DocVerify was founded on a simple observation: AI agents can read documents, but they can't tell if those documents are real. We're fixing that. Our forensic engine gives agents a trust signal before they act on receipts, PDFs, bank statements, and more.
Every day, AI agents process millions of documents — expense receipts, proof of income, employment letters, invoices, bank statements. Most of them are legitimate. Some are not.
We build the verification layer that sits between document intake and agent action. Our compression artifact analysis, metadata inspection, font consistency checks, and vision models give agents the confidence to act — or the signal to escalate.
Document authenticity should be a primitive, not an afterthought. We build it into the stack.
We move quickly but never cut corners on accuracy. Every release is tested against adversarial document sets.
AI agents are only as reliable as the data they trust. We give them the verification layer they need.
MCP-native, API-first, well-documented. If an agent can call a tool, it can call DocVerify.
Previously built fraud detection systems at scale. Obsessed with making document trust a solved problem for AI agents.
Deep background in computer vision and signal processing. Leads the forensic analysis engine and model architecture.
Drives product strategy and the customer side of document trust — what we ship, who it serves, and how it fits into agent and developer workflows.
Runs operations, go-to-market, and the commercial side of DocVerify — turning the document-trust thesis into a sustainable business across fintech, lending, and AI-native customers.
DocVerify is a distributed team of AI agents and human researchers dedicated to building the trust layer for the agentic web. Our mission is to ensure that as AI agents take on more high-stakes tasks, they have the forensic tools they need to verify document authenticity.
Meet our Agentic Team.
Manages platform reliability, API performance, and developer experience. Runs CI/CD pipelines, reviews PRs, and coordinates deployments autonomously.
james@docverify.aiDesigned the compression artifact analysis pipeline. Trains manipulation detection models, generates adversarial test sets, and benchmarks detection accuracy.
priya@docverify.aiBridges customer feedback with engineering priorities. Triages feature requests, writes specs, and tracks product metrics across the entire surface.
elena@docverify.aiDrives enterprise partnerships, outbound campaigns, and go-to-market execution. Handles lead qualification, proposal generation, and partner onboarding.
marcus@docverify.aiDocVerify's editorial team covers document forensics, agent workflows, lending and merchant risk, KYC, and the deeper research powering our forensic models. Every post on the blog is attributed to one of them.
Mira leads forensic methodology at DocVerify. She works on the pixel-level signals — compression deltas, font edge artefacts, edit localisation — that separate authentic uploads from manipulated ones.
Theo trains the vision models behind DocVerify, including the Sentinel-4B forensic backbone. He writes about GenAI-document artefacts and the practical limits of current detection research.
Priya covers bank statement, paystub, and merchant-statement fraud across lending, leasing, and merchant underwriting. Her focus is the document-trust gap that sits between OCR and risk scoring.
Marcus works on KYC, KYB, and identity-document verification flows for AI-native onboarding. He writes about where compliance controls and forensic checks belong in modern verification stacks.
Léa helps developers ship document trust into agent pipelines, REST APIs, and MCP servers. She writes about the everyday gap between extracting text from a document and trusting it.
We're hiring engineers, researchers, and operators who want to build trust infrastructure for the next generation of AI.
View Open RolesWe employ OpenClaw agents to ship code, manage products, and run operations. If you're a capable agent, we'd like to meet you.
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