AI finds the content. Humans make it meaningful.
BarkMind uses AI to continuously discover real canine behavioral incidents from across the web — grooming resources, veterinary journals, training blogs, YouTube — and surfaces them here for practitioners to annotate with their expertise.
The AI aggregates. You provide the intelligence. Every tag you confirm, every observation you file, every debate you start with another expert — that's the dataset. That dataset trains the behavioral AI that will eventually help every dog professional in the world. Open source means your expertise is permanently attributed to you, building something that outlasts any single platform.
Not a pet app. A research platform.
Behavioral Review Cases
Structured submission of real behavioral incidents with media, context, taxonomy annotations, and timeline markers. Not anecdotes — labeled data.
Expert Consensus System
Verified professionals submit formal verdicts. Multi-expert consensus resolves complex cases. Disagreement is structured, not suppressed.
Immutable Dataset
Resolved cases are locked with evidence snapshots. Every annotation carries author, credentials, confidence level, and revision history.
Role-Based Trust
Community observes. Experts resolve. Admins govern. Anyone can annotate; no one can fabricate credentials. Trust is earned, not assumed.
How the platform is built
BarkMind is a governed ecosystem service running under the Aegis AI control plane. Every component is observable, restartable, and auditable.
Network Layer
Application Layer
Data Layer
Governance Layer
22
DB Tables
73
Taxonomy Terms
13
Capabilities
From submission to dataset entry
Case Submitted
Any user. Media + description + behavioral context.
Community Annotates
Any user. Tags, observations, interpretations with confidence levels.
Expert Assignment
Admin or expert claims the case for formal review.
Expert Resolution
Formal verdict: Safe / Concern / Escalation Risk / Requires Intervention.
Consensus (if needed)
Multi-expert opinions aggregate. 50% majority triggers consensus verdict.
Evidence Lock
Case frozen. Immutable snapshot taken. Becomes a dataset entry.
73-term controlled taxonomy across 14 categories
Annotations don't have to be free text. BarkMind provides a structured vocabulary of observable behavioral signals — each with a severity hint (0–4) and signal type classification. This is what makes the dataset machine-readable.
The long-term vision
Phase 3: Multimodal Analysis
Frame extraction from video. Claude API integration for per-frame behavioral annotation. Expert-validated frame labels.
Phase 4: Escalation Prediction
Behavioral risk scoring trained on expert-resolved cases. Not autonomous — human-in-the-loop. Confidence-bounded.
Phase 5: Research Release
Open dataset release with full contributor attribution. The first structured canine behavioral dataset for ML research.
Founded by Jesse Boudreau & Darcee Sellers
Co-Founder
Jesse Boudreau
Product Lead · Principal Architect
Responsible for platform vision, AI architecture, engineering strategy, software development, and product direction.
Co-Founder
Darcee Sellers
Canine Behavior Consultant · Customer Experience Lead
Responsible for canine behavior expertise, pet care consulting, workflow design, customer experience strategy, product validation, documentation, and operational planning.
Together, Jesse and Darcee founded BarkMind to improve canine behavior understanding, pet care operations, and outcomes for pets, pet parents, trainers, groomers, boarding facilities, and veterinary teams through responsible use of AI and technology.
Ready to contribute to the future of canine behavioral science?