Trustworthy AI Infrastructure for Digital Identity

Know whether biometric evidence can be trusted — before making an identity decision.

BioTrust AI evaluates facial capture quality, authenticity, attack risk and operational reliability through one explainable trust layer for digital identity workflows.

Analyze a Demo Capture Request a Pilot

Make better identity decisions before unreliable or manipulated biometric evidence reaches your verification system.

Live Trust Analysis

Session bt_demo_7f42a1

Review

Trust Score

Review

Review recommended

Quality

Authenticity

Attack Risk

Context

Illustrative prototype output — not a production biometric assessment.

The Problem

Identity systems are only as reliable as the evidence they receive.

Modern eKYC pipelines rely on brittle, fragmented signals. When capture quality drops or content is manipulated, downstream decisions inherit that noise.

01/Failure mode

Capture Quality

Blur, poor lighting, compression, pose and device processing can reduce recognition reliability.

02/Failure mode

Manipulated Content

Retouching, face swaps, filters and synthetic content can alter identity-relevant evidence.

03/Failure mode

Presentation & Injection Attacks

Replays, virtual cameras, screens and injected streams can bypass conventional capture controls.

04/Failure mode

Fragmented Signals

Quality, liveness, device risk and fraud signals often live in disconnected systems.

05/Failure mode

Opaque Decisions

Binary outcomes provide limited explanation, uncertainty or guidance for operational teams.

Traditional workflow

Several disconnected tools

Inconsistent thresholds

Manual investigation for edge cases

BioTrust workflow

Unified signals in one API

Calibrated Trust Score

Explainable, policy-based action

How It Works

One trust layer. Three intelligence capabilities.

Compose the signals you need. Every layer is versioned, auditable and calibrated per use case.

Biometric Quality Intelligence

Predicts whether a capture is fit for downstream recognition.

Resolution, sharpness and compression assessment

Lighting and contrast checks

Pose, occlusion and capture-condition indicators

Expected downstream recognition utility

Recapture recommendations

Device / channel quality drift monitoring

Interactive Preview

See how a Trust Score is composed.

Choose a sample scenario to see the decision, signal breakdown and explanation.

Trust Score

Well-lit, sharp, frontal capture from a mainstream mobile device.

Quality

Authenticity

Attack Resistance

Context

Positive factors

High resolution with minimal compression
Consistent, natural illumination
Neutral pose with no occlusion

Attention factors

Capture channel not yet verified through attested SDK

Recommendation

Proceed with automated onboarding decision.

Illustrative prototype output — not a production biometric assessment.

Open Full Trust Analysis

Why BioTrust

Beyond another deepfake detector.

Trust layers are more than a single classifier. BioTrust combines quality, authenticity and context into a decision surface teams can operate.

Capability	Image-quality tool	Standalone fraud detector	BioTrust AI
Predicts biometric utility, not just visual quality
Combines quality and authenticity signals
Handles context and device variation
Represents uncertainty
Produces actionable explanations
Supports policy-based decisions
Designed for API integration
Supports cloud, private-cloud and future edge deployment

Use Cases

Where trust intelligence changes outcomes.

Design partnerships focus on eKYC and fintech. Adjacent industries are on the roadmap.

eKYC & Identity Verification

Improve onboarding decisions, identify recapture needs and escalate suspicious sessions with a calibrated trust layer that plugs into your existing verification pipeline.

Developer Experience

Integrate trust intelligence through one API.

Predictable schema, versioned models, sandbox environment. Plug BioTrust into the workflow you already run.

JSON response

{
  "analysis_id": "bt_demo_7f42a1",
  "trust_score": 82,
  "decision": "review",
  "confidence": 0.88,
  "signals": {
    "biometric_quality": 91,
    "authenticity": 76,
    "attack_resistance": 79,
    "capture_context": 84
  },
  "reasons": [
    {
      "code": "AUTHENTICITY_UNCERTAINTY",
      "severity": "medium",
      "message": "Local image-processing indicators require review."
    }
  ],
  "recommended_action": "Route to assisted verification."
}

Illustrative prototype output — not a production biometric assessment.

Explore API prototype →

REST API

SDK-ready architecture

Webhooks planned

Policy configuration

Audit events

Versioned models

Sandbox environment

Private deployment options

Security & Responsible AI

Trust infrastructure must itself be trustworthy.

Prototype architecture designed to support privacy, auditability and human oversight.

Data minimisation

Designed to process only what is necessary for a trust decision.

Configurable retention

Retention windows planned as customer-configurable controls.

Encryption in transit & at rest

TLS 1.2+ in transit; encrypted storage for prototype artefacts.

Role-based access

Workspace and role scoping planned for the enterprise dashboard.

Audit logs

Analysis and policy changes emit structured audit events.

Human oversight

Review queues and configurable human-in-the-loop workflows.

Model & version traceability

Every response references the model version that produced it.

Fairness evaluation

Planned bias evaluation across demographic and capture conditions.

Uncertainty communication

Scores are paired with confidence bands, not binary outcomes.

Privacy-conscious deployment

Cloud, private-cloud and future edge deployment options.

Data provenance documentation

Documentation of training data provenance and evaluation.

Incident-response readiness

Prototype incident-response playbooks and disclosure contact.

Read the full security & responsible-AI overview →

Product Architecture

Composable layers from capture to audit.

Every request flows through versioned, observable layers with policy and human review baked in.

Layer 1
Capture / eKYC workflow
Existing SDKs, web & mobile capture
Layer 2
Secure API gateway
TLS · auth · rate limits · audit
Layer 3
Quality Intelligence · Fraud & Authenticity Intelligence
Parallel signal extractors with versioned models
Quality Intelligence
Fraud & Authenticity
Layer 4
Trust Decision Engine
Signal fusion · calibration · uncertainty
Layer 5
Policy & Human Review Layer
Customer thresholds · reviewer queues
Layer 6
Audit, Monitoring & Customer Dashboard
Traceability · drift · reporting

Founder

Built from deep biometric research and enterprise data experience.

Cristian Botezatu

Founder, BioTrust AI

Cristian has more than nine years of experience across technology and data. His background includes doctoral research in biometrics at NTNU, an M.Sc. in Computer Science — AI and Algorithms through DTU and EPFL, European Commission-funded biometric research through iMARS, applied cybersecurity research at ATHENE, and an IEEE publication concerning selfie filters and face recognition. He has also held Lead Data Scientist and Chief Data Officer roles and has experience building and mentoring technical teams.

Biometrics Research

Doctoral research at NTNU

AI & Algorithms

M.Sc. through DTU & EPFL

Cybersecurity Research

Applied research at ATHENE

European R&D

iMARS biometric research

Enterprise Data Leadership

Lead Data Scientist / CDO roles

Entrepreneurship

Building and mentoring technical teams

Roadmap

From research baselines to commercial API.

Phase 1
Research baselines & prototype
Phase 2
Quality and authenticity modules
Phase 3
Integrated Trust Decision Engine
Phase 4
Pilot integrations and validation
Phase 5
Independent security / privacy assessment
Phase 6
Commercial API release

Pilot Programme

Help shape the trust layer for digital identity.

We are seeking eKYC platforms, fintech companies and identity teams for design partnerships, technical validation and pilot discussions.

Discuss a Pilot Try the Prototype