Context Intelligence for On-Device Vision Systems.
Understand what's happening. Make smarter decisions. Protect privacy — without ever uploading an image.
Cameras see pixels.
Ours understands meaning.
Traditional AI moderation has no model of what is actually happening. It sees shapes. It triggers on patterns. It cannot tell a medical exam from a violation. Context intelligence changes that.
See why this matters →pixels[1920×1080] → skin_detected
// what PurenetX understands
scene: clinical_setting
intent: professional
consent: established
decision: context_aware
Two Ways to Process a Camera Frame
One looks at pixels. One understands what's happening. The outcome — and the user experience — are completely different.
→ pixel_scan(frame)
→ skin_detected = true
→ decision = BLOCK
// No scene understanding
// No intent. No context.
→ scene = understand_context(frame)
→ intent = assess_intent(scene)
→ risk = score_risk(scene, intent)
→ decision = policy_decision(risk)
Same Scene. Completely Different Decisions.
Five real scenarios. See exactly what breaks without context — and what becomes possible with it.
Same physical content. Different understanding. Better outcomes for everyone. Full explainer →
What We Have Today
Our framework is validated through research. Real inference engine development is beginning now. We're seeking design and research partners for co-development and validation.
Context Intelligence Framework
Six-dimension analysis framework (scene, activity, relationship, consent, vulnerability, risk) validated through our 55-scenario research database.
Inference Engine
Real ML model training beginning Q3–Q4 2026. Seeking pre-seed funding and design partners for co-development. Timeline: 8–12 weeks per partner.
SDK Framework
Integration-ready framework available for evaluation. Design partners can begin data collection while the model develops in parallel.
Research Database (CKB)
55 professionally annotated scenarios across medical, fitness, celebration, intimate, and safety contexts. Available to research partners under NDA. About the CKB →
Design & Research Partners
Design partners (camera apps, platforms, robotics) for pilot validation — and research partners (universities, institutes) for co-authored publications.
Think you're a fit?
We're recruiting for co-development of the inference engine (Q4 2026 – Q1 2027) and 12-week pilot partnerships. We review applications weekly.
Get in touch →Four Principles. No Exceptions.
On-Device Processing
No cloud required. No data transmission. Privacy by architecture, not policy.
Transparent Reasoning
We show you why we made each decision. Explainability is not optional.
Research-First
Built on peer-reviewed principles. We validate before we publish numbers.
Honest About Stage
We're early. We say so. Transparency builds better partnerships than hype.
Security research taught me how systems get exploited. I built PurenetX to close the most consequential gap — harmful visual content captured before anyone can stop it. The answer isn't better moderation. It's earlier interception, with full context.
Concept Proven. Now Building the Real Thing.
✓ Framework designed for on-device, privacy-first processing
✓ Decision logic demonstrated through prototype examples
→ Model validation: pending design partner data
→ Production deployment: Q2 2027 target
We're early. We say so. Transparency builds better partnerships than hype. That's why we're recruiting design partners and researchers to build this with us — not to deploy a finished product.
From Framework to Production
Framework Validation
Model Development & Research
Enterprise Pilots & Publication
Broader Integration
See How It Works (Concept Demonstration)
Our concept prototype demo shows the framework and decision logic in action. Real model training: 8–12 weeks with design partner data. We review applications weekly — if your use case is a strong fit, you'll hear from us within 2 weeks.