Detect. Explain. Defend.

ScamAI detects deepfaked faces, face swaps, and fully AI-generated images and video. It also catches forged and tampered identity documents — including template-generated and synthetic IDs — manual edits and splices, and device-level attacks like virtual cameras and injection attacks that defeat naive selfie checks. One engine, the Eva V1.6 Detection Model, covers onboarding selfies, identity documents, claims media, live video meetings, and user-generated content.

Detection is probabilistic, not a yes/no: the Eva V1.6 Detection Model returns a tiered likelihood result and the signals that fired, so every result is explainable. You set the thresholds — auto-decline, step-up, or human review — and tune them per flow: stricter for high-value wires, looser for routine account changes. You control the false-positive/false-negative trade-off, not a vendor's fixed cutoff. Eva V1.6 is retrained as new generators appear, so accuracy tracks the current attack landscape, not a frozen benchmark.

Real-time detection is what Halo, ScamAI's meeting product, is built for. Halo runs deepfake detection on-device during live video meetings and flags a deepfaked face or manipulated feed while the call is happening, and because analysis stays on your machine, meeting video never leaves it. For application flows, the REST API offers real-time latency for live onboarding and step-up checks, plus batch endpoints for media libraries and claims backlogs. A face-swapped selfie gets declined before the account exists.

Media submitted to ScamAI is analyzed to produce a result, then discarded; what's retained is the result — score, signals, request metadata — not your content, unless you configure retention for your own audits. Customer media is not used to train models without agreement. Halo analyzes meeting video on-device, so that media never leaves your endpoint at all. And for data-residency or no-egress requirements, on-prem and private-cloud deployment keep media, results, and logs entirely inside your own perimeter.

New face-swap and diffusion tools appear constantly, so Eva V1.6 trains on real attack media — actual fraud attempts, not academic datasets — and retrains as new generators show up in the wild. The team publishes original research — papers and open datasets on deepfake detection, document forgery, and adversarial attacks on age-estimation systems, with named authors on arXiv — and studied techniques become detection signals. Ask any vendor when its model was last updated, and against what.

Integration is one REST API: send video, images, or documents; get back a scored result with evidence attached. Drop it into your existing decision point — onboarding/KYC, claims intake, the support or moderation queue — and route on the score: approve, step-up, or review. There is no model to train, so a first integration typically takes a day, not a quarter. SDKs are coming; Halo, a lightweight desktop app, needs none; regulated environments can deploy on-prem or private-cloud.