real-time deepfake/AI detection with explainable visual heatmaps

Elevator Pitch: We built ZSure (https://zsure.in), a real-time synthetic media verification tool that goes beyond binary “real or fake” labels by providing frame-by-frame explainable heatmaps. and you can try it out as an extension by simply searching for it on Chrome web store

Target Market: Enterprises, financial institutions, media organizations, government agencies, and cybersecurity teams

How will you make money?: Media houses, administration

How much capital have you raised?: None

Website: https://www.zsure.in/

City/Country: India

AI-assisted summary:

ZSure is a deepfake and synthetic-media detection product from Noida-based Hypotenuse Analytics India Pvt. Ltd., positioned for organizations that need to assess whether digital content, identities, or communications may be manipulated or AI-generated. (Source: https://www.zsure.in/) The startup matters because it is attempting to turn media-authenticity checks into an operational layer for fraud prevention, onboarding, content moderation, and investigative workflows. (Source: https://www.zsure.in/faq)

Problem and target users

ZSure addresses risks associated with synthetic video, cloned audio, generated images, manipulated documents, and synthetic identities, particularly where organizations must make decisions based on media they cannot easily authenticate manually. (Source: https://www.zsure.in/faq) Its stated enterprise use cases include KYC and onboarding, transaction monitoring, call-center screening, newsroom verification, content moderation, and forensic investigation. (Source: https://www.zsure.in/faq)

The company’s technical paper identifies end users such as journalists and fact-checkers, product and moderation teams, forensic investigators, security analysts, and high-volume platforms. (Source: https://www.zsure.in/ZSure_TechnicalWhitePaper_Version1.pdf) ZSure also offers a Chrome extension that lets users select a specific image or video on a webpage for analysis rather than scanning browsing activity by default. (Source: https://chromewebstore.google.com/detail/zsure-deepfake-detector/fjaefobloejecapppdjobobmlhlgkkja)

Product and solution

The core product is a media verifier that accepts image and video uploads and returns a manipulation score using what ZSure describes as a three-detector ensemble. (Source: https://www.zsure.in/detect) According to ZSure, the ensemble combines checks for visual structure, texture-level artifacts, and specialist signals associated with modern generative-media outputs, with ambiguous cases intended to be surfaced rather than forced into a binary answer. (Source: https://www.zsure.in/ZSure_TechnicalWhitePaper_Version1.pdf)

The company says its broader platform supports analysis of face-swap and lip-sync video, synthetic or cloned speech, generated images, altered documents, and KYC-related identity fraud indicators. (Source: https://www.zsure.in/faq) For enterprise deployment, ZSure advertises REST APIs and SDK options for embedding detection into onboarding, transaction-monitoring, and content-moderation workflows. (Source: https://www.zsure.in/faq)

Its technical paper also describes browser-extension, direct-upload, API, and cloud/batch access modes, with outputs intended to include a verdict, confidence score, and model-level detail. (Source: https://www.zsure.in/ZSure_TechnicalWhitePaper_Version1.pdf) The Chrome Web Store listing, updated August 25, 2026, identifies the extension as version 0.2.0 and notes limitations with heavily recompressed social-media content and DRM-protected video streams. (Source: https://chromewebstore.google.com/detail/zsure-deepfake-detector/fjaefobloejecapppdjobobmlhlgkkja)

Business model, pricing signals, and traction

ZSure appears to be pursuing a freemium-to-enterprise model: its extension uses Google sign-in to administer a free per-user scan quota, while its enterprise materials refer to volume pricing, service-level agreements, private deployments, and demo-led API access. (Source: https://www.zsure.in/privacy) (Source: https://www.zsure.in/faq)

Public pricing, named customers, funding information, and independently verified revenue or deployment figures were not identified in the sources reviewed. The Chrome Web Store listing showed one user and no ratings at the time it was crawled, so public marketplace adoption should be viewed as very early. (Source: https://chromewebstore.google.com/detail/zsure-deepfake-detector/fjaefobloejecapppdjobobmlhlgkkja)

ZSure’s white paper reports controlled benchmark results rather than live-production performance, including 90.9% overall accuracy across 2,660 images and materially weaker results on a 400-item heavily compressed social-media set; these are company-reported figures and should be independently validated by prospective enterprise buyers. (Source: https://www.zsure.in/ZSure_TechnicalWhitePaper_Version1.pdf)

Expert take

ZSure’s strongest positioning is its attempt to package media verification for multiple operational contexts rather than selling a narrow upload-and-score detector. Its emphasis on confidence signals, uncertainty, and workflow integration is directionally appropriate for fraud and trust-and-safety buyers.

The key diligence question is validation: early customers and investors should test performance on representative, adversarial, compressed, multilingual, and rapidly changing real-world media before relying on detection scores in high-stakes decisions. The absence of public enterprise references and independently audited performance evidence remains the central unknown.

Note: Information based on publicly available sources at the time of writing, and summarized by AI.