Build a Safety and Moderation Layer for AI Companion Apps

1. 1

   Build the detection stack with clinical input

   Real-time scoring for crisis signals (self-harm ideation, abuse disclosure, minors self-identifying), plus the slower arc-level patterns unique to companions: escalating dependency, isolation encouragement, romantic escalation toward users flagged as minors. Retain a licensed clinical advisor to shape taxonomies and intervention language; this is not a domain to wing.

2. 2

   Design interventions, not just alarms

   Detection is worthless without response: configurable actions per severity, from softening the companion's replies, to injecting crisis resources (hotlines, text lines, localized where possible), to locking romantic modes for suspected minors, to escalating to the platform's human review. Ship sane defaults so a three-person startup inherits responsible behavior out of the box.

3. 3

   Make privacy compatible with safety

   Companion conversations are radioactive in sensitivity. Process in real time without long retention by default, store only what audit obligations require, offer strict data processing agreements, and never train shared models on customer conversations without explicit contractual consent. Your privacy architecture will be diligenced by every serious buyer.

4. 4

   Track the regulatory wave and package compliance

   Rules for companion AI are actively forming (minors protections, crisis-response duties, disclosure requirements) and vary by jurisdiction, so build compliance packs that map your logs and interventions to named requirements as they land. Selling keep up with the law as a service multiplies your value every time a new rule passes.

5. 5

   Price per conversation volume with an audit tier

   API pricing by messages scored keeps small apps affordable; higher tiers add the audit dashboards, incident workflows, and compliance reporting bigger platforms need. The buyers are motivated: their app store standing, insurance, and press coverage all depend on the answer to what happens when a user is in crisis.

6. 6

   Lead the industry conversation honestly

   Publish safety benchmarks for the category, propose an intervention standard, and be transparent about your own false-positive and false-negative tradeoffs. The vendor that talks straight about limits becomes the default recommendation of researchers, journalists, and regulators, which in a scrutinized category is the entire sales funnel.

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Best for: A trust-and-safety or ML engineer, ideally with a clinical advisor, who wants to make a hard category less dangerous

Not for: Anyone who would ship shallow safety theater or handle crisis routing without real expertise. This is life-and-safety infrastructure, and it demands clinical and trust-and-safety depth.