Clinical Decision Support for IVF
Tanit AI is on a mission to elevate fertility practice for doctors and support patients in their parenthood journey.
Tanit is an AI-powered clinical decision support software designed to make fertility treatment decisions more consistent, explainable, and evidence-grounded — with seamless integration into existing clinical systems.
Why it matters
Fertility care involves complex patient profiles, evolving evidence, multiple protocols, and time-critical decisions. Clinics need decision support that improves consistency and quality — without disrupting workflows.
Protocol variability across clinicians
High cognitive load during cycle planning
Need for consistent, evidence-grounded decisions
Follow-up adjustments across evolving responses
Clinical Decision Support, built for IVF care
Treatment protocol support
Decision support across cycle planning, stimulation strategy, and protocol selection — adapted to your clinic's standards.
Decision-making with rationale
Recommendations with interpretable reasoning and traceable medical sources.
Follow-up & adjustments
Support for monitoring response and adapting treatment plans over time, aligned with clinical rules and oversight.
Core use cases
Fertility Specialist
- Decision support during consultation and cycle planning
- Compare protocol options with clear rationale and trade-offs
- Explain decisions in a patient-friendly way (doctor-approved)
Clinical Management
- Standardize practice across physicians and sites
- Embed clinic protocols and governance into decision support
- Improve onboarding and consistency of care delivery
Protocol + Follow-up Adjustments
- Support monitoring and adjustment decisions over time
- Flag inconsistencies versus clinic protocol
- Keep recommendations auditable and explainable
See Tanit in action
How it works
Tanit behaves as an intuitive Generative AI assistant integrated into your clinical environment, designed for rapid adoption with minimal friction.
Fertility reasoning language models tailored to reproductive medicine context
Predictive models (where applicable) to support probability-aware decisions
Traceable sources grounding each recommendation
Interpretability: structured rationale, constraints, and uncertainty signals
Seamless integration with EHR/HIS
Integrates with existing EHR/HIS through interoperable connectors and APIs
Minimal disruption: decision support appears inside the tools clinicians already use
Clinic-controlled configuration: protocols, permissions, and governance
Deployment options
Safety, security, and governance — built in
Human-in-the-loop
Tanit supports decisions; clinicians remain responsible for final medical judgment.
Traceability
Rationale and sources attached to recommendations.
Security controls
Role-based access, audit logs, encryption, and controlled data boundaries.
Responsible adoption
Designed for reliability, accountability, and clinical governance.
Ready to evaluate Tanit in your clinic?
Request a demo or start a pilot with your fertility team.