SBIR Phase I Overview
Biomedical data generation outpaces scientific decision-making.
NIH-funded programs generate large volumes of multi-modal biomedical data and publications, yet a core bottleneck remains: converting heterogeneous evidence into ranked hypotheses, clear uncertainty, and concrete next-step decisions. Teams often rely on fragmented tools and scarce expert time, leading to inconsistent reproducibility. This is further complicated by generic AI tools that lack formal uncertainty quantification and risk introducing stochastic hallucinations.
PromptGenix: an evidence-weighted hypothesis intelligence engine.
PromptGenix integrates public biomedical datasets and literature into a traceable decision workflow that produces testable hypotheses with confidence ranking and explicit evidence links. Unlike black-box models, PromptGenix computes posterior confidence deterministically via a Bayesian engine. LLMs are strictly constrained to explanation and synthesis under "no evidence → no claim" guardrails.
What is new (technical)
- Evidence objects: standardized representation of signals from public datasets and literature
- Probabilistic ranking: statistical scoring + Bayesian updating to compute posterior confidence
- Traceability: every hypothesis includes “why” and links to supporting/conflicting evidence
- LLM guardrails: narrative generation is evidence-bound (“no evidence → no claim”)
SBIR Phase I scope
- Build: validated prototype that generates ranked hypotheses and reviewer-ready reports
- Validate: traceability, reproducibility across reruns, and usefulness for study decisions
- Measure: time-to-hypothesis, rerun consistency, and pilot “useful” ratings
- Deliver: HTML/PDF artifacts suitable for review and partner discussions
Faster, more rigorous science—without requiring more expert bandwidth.
PromptGenix helps research teams move from “outputs” to “decisions” by producing ranked hypotheses with transparent uncertainty. This enables clearer experimental planning, more reproducible interpretation, and improved reviewer confidence in how conclusions were reached.