PetSignal.ai does not try to translate what your pet is "saying." We decode signals you can observe — body posture, vocal prosody, and context — by grounding every output in four peer-reviewed and lab-based research traditions. Here is how each one shapes the analysis you see.
Engine 1 · Body Signals
Purdue Body Signal Engine
Purdue University · Canine Welfare Science
Dogs communicate through their whole body — ears, eyes, mouth, tail, posture, muscle tension, and weight distribution. Purdue's Canine Welfare Science framework breaks this down into observable parts so we can describe what a dog is doing, not what we guess it 'means.'
Alexandra Horowitz · Barnard College Dog Cognition Lab · Inside of a Dog
Dogs do not experience the world the way humans do. Their primary channel is smell, not vision. Horowitz's umwelt framework reminds us to ask 'what is this animal sensing from its own perspective' before drawing any conclusion — and to reject anthropomorphic shortcuts like 'the guilty look.'
Three anti-misreading red lines built into every prompt: anti-guilt, anti-translation, umwelt-first
Six context slots inform the analysis: location, trigger, smell context, owner action, history, and baseline
Outputs never include 'your dog says...' translations — we surface signals, not made-up words
Lund University · MEOWSIC (Melody in Human–Cat Communication)
Cat vocalizations are not a phrasebook. The MEOWSIC project at Lund University studies the prosody of cat-human communication: pitch, melody contour, duration, intensity, rhythm, and voice type. We decode those acoustic features instead of inventing translations.
Six prosodic features per vocalization: F0 pitch, melody contour, duration, intensity, rhythm, voice type
Distinguishes meow, trill, purr, hiss, growl, yowl, chirp — each tied to context, not a fixed meaning
Designed to evolve toward per-cat baselines: every cat's voice profile is individual
Earth Species Project · NatureLM-audio · multimodal animal communication research
Earth Species Project's research informs how we approach AI itself: multi-modal evidence (body + voice + context), probabilistic outputs instead of confident translations, and individual-baseline thinking. We treat every observation as a data point, never a verdict.
Multi-modal reasoning: we never let one signal source override the others
Probabilistic output discipline: confidence rarely exceeds 0.85, never 0.90 without textbook signals
Individual baselines: the more you upload over time, the more PetSignal.ai learns what is normal for your pet
The four engines converge on a small set of non-negotiable behaviors that govern every analysis.
We surface signals, not translations
PetSignal.ai never says 'your dog is saying I am hungry.' We describe what is observable and offer plausible causes, hedged.
We refuse the 'guilty look' myth
Lowered head, averted gaze, pinned-back ears are stress and appeasement signals — not moral guilt. Decades of cognition research back this up.
We always offer an out to a professional
When signals suggest pain, fear escalation, or risk to humans, we recommend a licensed veterinarian or certified behaviorist. Software never replaces a clinical exam.
We are conservative on confidence
Single still photos earn confidence in the 0.55–0.80 range. We never claim 0.95 certainty from one image.
Upload a photo or short video. We will surface body signals, voice prosody (when applicable), context reasoning, and — for dogs — an approach-risk level.