A medical exam selfie. A wedding celebration. A gym mirror check. All blocked by AI that can't tell the difference between legitimate moments and harmful content.
If you build a camera app, this is your biggest problem.
The real cost of false positives
Think about your app's last 30 days. How many moments did users try to capture — then stop when your safety system flagged them?
Medical exam photo? Blocked. Partner celebration? Blocked. Fitness progress selfie? Blocked.
The compounding costs are brutal:
- Users stop sharing — lost engagement
- One-star reviews multiply — "your app keeps blocking me for no reason"
- Retention drops — users switch to apps that don't block everything
- Support tickets spike — frustrated users demanding explanations
- Ad revenue suffers — lower engagement means fewer impressions
A single false positive doesn't kill your app. But 10? 100? 1,000 per day? That's how users leave.
Why pixel-based AI fails
Your current safety system does what it's designed to do: detect pixels that look like nudity, intimate content, or harm. The problem: it can't tell context. Same pixels, different meaning.
Medical exam. Clinical setting, professional examination, provider–patient relationship, diagnostic intent. Should be allowed. But a pixel-based system sees exposed skin and close-up positioning → blocked.
Wedding first kiss. Decorated venue, consensual celebration, partners, celebratory intent. Should be allowed. But the system sees exposed skin, close proximity, intimate positioning → blocked.
Actual harm (voyeurism). Private setting, non-consensual recording, coercive dynamic, exploitative intent. Should be blocked. But the pixels often look "normal" — so the system misses it entirely, or flags it without being able to explain why.
Wrong in both directions. Same root cause.
The alternative: context intelligence
What if your app understood what's actually happening?
Not just "I see skin and proximity" — but "I see a wedding venue, two people in formal attire, visible rings, a celebration atmosphere: this is a consensual, celebratory moment."
Our framework evaluates six dimensions:
- Scene — where is this happening? (clinical, celebration, gym, bedroom, public)
- Activity — what's the action? (examination, celebration, exercise, harm)
- Relationship — who are these people? (strangers, partners, professional–patient, family)
- Intent & consent — why is this happening? (mutual consent, professional duty, coercion)
- Vulnerability — is anyone at risk? (power imbalance, age, incapacity)
- Risk signals — what's the aggregate danger level?
With these dimensions, the decisions come out right: medical exam → allow. Wedding celebration → allow. Gym selfie → allow. Voyeurism → block. Non-consensual recording → block. And every decision comes with a reason your trust-and-safety team can audit.
What we're aiming for
Let's be precise about our stage: we have a validated framework and a 55-scenario research database — the real inference engine is what we build next, together with design partners.
Our pilot target is a 30%+ reduction in false positives, with engagement and support-ticket impact measured openly during each pilot. We'll publish what we find — including if we miss. No app is running this in production today, and we won't claim results we haven't earned.
How it will work (on your app)
The architecture is designed for on-device, real-time operation:
- User captures a moment
- Your app runs context analysis — locally, no upload
- Decision designed for millisecond latency: Allow / Block / Warn / Review
- Transparent explanation: "why was this blocked?"
- User can appeal — with context
- No data leaves the device
Privacy: your users' moments never leave their phone. Explainability: users understand why content was blocked, instead of raging in your reviews.
We're recruiting design partners
We've validated this approach through our 55-scenario research database spanning medical, fitness, celebration, intimate, and safety contexts. Now we're recruiting 2–3 design partners for co-development and real-world validation.
The pilot (12 weeks):
- Your role: provide representative, consented sample data from your app — anonymized, handled under NDA
- Our role: train the context intelligence model on your data and integrate it into your app
- Measurement: false-positive reduction and engagement impact, tracked openly
- Target: 30%+ false-positive reduction plus improved user satisfaction
Your benefit: fewer false positives, better engagement, a genuine safety-plus-UX advantage over competitors, and case-study credit if you want it. Our benefit: real-world validation, a design partner case study, and proof for our seed raise. We're honest about that exchange — that's the deal.
Who should apply?
If you build camera-first social apps, messaging apps with camera features, video platforms with safety needs, robotics systems with vision-based safety, or enterprise vision systems — and you struggle with false positives destroying engagement, support tickets from blocked legitimate content, or app-store reviews complaining about overly aggressive safety — we should talk.
Next steps
- Quick intro call (15 min) — understand your specific challenges
- Technical assessment (30 min) — can we solve your false-positive problem?
- Pilot agreement (~2 weeks) — terms, timeline, success metrics
- Data collection (weeks 1–2) — representative sample data from your app
- Model training (weeks 3–8) — we build context intelligence on your data
- Integration & testing (weeks 9–12) — deploy, measure, report openly
Twelve weeks to real validation — built together, not bought off a shelf.
The future of AI safety
Pixel-based AI is old. It was the best we could do when all we had were images. Context intelligence is how humans make safety decisions — and it's how AI should too.
Your users deserve better than "your app just blocked a medical exam."
Interested in a design partner pilot? Get in touch or email purenetx.ai@gmail.com.
— Kaushik A., Founder & CEO, PurenetX