We don't hide how our AI works. We teach you. Follow a real scenario through every layer of the system.
A veterinarian examines a horse showing signs of colic. She opens V.E.T.S. and speaks naturally to Florence, her AI medical assistant:
"Gelding, 12 years, acute abdominal pain, elevated heart rate, no gut sounds on right side."
That sentence enters the system as a Pr Prompt — the structured instruction that tells the AI what to do. The prompt carries context: who's asking, what animal, what urgency level.
The AI doesn't just read words. It converts them into mathematical meaning through Em Embeddings — turning "no gut sounds on right side" into a vector that's mathematically close to "right dorsal displacement" and "large colon impaction."
This understanding is powered by Lg Large Language Models that have been trained on vast medical and veterinary knowledge, then grounded in V.E.T.S. data so they understand your specific context.
Those vectors search the knowledge base through Vx Vector Search — comparing against indexed documents to find the most relevant protocols, case histories, and treatment guidelines.
Florence uses Fc Function Calling to pull the patient's history, check previous treatments, and look up herd-level patterns — structured actions the AI takes on your behalf.
The matching results are assembled through Rg RAG (Retrieval-Augmented Generation) — the pattern that grounds the AI's response in actual V.E.T.S. knowledge rather than generic training data.
Before the response reaches the vet, it passes through Gr Guardrails — checking medication dosages against species-specific limits, flagging drug interactions, ensuring the recommendation doesn't contradict established protocols.
Images and scans — X-rays, ultrasound findings, wound photos — are processed via Mm Multimodal understanding, where the AI interprets visual data alongside text.
Florence operates as an Ag Agent — not just answering a question but orchestrating a multi-step workflow: search, retrieve, check safety, format the response for a veterinarian in the field.
The system continuously improves through Ft Fine-tuning — learning from every correction, every expert edit, every new protocol added to the knowledge base.
All of this is built on proven Fw Frameworks for reliability — battle-tested patterns for prompt engineering, retrieval pipelines, and response generation that ensure consistent quality.
The AI is regularly stress-tested through Re Red-teaming — deliberately probing for weaknesses, hallucinations, and edge cases before they reach real users.
For routine tasks — form auto-fill, quick lookups, simple categorization — efficient Sm Small Models handle the work instantly, saving the heavy reasoning for cases that need it.
Complex cases engage Ma Multi-Agent collaboration — Florence consults Clerk for patient records, Penny for billing codes, and Lassie for herd-level context, all coordinated seamlessly.
Training data is enhanced with Sy Synthetic Data for rare conditions — generating realistic but artificial examples so the AI can learn about uncommon cases without waiting for them to happen.
The system is growing toward responsible Au Autonomy in routine decisions — auto-categorizing records, scheduling follow-ups, flagging anomalies — always with human oversight a click away.
In Interpretability ensures you can always see WHY the AI made a recommendation — which documents it referenced, what confidence it has, and where uncertainty exists.
And for the most complex diagnostic reasoning, advanced Th Thinking Models work through problems step by step — weighing differential diagnoses, considering contraindications, and explaining their reasoning chain.