AI for applied sciences—such as health, economics/finance, and engineering—must do more than generate answers. It must understand fundamentals, protocols, frameworks, follow real processes, retain application context, and operate within measurable standards and guardrails.
Currently in stealth, starting with health
Expert-labeled datasets grounded in real workflows, edge cases, and constraints—reflecting. Inclusive of non-native english populations and developing economies.
Benchmarks for correctness, robustness, uncertainty handling, standards adherence, and risk behavior—measured against real-world variability.
Fine-tuned models for reaoning on frameworks, protocols and knowledge.
Autonomous systems adapting to the application setting.
AI for Applied Sciences requires optimization for disciplined reasoning over semi-structured domain data, executing real protocols and workflows with measurable adherence to standards.
Pharmacist — Domain Reviewer & Operations Lead
MLE Intern — Datasets & Evaluation Engineering
To apply or inquire, email a brief note and resume to info@tarkiklabs.com