How the AI actually works.

Most pet apps hide the AI behind a black box. We’re going to walk through exactly how mypooch.ai’s recommendations are generated, what data informs them, and what they can and can’t do.

The framework: predator pattern

Behind the AI is a behavioral framework borrowed from working-dog training, where the predator-pattern sequence — orient, eye, stalk, chase, grab, kill-bite, dissect, consume — explains a huge percentage of “problem behaviors” pet owners struggle to describe.

A Border Collie chasing kids isn’t being bad. A Labrador grabbing every shoe isn’t broken. They’re running the same hunting program every dog has, just with the wrong outlet. The framework lets the AI categorize what your dog is doing and prescribe outlets that match.

This isn’t an academic theory we read in a textbook. It’s the same framework Chris Moran (founder) uses with private clients — the high-drive cases other trainers gave up on. The AI is trained on this lens.

What inputs the AI uses

Every output the AI produces is shaped by a stack of inputs about your specific dog:

Every recommendation — daily plan, drill, exercise routine, product pick, behavior search answer — is generated by passing your dog’s full profile and recent history into the model alongside the question. The model isn’t just guessing from breed alone.

How a check-in becomes a read

When you submit a daily check-in (text or video), here’s what happens:

• 1. Context assembly: the app pulls your dog’s profile, last 5 check-ins, current energy baseline, and any flagged behaviors.
• 2. Input parsing: the AI parses your description (and, on Premium, analyzes the video) for behavioral signals — body language cues, vocalizations, movement patterns, environment.
• 3. Framework matching: the model matches what it observes against the predator-pattern framework and known behavioral patterns for your dog’s profile.
• 4. Scoring: energy and stress are scored on 0-10 scales, calibrated to your dog’s baseline so the score is comparable over time.
• 5. Mood tagging: 2-4 mood tags from a curated list (playful, anxious, settled, frustrated, alert, etc.) are assigned.
• 6. Plan generation: the AI generates a daily plan that matches the dog’s current state — physical exercise dosage, mental enrichment, training drills, and recovery time.
• 7. Output: all of this lands in your dashboard, plus a timeline entry that builds your longitudinal record.

What’s under the hood

The actual AI is a large language model. We use a curated combination of providers, depending on the task. We don’t train our own foundation model from scratch — we use commercial-grade models from providers like OpenAI and Anthropic, with our own behavioral framework, prompts, and guardrails layered on top.

For video check-ins, we use multimodal vision-capable models that can analyze short clips for movement, body language, and environmental context.

What we don’t do

• We don’t train public models on your data. Your dog’s check-ins, videos, and profile data aren’t used to train models that anyone outside our platform can access. See Privacy Policy.
• We don’t pretend the AI is perfect. It’s a powerful pattern-matcher, not an oracle. It can miss things. It can misread context.
• We don’t replace vets or behaviorists. The model gives behavioral and educational reads. It does not diagnose, prescribe, or treat. See Veterinary Disclaimer.
• We don’t recommend products we wouldn’t use ourselves. Affiliate recommendations are curated by a working trainer, not driven by the highest commission.

How we validate

Three layers of validation keep the AI honest:

• Trainer review: Chris Moran reviews the AI’s framework outputs against the same cases he’d see in his private practice. If the model gets a case wrong in a way a trainer wouldn’t, the prompt and guardrails get updated.
• User feedback: every check-in and recommendation can be marked accurate or not. Patterns of wrong answers feed back into improvements.
• Safety guardrails: the AI is prompted to defer to a vet for medical questions, to a certified behaviorist for aggression or bite cases, and to call out emergencies clearly rather than try to handle them.

What we’re still working on

Honest list of things we know are imperfect today and that we’re improving:

• Video analysis accuracy for very short or low-quality clips
• Recognition of breed-specific behavioral baselines for rare and mixed breeds
• Cross-language behavioral terminology (we’re English-only at launch)
• Sensitivity to senior dog cognitive patterns vs. behavior changes from physical decline

We surface these in the app when relevant. If the AI is uncertain, it says so.

If you want to go deeper

Questions about how we built this, what data we use, or how a specific recommendation was generated? Email support@mypooch.ai. We answer.