Meet Yukti.
Our first AI decision model
focused on healthcare.
Built around focused YES / NO answers, because some
questions need clarity without a lengthy conversation.
Less output. More focus.
Not every answerneeds a paragraph.
Fine-tune an existing LLM on medical data for defined tasks.
Less generated text. A more focused output.
Conceptual architecture · not live
YuktiModel researchMedical AI
A short answer stillhas to be correct.
Evaluate medical accuracy, sensitivity and specificity across question types.
Compare output tokens and latency with the same base model.
Medical AI evaluation: efficiency alongside reliability.
Task-specific fine-tuningresearch
KareOS integrationfuture
Connected.
Yukti is a healthcare decision model research project by Kraionyx AI. It explores task-specific fine-tuning and focused Yes/No answers for defined healthcare research questions. The research preview is not publicly available yet.
In this research, a healthcare decision model evaluates information against a defined task and explicit criteria. Whether its answers are reliable must be established through evaluation; a short answer alone does not establish correctness.
Yukti explores whether task-specific fine-tuning can produce shorter answers for defined questions. Accuracy, sensitivity, specificity, latency, and output tokens still require reproducible comparison with a base model. No performance advantage is established by this waitlist.
Yukti is currently in research development. Join the waitlist above for updates when the research preview is ready. Public chat and developer APIs are not available in this launch.
For each task, define Yes and No against explicit clinical criteria. No does not automatically mean safe; Yes does not automatically mean disease present. The mapping must stay consistent for that task.
Yukti is a research initiative, not a substitute for professional medical judgment. Clinical use requires validation, safety assessment, and regulatory review. Missing information and uncertainty need a defined handling process before deployment.