Commercialization Plan (Phase I → Phase II)
Positioning
Evidence-weighted hypothesis intelligence engine for faster, more transparent scientific decisions.
PromptGenix commercializes a hypothesis intelligence platform that integrates public biomedical datasets and scientific literature to produce ranked, testable hypotheses with probabilistic confidence, explicit evidence links, and actionable next-step recommendations. The platform is designed to support (not replace) human scientific judgment by making evidence integration faster, reproducible, and reviewer-friendly.
Commercial focus: sell the decision product (ranked hypotheses + uncertainty + traceability), not “data analysis as a service.”
Primary customers
- NIH-funded academic labs in immunology, infectious disease, vaccine/HIV research
- Government research groups needing reproducible, auditable decision support (secure/on-prem options)
- Biotech & translational teams prioritizing targets, biomarkers, and experiments under time constraints
- CROs & core facilities supporting multiple projects with standardized, traceable deliverables
Academic
Government
Biotech
CRO / Core
Value proposition
- Faster hypothesis-to-experiment: compress synthesis from weeks to days (or <24h for accession-driven runs)
- Confidence with uncertainty: probabilistic ranking + credible intervals and evidence coverage
- Traceability: every hypothesis links to evidence objects and cited sources (supporting vs. conflicting)
- Reproducibility: versioned configs, deterministic reruns, exportable artifacts
- Flexible deployment: local workstation, secure VPC, or on-prem for sensitive settings
Competitive landscape
Differentiation vs. point tools, workflow frameworks, and general-purpose AI assistants.
| Category | Strengths | PromptGenix differentiation |
|---|---|---|
| Point tools Seurat, GSEA, FlowJo, etc. |
Best-in-class for specific steps; familiar to labs. | Converts outputs into standardized evidence objects and produces ranked hypotheses with uncertainty, reducing manual synthesis and “glue code” across tools. |
| Workflow scripting custom pipelines / notebooks |
Flexible; tailored to a lab’s preferences. | Productized, maintainable engine with deterministic configs and reusable templates; easier onboarding and consistent QA/traceability. |
| Generic LLM assistants | Fast writing/summarization; broad coverage. | Evidence-weighted inference + “no evidence, no claim” guardrails; LLM used for explanation only, constrained by evidence links, citations, and uncertainty flags. |
Business model
Subscription-first, with deployment tiers for security and scale.
Initial go-to-market
- Pilot program: 2–3 partner labs (Phase I) with structured KPIs (traceability, usefulness, reproducibility)
- Letters of support: pilot partners and collaborating PIs to demonstrate Phase II readiness
- Proof assets: public demo reports + reproducible runs using public datasets and clear “confidence outputs”
- Early channel: NIH-funded networks, core facilities, and government groups needing auditable decision support
Near-term traction: pilots → references → paid evaluations → Phase II scale-out.
Revenue options
- Subscription (SaaS): per seat / per lab for hypothesis engine + updates + templates
- Secure tier: customer VPC / on-prem deployment package (government, regulated biotech)
- Compute add-on: managed runs for heavy workloads (optional)
- Services: onboarding, workflow mapping, and support (non-recurring, accelerates adoption)
- Enterprise: multi-team deployments, SSO, audit logging, and expanded governance
SaaS
Secure tier
Hybrid
Enterprise
Milestones
Phase I proves decision value; Phase II scales adoption and revenue.
| Stage | Deliverables | Commercial outcome |
|---|---|---|
| Phase I | Evidence layer + hypothesis decision engine; traceable reports with confidence bands; pilot KPI evaluation; initial packaging for deterministic reruns and audit-friendly artifacts. | Demonstrable decision support value; priceable product scope; partner references and Phase II-ready roadmap. |
| Phase II | Expanded modality coverage and dataset breadth; improved UI and automation; multi-site pilots; governance and secure deployments; repeatable onboarding and support workflows. | Paid subscriptions and secure deployments; repeatable customer acquisition; scalable revenue and partnerships. |
Commercial thesis: Customers adopt PromptGenix when it demonstrably reduces hypothesis generation time,
improves transparency and reproducibility, and produces confidence-ranked hypotheses that directly inform study design decisions.
Phase I establishes technical feasibility and reviewer trust using public datasets; Phase II will scale PromptGenix to additional disease areas, expand decision-engine calibration, and convert pilot partnerships into revenue through subscription and secure deployment tiers.