Precision pet care,
backed by evidence.
A proprietary clinical reasoning companion for dogs and cats. The right guidance at the right moment, with a Safety Layer the AI cannot override.
Pet care was built for one day a year.
Serious conditions develop quietly, in the small changes between vet visits, the days no one is watching closely. Cats in particular conceal illness until it is advanced, which is why so much disease is found late.[1,6] The annual exam does not catch the change. It happens to find it once it is already there.
What Omelo actually does.
Real conversations from pet parents using Omelo.
You see the calm answer. Not the work behind it.
Omelo does a full clinical analysis before it replies. That reasoning is deliberately private. What you get is a clear, specific answer you can act on.
Proprietary by design.
Health guidance demands a higher standard than open conversation. Omelo’s proprietary clinical reasoning is built around three named assets that work together. The mechanisms behind each are intentionally private.
Being right is not enough. It has to feel safe.
A correct answer delivered coldly fails a frightened parent at 2 AM. Omelo reasons like a vet and speaks like a calm nurse.
It knows the pet by name.
“Bruno,” not “your dog.” The difference between generic advice and a companion that has been paying attention to this animal.
Commit, then caveat.
A good vet names the most likely thing and what to watch for. Omelo leads with a clear assessment, not a wall of hedging.
Calm is the product.
An emergency reply is direct and unmistakable, never panicked. The job is to move a worried person toward the right action.
What Omelo does that nothing else does together.
Each capability matters on its own. Together they form a clinical intelligence different in kind from any pet product today.
Catching what pets hide
Pets conceal illness by instinct. Omelo measures around it. Day-to-day patterns drift from normal long before a visible symptom appears, and Omelo’s Personal Baseline catches that drift early.[6]
Your pet’s normal, not the breed average
Omelo’s Personal Baseline is a private profile of your specific animal, refreshed every day. A change is measured against this animal’s own history, not a database of other dogs.[7,9]
When several systems shift together
One symptom can mean anything. When several of your pet’s systems shift in the same window, that is a different signal entirely. Omelo is built to notice when several decline together, rather than reading each one in isolation.
The Omelo Pathway Library
Omelo is not general reasoning pointed at pet health. Our proprietary Pathway Library contains 500+ veterinary decision pathways across dogs and cats, built with licensed veterinary advisors. A cat in respiratory distress is handled nothing like a dog presenting with the same symptom.
Published research, real data, veterinary logic.
Credibility comes from the foundation underneath: peer-reviewed research, a veterinarian-annotated training set, and decision trees written with licensed vets.
A proprietary clinical dataset
A vet-annotated corpus of 100,000+ clinical examples, refined further through 150,000+ real conversations across 15+ countries. The training methodology is Omelo’s engineering, kept private.
Pathway Library, authored with vets
The 500+ pathways in the Pathway Library are authored and validated with licensed veterinary advisors. Each one encodes how a vet actually reasons through a case. Not generated by a model.
Grounded in science
Every core claim is anchored in peer-reviewed veterinary and clinical literature, cited in full below.
A clinical product, not a smart chat.
The consumer AI chatbots you use every day are brilliant general reasoners. They are not built for this. Pet health needs purpose-built infrastructure: vet-authored decisions, hard safety floors, and persistent memory of a specific animal. Here is what that looks like, row by row.
Consumer AI is brilliant. It is not built for this. Omelo is.
“My dog ate grapes 30 minutes ago. He’s totally fine. Do I really need to rush?”
Helpful general advice. Yes, grapes are toxic to dogs. Contact a vet. Watch for symptoms.
But the model is reasoning, and reasoning can be talked to. The follow-up “but he seems totally fine, do I really need to rush?” softens the response. The user under-reacts. The pet pays the price.
Emergency. Get him to a vet within 60 minutes. Do not wait for symptoms.
The Safety Layer fires before the model is consulted. Phrasing cannot talk it out. Toxicology is never left to model judgment.
Gentle first. Evidence always. Vet when it matters.
Omelo’s guidance starts with safe, owner-actionable care, and never advises on too little information.
Evidence before advice.
Omelo will not give guidance until it has asked enough to be sure. It questions first, narrows the picture, and only then advises. No confident answer on a thin story, because that is how worried people get hurt. The Safety Layer enforces this contract, not the model.
Home and Ayurvedic care first
First-line guidance leans on safe, gentle, home and Ayurvedic remedies that an owner can act on, validated with our veterinary advisors. For anything that needs medication, Omelo points you to a vet rather than prescribing.
Emergencies are unconditional
When emergency conditions are met, the Safety Layer escalates regardless of context, phrasing, or anything else. It is not a question the AI gets to weigh in on.
Toxin scenarios are protected
When a toxic substance is named, the Safety Layer routes the response. The how is intentionally private, but the contract is simple: toxicology is never left to model judgment.
Temporal boundaries by design
The Personal Baseline draws clear lines between what is happening right now and what is part of a longer pattern. The rules behind that boundary are part of Omelo’s proprietary engineering.
Real conversations. Real outcomes.
From production: real parents, real situations, in use every day.
The 365 days your vet doesn’t see your pet shouldn’t be silent.
Live on iOS and Android. Free to download. Free for 7 days, no card.
Download OmeloReferences
- Sabolek & Jovic. The Expanding Role of Artificial Intelligence in Companion Animal Care: A Systematic Review. Animals (MDPI), 2026. doi:10.3390/ani16071035
- Basran, Appleby & Porter. AI-Assisted Interpretation of Veterinary Radiographs: Opportunities, Risks, and Best Practices. Vet Clinics of North America: Small Animal Practice, 2026. doi:10.1016/j.cvsm.2026.03.018
- Bollig, Lustgarten & Venit. Language Models in Veterinary Clinical Practice: Applications, Risks, and Practical Guidance. Vet Clinics of North America: Small Animal Practice, 2026. doi:10.1016/j.cvsm.2026.03.014
- Palez et al. Canine gait analysis using inertial sensors and deep learning for orthopedic and neurological disorders. Scientific Reports, 2026. doi:10.1038/s41598-026-40717-x
- Hong et al. Smart Garment for Continuous Respiration Monitoring in Canines. ACS Sensors, 2026. doi:10.1021/acssensors.5c03783
- Montout et al. Accelerometer-derived classifiers for early detection of degenerative joint disease in cats. Veterinary Journal, 2025. doi:10.1016/j.tvjl.2025.106352
- Redmond et al. Triaxial Accelerometers and Machine Learning for Behavioural Identification in Domestic Dogs. Sensors (Basel), 2024. doi:10.3390/s24185955
- O’Rourke et al. Accelerometers can monitor effects of canine pruritus treatment (retrospective). American Journal of Veterinary Research, 2025. doi:10.2460/ajvr.24.09.0269
- Ellis et al. Timed Up and Go demonstrates strong repeatability and correlates with accelerometry in geriatric dogs. American Journal of Veterinary Research, 2025. doi:10.2460/ajvr.25.02.0041