Facilities & Environment
Overview
Phase I execution is supported by a modern local compute environment and secure, cloud-ready workflows.
PromptGenix Phase I focuses on an evidence-weighted hypothesis intelligence engine that integrates public biomedical datasets and literature. Development is supported by a dedicated local workstation for rapid iteration and deterministic reruns, with an optional secure cloud path (e.g., AWS VPC) for scalability and collaboration testing as needed.
Phase I validation will use public datasets only to avoid IP and data-use constraints, while demonstrating feasibility, reproducibility, and transparent evidence linking suitable for SBIR review.
Local development environment
- Dedicated workstation: high-memory Mac Studio-class system for rapid prototyping and iterative development
- Core toolchain: Python/R for evidence feature extraction, inference, and report generation
- Reproducibility: versioned environments, pinned dependencies, config snapshots, and checksums
- Artifacts: exportable HTML/PDF reports, evidence objects, and audit-friendly logs
Local-first
Deterministic reruns
Evidence objects
HTML/PDF outputs
Secure & scalable deployment option
- AWS-ready architecture: supports deployment in a secure VPC for scale testing and controlled collaboration
- Data separation: Phase I uses public data; sensitive datasets remain external to PromptGenix environments
- Controlled access: role-based access patterns and audit-friendly storage options (as applicable)
- Portability: reproducible configs enable consistent execution across local and cloud contexts
Phase I policy alignment: evaluation uses GEO/SRA/FlowRepository and public literature sources to ensure clean SBIR review.
Resources supporting Phase I
Software, public datasets, and operational supports aligned to SBIR deliverables.
Software stack
- Evidence layer: standardized extraction of effect sizes, uncertainty, reproducibility, and context descriptors
- Inference: statistical scoring + Bayesian updating for posterior confidence estimates
- Interpretability: LLM used only for explanation constrained by evidence links (“no evidence → no claim”)
Validation sources
- Public datasets: GEO/SRA (omics) and FlowRepository (immune profiling)
- Public literature: PubMed/PMC for priors and traceable citations
- Evaluation focus: traceability, reproducibility, calibrated uncertainty, and utility
Operational supports
- Documentation: SBIR portal pages + reviewer-ready evidence-linked reports
- Pilot readiness: structured onboarding and feedback templates for partner labs
- Collaboration: mechanisms to support letters of support and Phase II pilot expansion
Facilities fit to the work
Environment supports rapid prototyping, reproducible validation, and SBIR-compliant evaluation.
| Need | Environment support | Phase I output |
|---|---|---|
| Fast iteration | Local compute enables rapid evidence-layer development, inference tuning, and report refinement | Stable end-to-end hypothesis ranking on multiple public datasets with deterministic reruns |
| Reproducibility | Pinned versions, config snapshots, and checksums; exportable evidence objects and logs | Rerun-consistent outputs; reviewer-ready artifacts with traceability and uncertainty labeling |
| Scalability testing | Cloud-ready execution path (optional) for heavier workloads and multi-user patterns | Feasibility evidence for Phase II secure deployments and multi-site pilots |
Bottom line: The current facilities and environment are sufficient to deliver Phase I milestones
(evidence ingest, hypothesis prioritization, traceable reporting, and measurable validation on public data),
while maintaining a clean path to secure scale-out in Phase II.