The face you trust
is no longer proof
of anything
FaceOff proves what your systems process -verify what is real, where data lives, and evidence it on demand
Core platform architecture
Data sources
Multimodal AI analysis
Evidence correlation
Risk assessment
Decision intelligence
Explainable output
Services
Two problems, one platform
Data Security
Trust Factor graded 1–10
Multimodal detection that scores whether a face, a voice or a document is real — on an upload, a live call, a payment or the page itself — and produces evidence that survives scrutiny.
Data Privacy
18 DPDP obligations owned
One privacy fabric for the DPDP Act — consent, discovery, DSAR, masking, assessments, breach and audit evidence, all resolving against the same record of where personal data lives.
AI Governance
Why choose FaceOff
DPDP is not a project. It is a configuration
- 01
Detection and privacy on one platform
Most vendors sell you one or the other, and you integrate the seam yourself. The estate map that answers a DSAR is the same one your fraud team queries when a face fails a check.
- 02
Enforced, not logged
Downstream systems check consent before they process, and acknowledge it. A consent record that nothing reads is a log file, and a log file is not a control.
- 03
Evidence as a by-product
The audit trail is produced by running the controls, not assembled the week a regulator writes. Every hop is timestamped, append-only and replayable.
- 04
Explainable by construction
Every verdict exposes which of the eight models produced it and which dismissed it, with the per-decision detection chain retained. A finding you cannot defend is not a finding.
- 05
Multi-jurisdictional from day one
DPDP, GDPR, CCPA/CPRA, LGPD and PIPEDA run on one control library, so what you build for the 2027 deadline still works when the next law lands.
Core capabilities
What the platform actually does
Score synthetic media in real time
Eight models run in parallel across vision, audio and physiology — facial structure, micro-expression, ocular dynamics, posture, heart rate, speech sentiment, audio tone and SpO2 — and fuse into a single graded Trust Factor.
Verify the human, not just the credential
Behavioural biometrics, speaker verification, facial authentication and synthetic-identity graph analytics establish that the person on the other end exists and is who the record says.
Find every copy of personal data
Discovery and mapping across structured and unstructured estate, so obligations resolve against a live catalogue rather than a spreadsheet somebody maintained until they left.
Carry consent to the systems that process
Collected on every channel, normalised into one purpose-scoped record, and pushed to CRM, analytics, warehouse, martech and processors — each acknowledging it before it processes.
Produce evidence that survives scrutiny
Notice served, choice made, policy version applied and every downstream acknowledgement land in an append-only trail. Detection findings keep their full reasoning chain.
Govern the models doing the work
A register of every model in production with its training-data provenance and the decisions it influences, assessed against the EU AI Act and DPDP Sec. 10 in the same engine as your DPIAs.
01 / 06
Key benefits
What changes once it is running
Fraud stopped before it completes
Impersonation is caught at onboarding, in the call and at the payment — while the transaction can still be refused, not in the reconciliation that follows it.
Regulatory exposure reduced
The DPDP Act carries up to ₹250 Cr for a failure of reasonable security safeguards, assessed per instance. Controls that run continuously are what stands between you and that number.
Investigations that hold up
Forensic reports, per-decision detection chains and provenance tiers on every output mean a finding can be defended to a board, a regulator or a court.
One programme, many regulators
One catalogue, one control library and one assessment engine serve every regime you are in scope for, instead of a separate project each time a jurisdiction moves.
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