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Veterinary AI is not clinically credible because a veterinarian appears in a photograph or reviews a launch announcement. Clinical expertise has to influence what the company builds, how the product is tested, how customer feedback is interpreted, and what happens when an output does not meet expectations. CoVet formalized that approach with a dedicated in-house Medical Department.
That is the standard CoVet has set for itself.
CoVet has built substantial veterinary representation across the company. Its current public materials highlight more than 30 DVMs and RVTs across CoVet, alongside an in-house medical team and veterinary professionals working in clinical, product, implementation, support, and customer-facing roles. That breadth matters because clinical input is distributed across functions rather than concentrated in a single advisory role.
Those numbers matter, but the operating model matters more. CoVet's medical team works across product development, clinical review, education, localization, implementation, support, and customer communication. Clinical excellence is not a final approval step. It is an input throughout the company.
This article is published by CoVet and describes CoVet's own team and operating model as of August 18, 2026. Team composition and roles can change. AI-generated records remain drafts that the responsible veterinary professional must review, correct, and approve.
The short answer
Clinical excellence in veterinary AI means that people with current, varied practice experience help shape the product continuously. At CoVet, that includes:
A dedicated in-house medical department led by Co-founder and Chief Veterinary Officer Dr. Mike Mossop
More than 30 DVMs and RVTs represented across CoVet's current public materials
Veterinary professionals working across clinical, product, implementation, support, and customer-facing functions
Veterinary contributors representing general practice, emergency, specialty, equine, large-animal, exotics, mobile, academic, and international settings
Clinical input into templates, workflows, product requirements, testing, quality review, localization, implementation, support, and education
A specialty advisory board that adds perspectives from fields including internal medicine, surgery, radiology, oncology, ophthalmology, dermatology, behavior, emergency care, equine practice, and exotic-animal medicine
That structure does not make any AI system infallible. It creates a stronger route for identifying clinical risk, understanding workflow nuance, and improving the product with the people who will use it.
Why veterinary representation changes product decisions
Veterinary medicine is not one workflow.
A companion-animal wellness visit, an emergency handoff, an oncology recheck, an equine field call, a production-animal visit, and a dental procedure may all create a medical record. They do not use the same language, structure, source material, or sequence of work.
The differences go beyond species and specialty. A locum veterinarian may work in several hospitals with different templates and practice management systems. An emergency clinician may need a record to carry across shifts. A technician or nurse may collect history, document treatments, and prepare client communication while the veterinarian manages the medical plan. A mobile clinician may work without reliable connectivity. An international group may need different terminology, languages, regulatory conventions, and client-facing tone.
Software built around one idealized consultation can look impressive in a demo and still create friction in practice. Clinicians with varied experience are more likely to recognize the edge cases early because they have lived them.
That is why CoVet seeks veterinary input across the company instead of treating one clinical leader as a substitute for broad representation.
What CoVet's medical team does
CoVet's in-house medical department was created to keep clinical reality close to company decisions. Dr. Mike Mossop, CoVet's Chief Veterinary Officer and a practicing veterinarian, leads that work.
The medical team contributes in several connected areas.
1. It translates clinical work into product requirements
Veterinary teams rarely describe a problem in software language. They describe what happened in the room: a finding appeared in the wrong section, a dental notation did not match the clinician's convention, a multi-patient encounter became difficult to separate, a client document used the wrong tone, or a specialty template omitted detail that mattered.
Clinical team members can distinguish among several possible causes. The issue may be a product defect, a template instruction, a documentation preference, a regional convention, an integration limitation, or an AI output that needs further evaluation.
That distinction helps product and engineering teams work on the right problem.
2. It helps design templates and workflows
A template is not simply a page with headings. It reflects how a clinician gathers evidence, organizes reasoning, records decisions, communicates instructions, and supports continuity of care.
CoVet's veterinary professionals contribute to templates for different specialties, species, settings, and document types. They also help shape surrounding workflows such as medical-history review, client communication, dental documentation, multi-day cases, checklists, tasks, and handoffs.
The goal is not to force every veterinarian into one standard note. It is to provide enough structure for consistency while preserving the clinician's judgment and the practice's own standards.
3. It participates in testing and quality review
AI quality cannot be reduced to whether a note generated successfully. Clinical reviewers need to look for factual errors, unsupported additions, material omissions, incorrect attribution, medication or dosage problems, structural mistakes, and output that is technically readable but clinically unhelpful.
CoVet's medical team helps test workflows and review clinical behavior, particularly when products, templates, source material, or underlying systems change. It also works with product, engineering, and quality-assurance teams to turn clinical concerns into reproducible examples and acceptance criteria.
This is an ongoing process, not a one-time certification. AI systems and the workflows around them evolve, so clinical evaluation must continue after launch. That principle is consistent with the NIST AI Risk Management Framework, which treats ongoing measurement, monitoring, and governance as part of responsible AI deployment.
4. It closes the loop with support and customers
Customer feedback is especially valuable when someone can interpret the clinical context behind it.
CoVet's medical team works with support, implementation, and customer-facing colleagues to investigate questions, improve templates, explain safe use, and identify patterns that should influence the product. That work can include feedback from individual clinicians, hospital groups, specialty teams, educators, technicians or nurses, and practice leaders.
The internal loop is practical: listen to the user, understand the clinical workflow, test the behavior, determine what kind of change is needed, and carry the learning back into the product or guidance.
5. It supports localization, not only translation
Clinical language is regional. Terminology, abbreviations, note structure, professional roles, client expectations, and record requirements differ across markets.
CoVet supports more than 100 languages, but language coverage alone is not enough. Veterinary contributors in different countries help the company consider local terminology, accents, documentation norms, and workflows.
The same principle applies within a country. Emergency, specialty, equine, general practice, and production-animal teams may use different conventions even when they speak the same language.
6. It contributes to education and responsible adoption
Good implementation includes more than showing users which button to press. Veterinary teams need to understand where AI helps, where it may fail, what information should be spoken or provided, and why the final record still requires professional review.
CoVet's veterinarians contribute to demonstrations, help content, training, webinars, clinical explanations, and practical examples. The company's in-house medical team also contributes public education on how AI can be used responsibly across veterinary workflows.
That educational role is important because responsible AI use depends on informed users, not just product controls.
How clinical expertise supports accuracy without promising perfection
The word "accuracy" is often used as if it has one universal definition. It does not.
Transcription word accuracy, user satisfaction, edit frequency, factual agreement with a reference, and the absence of negative feedback are different measures. A percentage can look precise while hiding what was scored, which cases were included, and whether a clinically important error counted differently from a punctuation change.
CoVet does not publish a universal clinical-accuracy percentage. Case complexity, audio quality, template design, language, available patient context, clinician style, and the scoring method all affect the result.
Instead, clinical expertise strengthens the systems around quality:
Designing workflows that collect the right source information
Creating instructions that discourage unsupported assumptions
Testing representative cases rather than only ideal examples
Reviewing clinically meaningful errors and omissions
Distinguishing product defects from individual documentation preferences
Adding source links where appropriate so users can verify generated statements
Escalating uncertain, urgent, or high-risk situations to human judgment
Teaching users to review every output before it enters the medical record
Veterinary representation is therefore evidence about how a product is built. It is not proof that every output is correct, and it never replaces the treating professional's responsibility.
Representation has to include the whole veterinary team
Veterinary care is delivered by teams. A product designed only around the veterinarian's final signature can overlook the people who collect histories, prepare patients, document treatments, coordinate follow-up, manage client communication, and keep the practice moving.
CoVet emphasizes veterinary-practice experience across multiple roles, including veterinarians, technicians or nurses, client-service professionals, practice leaders, and implementation staff. That experience helps the company evaluate whether a workflow works for the people around the veterinarian as well as for the veterinarian.
CoVet also works with veterinary technicians and nurses through its team and industry relationships. In its partnership with the Registered Veterinary Technologists and Technicians of Canada, CoVet announced free Support accounts and AI education tailored to veterinary technologist workflows.
Representation should also span career stage, geography, specialty, species, practice type, and organization size. No single veterinarian can represent all of veterinary medicine. The strongest approach is a network of clinical voices with a clear way to influence decisions.
Five questions to ask any veterinary AI provider
A buyer should be able to distinguish meaningful clinical involvement from a marketing claim. Ask:
How many veterinary professionals work across the company, and what roles do they hold? Separate full-time operating roles from occasional advisors or endorsers.
Where does the medical team have decision-making influence? Look for involvement in product requirements, testing, error review, release decisions, implementation, and education.
Which clinical settings are represented? General practice alone does not cover emergency, specialty, equine, large-animal, exotics, mobile, academic, or international workflows.
How does customer feedback reach clinical and product teams? Ask for the process, not confidential examples.
How does the vendor evaluate quality? Require definitions, representative cases, clinically meaningful error categories, and a clear clinician-review policy.
A title or advisory board can add value, but buyers should look for evidence that clinical people influence everyday operating decisions.
CoVet's clinical operating model at a glance
Layer | Clinical contribution | Why it matters |
|---|---|---|
Company leadership | Chief Veterinary Officer and veterinarian co-founder | Clinical priorities have a route into company strategy |
In-house medical department | Product, templates, testing, review, education, support | Clinical work is continuous and operational |
Company-wide veterinary staff | More than 30 DVMs and RVTs highlighted across CoVet's current public materials | Expertise is distributed across functions, regions, and customer workflows |
Clinic-experienced employees | Veterinary-practice experience across clinical, technical, client-service, implementation, and leadership roles | Product decisions reflect multiple roles around patient care |
Specialty advisory input | Specialists, researchers, and field clinicians across multiple disciplines | Edge cases and specialty needs receive direct attention |
Customer feedback loop | Clinicians, support, implementation, product, engineering, and QA collaborate | Clinical concerns can become testable product improvements |
The bottom line
Clinical excellence is not a claim CoVet can complete and move past. It is a way of operating.
CoVet has built its clinical operating model around broad veterinary representation rather than a single clinical spokesperson. The company's in-house medical team, DVMs and RVTs across multiple functions, specialty advisors, and clinic-experienced employees give clinical questions multiple routes into the product and the company.
The purpose is not to remove the veterinarian from the process. It is to build technology that better understands veterinary work, gives professionals more useful starting points, and improves through clinically informed review.
Veterinary AI should be judged by what it does in real practice. The best chance of meeting that standard comes when the people who know the work help shape the system at every stage.
See CoVet's clinical approach in practice
Clinical representation matters most when it changes the product and the workflow. Book a CoVet demo to see how the platform handles your clinical settings, templates, documentation standards, and team workflows.
Frequently asked questions
Who leads CoVet's medical team?
Dr. Mike Mossop, DVM, Co-founder and Chief Veterinary Officer, leads CoVet's in-house medical department. He is a practicing veterinarian and helps connect clinical priorities with product development, education, and company strategy.
How many veterinary professionals work at CoVet?
CoVet's current public materials highlight more than 30 DVMs and RVTs across the company, alongside its in-house medical department and broader clinic-experienced team. Team composition changes over time, so buyers evaluating clinical representation should ask for the current breakdown by role and function.
How should buyers compare veterinary representation across AI providers?
Ask each vendor how many veterinary professionals work in full-time operating roles, which functions they influence, which clinical settings they represent, and how clinical feedback reaches product and quality teams. Public headcounts are useful context, but role depth and decision-making influence matter more than a single number.
Does having veterinarians on the team guarantee accurate AI records?
No. Veterinary expertise improves workflow design, testing, clinical review, support, and education, but no AI system should be treated as infallible. The responsible veterinary professional must review, correct, and approve every generated medical record.
What does it mean that more than half of CoVet has worked in practice?
It means more than 50% of CoVet employees have direct experience working in veterinary practice across clinical and operational roles. That experience includes veterinarians, technicians or nurses, client-service professionals, practice leaders, and other clinic roles.
About the Author
Mike Parent
A longtime veterinary industry entrepreneur, Mike was co-founder of a leading Canadian veterinary telehealth company and was nominated for the EY Ontario Entrepreneur Of The Year award. He now serves as COO of CoVet, bringing firsthand insight into the challenges facing veterinary teams, an operator’s perspective, and a lifelong connection to the profession through his veterinarian mom.
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