Why it matters
Without AI review, feedback tends to vary between coaches, milestones may be missed, client questions escalate, and progress records become opaque, leading to disputed outcomes, CPD gaps, and welfare oversight challenges.
From design to safety
Design
AI-driven criteria design and intake
The system reads intake forms, existing homework samples, and programme criteria to shape feedback rubrics, scoring thresholds, and coaching templates for the class curriculum, including threshold values and exemplar videos as references.
- owner video portal
- criteria library
- feedback rubric
Build
System integration and data flows
During build, connect owner video uploads, wearables readings, and coaching notes to a central analytics engine, establishing data governance, retention schedules, and security checks, plus audit trails for every upload.
- video capture hub
- wearable data bridge
- progress log schema
Operation
Real-time review and coaching outputs
In operation, the tool analyzes incoming owner videos, flags deviations from thresholds, and generates feedback notes, suggested cues, and practice plans for next sessions, with auto-generated client-facing summaries.
- feedback engine
- criteria checks
- progress tracking
Maintenance
Ongoing data quality and model refresh
Regularly audit video quality, sensor data, and rubric relevance; retrain models on new course materials, and refresh coaching templates and client-facing reports, maintaining versioned changelogs and stakeholder approvals.
- quality metrics dashboard
- model refresh scheduler
- report templates
Safety
Welfare and safety oversight through reviews
All feedback and progress logs are reviewed by a qualified trainer and welfare officer to ensure safety compliance, ethical standards, and client transparency, with incident logging linked to Canine Good Citizen criteria.
- safety oversight portal
- welfare review logs
- incident flags
Part by part
- 1
owner video portal
- Watch for
- uploads missing metadata, inconsistent file naming, or corrupted files that hinder analysis.
- AI action
- flag for manual check and request re-upload when issues are detected.
- 2
criteria library
- Watch for
- outdated criteria not aligned with current programme outcomes or LIMA guidelines.
- AI action
- trigger update workflow and notify head trainer.
- 3
feedback rubric
- Watch for
- ambiguous scoring, lack of escalation paths, or missing competency criteria.
- AI action
- validate against rubrics and suggest standardized language.
- 4
video capture hub
- Watch for
- low resolution, unstable connections, or missing frame rates that degrade analysis.
- AI action
- request higher quality stream and adjust encoding settings.
- 5
wearable data bridge
- Watch for
- misaligned sensor types, gaps in time stamps, or non-synchronized events.
- AI action
- re-synchronize data streams and flag gaps for review.
- 6
progress log schema
- Watch for
- incomplete session notes, duplicate entries, or generic feedback.
- AI action
- enforce structured fields and auto-fill based on analysis.
Worked example
Video Homework Review for Puppy Heelwork
Illustrative scenario with sample data.
An owner submits weekly heelwork video; trainer requests standard stance, signal fluency, and heel-position criteria.
Signal in
owner_id: 421, dog_id: 88, session: 5, video_url: vdo/owner421/dog88/session5.mp4, rubric_version: v2, wearables: on, timestamp: 2026-11-08T10:00:00Z
System out
alert: heel cue drift detected; suggested cues adjusted; practice plan generated; notes stored under dog88_session5; progress log updated with session rating and trend line.
Human sign-off: Head trainer reviews the AI notes, approves the suggested plan, and signs off on the client-facing progress report; veterinary input considered if any medication interactions are noted.
How to roll it out
Phase 1
Weeks 1 to 4: Baseline
Calibrate data feeds, define initial rubrics, and establish owner onboarding workflows, plus alignment with CPD tracking and data governance.
Phase 2
Weeks 5 to 8: Pilot
Run pilot with a subset of classes, refine criteria checks, test reporting templates, and verify data integrity across devices and uploads.
Phase 3
Weeks 9 to 12: Scale
Roll out across all classes, integrate with CPD records, enforce data governance discipline, and archive legacy data for audits.
Where people stay in charge
- Decisions about welfare, safety thresholds, and trainer qualifications must stay with a certified trainer or welfare officer.
- All medical, medication, and veterinary considerations remain under veterinary supervision or veterinary behaviourist guidance.
- Data privacy and retention decisions must be approved by the data governance lead with quarterly audits and documented rationales.
Questions owners and crews ask
How does AI help coaches provide consistent feedback to clients?
AI standardizes feedback language, flags criteria gaps, and creates practice plans; human staff review and tailor advice for each dog and owner.
What data is collected from owner videos and wearables?
Video metadata, pose annotations, cue timing, and wearable sensor readings are collected and stored with time stamps for traceability.
Who reviews flagged issues before clients see them?
A head trainer or certified behaviour consultant reviews AI flags, then finalizes client-facing notes.
How is privacy protected in client video submissions?
Access controls, encryption at rest and in transit, and limited retention align with data governance policies and consent terms.