Vets spending 20% or more of their clinical time on documentation rather than patient care because SOAP notes have to be written from scratch for every visit, often after the client has left?
Wellness recall campaigns sending the same generic reminder to every overdue client regardless of which services are due, the patient's age and species, or their previous response to reminders?
AI for Veterinary Practices
Veterinary documentation is one of the highest time costs in clinical practice -- structured SOAP notes must be written for every consultation, completed accurately, and linked to the patient record before the next appointment. In high-volume practices, this takes vets away from patient care and into administrative work that generalist AI tools handle poorly because they have no understanding of species-specific clinical data, veterinary drug protocols, or multi-species practice workflows.
The AI tools that produce real output in veterinary practice are built around specific clinical tasks -- SOAP documentation, recall prioritisation, risk scoring, imaging triage -- with veterinary clinical context built into the model rather than bolted on as an afterthought.
AI-assisted SOAP note generation
Patient risk scoring
Recall intelligence by species and age band
Front-desk chatbots
RaftLabs builds custom AI tools for veterinary practices and vet groups. We develop AI-assisted SOAP documentation with ambient session capture and species-specific clinical field population, patient risk scoring for dental disease, obesity, chronic disease progression, and senior monitoring frequency, wellness recall intelligence with overdue patient prioritisation by species and age band, diagnostic image triage for radiograph pre-screening, client-facing chatbots for appointment booking and symptom triage, and AI clinical coding with procedure and drug code suggestion from closed SOAP notes. Fixed cost, 12-16 week delivery.
AI +Clinical data integration
·Species-specificClinical context
·FixedCost delivery
·12-16Week delivery cycles
AI for veterinary practices built around specific clinical tasks, not general AI promises
The veterinary practice tasks where AI produces measurable, reliable output are specific: SOAP documentation that follows a defined clinical structure, recall scoring that weights patients by species and clinical history, radiograph triage that flags abnormal findings for priority review, and client triage chatbots that apply species and breed context before giving a response. Each of these tasks has a defined input, a defined output, and a way to measure whether the AI is performing correctly. That is the category of problem where AI is worth building.
General-purpose AI fails in veterinary settings because the context it needs is not in the training data at the level required. Species-specific dosing, DEA controlled substance flags, multi-species practice billing codes, the clinical significance of a finding in a rabbit versus a cat -- these are not things a general-purpose language model handles reliably without fine-tuning and veterinary-specific grounding. We build AI tools with that context included: veterinary drug databases, species-specific clinical logic, and practice management data integration so the AI is working from the patient's actual record rather than generating generic output. The result is tools that vets use because they make clinical work faster and more accurate, not because a vendor has called something AI.
What we build
AI-assisted SOAP documentation
Ambient session capture during the consultation with structured SOAP note generation from the recorded encounter. Species-specific clinical fields auto-populated from the conversation -- the system recognises that a canine orthopaedic examination and a feline respiratory consultation require different structured fields and populates accordingly. Vet review and edit interface before the note is finalised and committed to the patient record. The system does not finalise any note without vet confirmation -- the AI produces a draft, the vet approves it. Reduces post-consultation documentation time for high-volume practices and removes the need to transcribe from memory after the client has left. Integration with the patient record so the approved note appears in the clinical history immediately without a separate data entry step.
Patient risk scoring
Age, species, breed, weight, and clinical history combined into a risk profile for common conditions relevant to the patient's demographics. Dental disease severity score based on previous dental examination findings and time since last dental. Obesity risk flag based on body condition score history and species-appropriate weight range. Chronic disease progression monitoring for diabetic patients -- glucose trend, insulin dose change history, compliance with monitoring appointments. Cardiac patient monitoring frequency recommendation based on disease stage and last echocardiogram date. Senior patient flag with recommended monitoring interval based on species and age. Risk scores surfaced at the point of appointment booking so the reception team can offer appropriate services, and in the patient record header so the consulting vet has the flags before the examination begins.
Wellness recall intelligence
Overdue patient prioritisation by species, age band, time since last visit, and service type due -- rather than a flat list of every patient overdue for anything. Optimal recall timing per patient based on vaccination schedule, wellness plan entitlements, and the patient's historical appointment adherence. Clients who have previously responded to SMS recalls contacted by SMS first; clients who have historically responded to email recalls contacted by email -- channel selection based on behaviour rather than practice preference applied uniformly. Personalised recall message content generated by species and due service rather than a single template sent to the entire overdue list. Campaign performance tracking by patient segment -- species, age band, recall channel -- so the practice can see which recall strategies are converting to booked appointments.
Diagnostic image triage
Radiograph pre-screening for common findings across the clinical areas where the practice has the highest volume. Bone density assessment flagged against species and age-appropriate reference ranges. Soft tissue mass identification on abdominal and thoracic films with location and size recorded in the triage output. Pneumothorax and pleural effusion indicators on thoracic films flagged for immediate vet review. Spinal assessment for intervertebral disc disease indicators on lateral views. The triage output is presented as a flag for vet review with the specific finding and location noted -- not a diagnosis, and not presented to the client. The purpose is to reduce the time spent reviewing normal films and to surface abnormal findings for priority review rather than to replace clinical judgement at any point in the workflow.
Client chatbots and triage
Client-facing chatbot for appointment booking, post-visit care instruction queries, medication refill requests, and symptom triage. Species and breed context captured at the start of every conversation so the triage logic and the responses are specific to the patient being discussed rather than generic. Symptom triage output classified as urgent -- contact the practice immediately -- routine -- book an appointment in the next few days -- or monitor at home with a specific list of signs that should prompt escalation. Medication refill requests routed to the practice for prescribing vet review with the patient record and current medication list attached. Complex queries and upset clients escalated to practice staff with the full conversation context so staff do not ask the client to repeat themselves. Appointment booking directly in the scheduling system from within the chatbot conversation.
AI clinical coding and billing
Procedure and drug code suggestion generated from the content of the completed and approved SOAP note. The AI reads the documented procedures, medications administered, and diagnostic tests ordered and suggests the corresponding billing codes from the practice's price list. Controlled substance flag triggered when a dispensed drug appears in the DEA schedule list -- the dispensing vet is prompted to complete the controlled substance log entry before the invoice is closed. Invoice pre-population from the closed visit note so the billing step starts from a near-complete invoice rather than a blank form. Missed charge identification when a documented procedure or product does not appear in the draft invoice. Reduces manual coding time per consultation and reduces the rate of missed charges in high-volume practices where invoicing is done under time pressure at checkout.
Frequently asked questions
SOAP documentation assistance has the most direct ROI in high-volume practices. If a vet sees 30 patients a day and each SOAP note takes six minutes to write manually, any reduction in that time has an immediate effect on the number of patients the vet can see or the time they recover at the end of the day. Wellness recall intelligence has clear ROI for practices with a large overdue patient population -- prioritising which patients to contact and personalising the outreach by species and due service converts more recalls to booked appointments than a generic batch message. Patient risk scoring has ROI at the appointment level -- surfacing a dental disease flag or a senior monitoring recommendation at check-in increases the rate at which appropriate services are accepted, because the clinical prompt is timely rather than reactive. Diagnostic image triage ROI depends on volume; practices with a high radiograph throughput benefit most.
The output of AI-assisted SOAP documentation is a draft, not a finalised record. Every note produced by the system is reviewed and edited by the consulting vet before it is committed to the patient record. The vet can change any field, add findings the system missed, and remove anything that is inaccurate. The accuracy of the draft improves with species-specific clinical grounding -- a system trained on general speech-to-text will produce a weaker draft than one with veterinary clinical terminology and species-specific field templates built in. In practice, vets find that editing a near-complete draft is significantly faster than writing from scratch, even when a meaningful number of fields need correction. We build with veterinary clinical vocabulary and note structure so the draft accuracy is high enough to make editing faster than writing.
Integration depends on what data your practice management system can expose. If the system has an API, we can connect the AI tools to the patient record, appointment schedule, and billing system so data flows without double-entry. If the system can export data in a structured format, we can build a one-directional integration that reads patient history and pushes outputs back via import. Many veterinary practice management platforms have limited APIs, which we assess during the discovery phase before scoping the build. If you are building both systems with us -- an AI documentation tool and a new practice management system -- the integration is native and designed from the data model up rather than retrofitted.
Multi-species documentation is where the species-specific clinical context built into the system matters most. A generalist transcription tool produces the same output structure regardless of whether the patient is a dog, a cat, a rabbit, or an exotic. The AI documentation tools we build apply species-specific SOAP templates based on the patient record opened at the start of the consultation -- the fields, clinical prompts, and reference values in the note structure are appropriate for the species being examined. Dosing references and drug flags in the clinical coding layer also apply species-specific logic. For mixed practices with small animal, equine, and exotics departments, each department can have its own note templates and coding logic within the same system.
