Three layers. One intelligence engine.
Verification checks a moment.
Intelligence sees the whole picture.
Most identity systems perform a biometric check or document scan tied to a single session. Once that check is complete, there is no ability to understand whether subsequent interactions truly belong to the same individual — or whether identity has been transferred, spoofed, or gradually compromised.
Single checkpoint
Traditional verification happens once. After that, identity is assumed — never re-confirmed across sessions, devices, or contexts.
Isolated signals
Document checks, liveness scans, and background screens each work alone. No system connects the dots between them.
Invisible networks
Two accounts share an address. A passport appears under two names. A device links five identities. Flat databases can't see these patterns.
Static trust
Trust is granted once and forgotten. There's no mechanism to detect when identity has been transferred, spoofed, or gradually compromised.
Three layers.
Each independently valuable.
The platform is structured into three distinct but complementary layers. While they operate together as a unified system, each layer is independently valuable and can be productised in its own right.
Graph Intelligence
Identity as a network, not a record.
Traditional systems store identity as isolated attributes — a name, a document, a check. Our graph models identity as a living network of interconnected entities and events. Each interaction adds structure. Each connection reveals context that flat databases can't see.
- Entity resolution across devices, sessions, and contact points
- Relationship mapping between people, documents, and organisations
- Cross-identity pattern detection (shared infrastructure, reused data)
- Temporal graph analysis — how identity behaviour evolves over time
AI Analytics & Inference
From relationships to risk signals.
The AI layer transforms graph structure into actionable intelligence. Rather than evaluating signals in isolation, it considers combinations — behavioural consistency, device usage, network proximity, and temporal change. GPU-accelerated graph neural networks enable real-time inference across millions of identity relationships.
- Multi-signal identity confidence scoring (biometric + behavioural + device + network)
- Anomaly detection against learned behavioural baselines
- Synthetic identity and coordinated fraud ring detection
- Link prediction to surface hidden relationships before they're exploited
Identity Intelligence Infrastructure
Complexity in. Clear signals out.
External systems don't need to understand graphs or models. They need clear signals to make decisions. The 3I layer translates deep intelligence into API-driven outputs — trust scores, risk flags, continuity assessments, and entity linkage insights delivered in milliseconds.
- Real-time trust score queries via REST and GraphQL
- Webhook event streams for identity state changes
- Contextual risk indicators — not binary pass/fail, but nuanced signals
- Audit trail export for compliance and regulatory reporting
What this
makes possible.
Continuous Identity Assurance
Trust isn't a one-time gate. It's reinforced or degraded with every interaction, building a living picture of identity over time.
Coordinated Fraud Detection
Spot fraud rings, shared accounts, and synthetic identities by analysing the network — not just individual claims.
Contextual Risk Scoring
A login that looks valid in isolation gets flagged when viewed in context. A consistent user gets fast-tracked. Decisions informed by behaviour, not just attributes.
Cross-Session Continuity
Detect when the same human is present across sessions, devices, and contexts — or when they're not.
Behavioural Baselines
Learn how a real person behaves, moves, and interacts. Detect subtle breaks in continuity that traditional systems miss.
Privacy-First Architecture
On-device biometric processing. Hash-based identity matching. Intelligence without unnecessary data exposure.
Built for GPU.
Not bolted on.
Identity graph traversal and neural network inference are inherently parallel workloads. GPU acceleration isn't a marketing label — it's an architectural requirement. Traversing millions of identity relationships in real-time, running graph neural networks for anomaly detection, and computing trust scores across dense entity networks demands the kind of parallel compute that only GPU infrastructure provides.
Graph Traversal
Parallel exploration of identity relationships across millions of nodes. What takes CPU-bound systems seconds happens in milliseconds.
Neural Network Inference
Graph neural networks evaluate identity signals in context — behavioural patterns, device fingerprints, and network structure — simultaneously.
Real-Time Scoring
Trust scores computed on every interaction, not batch-processed overnight. Identity intelligence that's current, not historical.
Data Flow
From raw identity signals to actionable intelligence
From verification to intelligence.
Certifyd's 3I platform is currently in development as part of NVIDIA Inception. We're working with early partners to validate the architecture across workforce identity, fraud detection, and compliance automation.