About Nable AI
Founded in 2025 and headquartered in Mississauga, ON, Nable AI builds workforce scheduling optimization for regulated environments. We serve long-term care homes, post-secondary institutions, and organizations governed by collective agreements, labor laws, and institutional policies.
Our platform translates complex scheduling rules into optimized, compliant, and explainable staffing decisions — replacing spreadsheets with a system that encodes your policies and defends every decision.
Built on Primary Research
We conducted 20+ interviews with schedulers, managers, union representatives, and administrators across healthcare and post-secondary institutions. The finding was consistent:
90% cited scheduling as their primary operational pain point.
Manual processes, compliance exposure, perceived unfairness, and costly overtime were universal challenges. Enterprise tools were too expensive and too slow to deploy. Nable AI was built to fill that gap.
Our Story
We started Nable AI to solve a problem shared across regulated institutions: chronic scheduling inefficiency, compliance risk, and perceived unfairness in shift distribution.
For years, managers in long-term care homes and post-secondary institutions have spent 30–50% of their time on scheduling — navigating collective agreements, seniority rules, overtime limits, and fatigue constraints using spreadsheets that contain errors 88% of the time.
Enterprise workforce management tools exist, but they cost too much, take too long to deploy, and aren't built for the specific compliance requirements of regulated environments. We built Nable AI to be affordable, fast to deploy, and compliant by design.
Our Mission
To provide regulated institutions with scheduling technology that enforces compliance, ensures fairness, and explains every decision — at a fraction of the cost of enterprise alternatives.
Our Vision
A future where every regulated institution — across long-term care, higher education, healthcare, and public safety — has access to scheduling technology that is compliant by design, fair by default, and explainable to every stakeholder.
Our Core Values
Compliance by Design
Every schedule is validated against collective agreements, labor laws, and institutional policies before publication. Compliance is not an afterthought — it's the foundation.
Explainability by Default
Every scheduling decision comes with human-readable rationale and a full audit trail. No black boxes. Managers, unions, and staff can see exactly why decisions are made.
Fairness
Equitable distribution of shifts, workloads, weekends, and holidays — with transparent scoring that supports DEIA objectives and reduces grievances.
Operational Simplicity
Deploy in weeks, not months. No complex integrations required to start. Affordable for institutions that can't justify enterprise WFM pricing.
Privacy
Protecting organizational and staff data is essential. Our platform uses encryption, secure infrastructure, and role-based access controls designed to meet PHIPA, HIPAA, GDPR, and SOC 2 requirements.
Our Founding Team
Yi Jin
CEO & Founder
Instructional design and healthcare education leader with experience supporting complex operational workflows in hospitals and public-sector organizations. Specializes in human-centered design, change management, and workforce training. Brings a practical, user-focused approach to ensuring scheduling tools work for real institutions.
Education: Florida State University (FSU)
Er Jin
CTO & Founding Engineer
AI engineer and technical builder with proven startup experience. Former founding engineer at an AI video platform that raised venture funding. Skilled in machine learning, optimization algorithms, cloud infrastructure, and scaling early-stage products into production-ready systems.
Education: RWTH Aachen University
Zuoyun Zheng
Founding Engineer
Engineer specializing in designing and implementing the algorithms that power Nable AI's scheduling engine. Focuses on translating optimization concepts into production-ready code, building the logic that supports compliance enforcement, fairness scoring, and real-time decision-making across complex scheduling environments.
Education: RWTH Aachen University
Advisors
Jacqueline Silvera
DEIA Advisor — University Health Network (UHN)
Advises on diversity, equity, inclusion, and accessibility in scheduling design and fairness scoring.
Prof. Stefan Decker
Technical Advisor — Fraunhofer FIT
Advises on operations research, optimization algorithms, and AI system architecture.
Ready to explore compliant, explainable scheduling for your institution?
Get in Touch