Commercialization Plan (Phase I → Phase II)
Positioning
Evidence-weighted hypothesis intelligence 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.
Key design choice: LLMs do not set confidence. Confidence is computed by an evidence-weighted Bayesian decision engine;
the LLM only explains rankings using linked evidence objects and citations (supporting or conflicting), with uncertainty explicitly stated.
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 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 + 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 (Phase II+).
Bayesian summary:
Posterior confidence = P(H|E) ∝ P(H) × Πᵢ P(Eᵢ|H), reported with CrI + support/conflict coverage.
Business model
Revenue streams
- SaaS subscriptions (Phase II): seat-based tiers for labs and core facilities.
- Enterprise / Gov deployments: secure VPC or on-prem licensing + support.
- Services-to-product bridge: limited pilot engagements that convert into recurring subscriptions.
Commercial focus: sell the decision product (ranked hypotheses + uncertainty + traceability), not “data analysis as a service.”
Pricing anchors (Phase II targets)
- Lab tier: $499–$1,499 / month (small team, limited runs, standard reports).
- Core/CRO tier: $2,500–$7,500 / month (multi-project, higher throughput, audit trail).
- Enterprise: custom (secure deployment + integrations + SLAs).
Competitive landscape
Differentiation vs. point tools, workflow frameworks, and general-purpose AI assistants.
| Category | Strengths | PromptGenix differentiation |
|---|---|---|
| Point tools |
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 |
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. |
Go-to-market
Initial channels (Phase I → II)
- Pilot studies: 2–3 public-data demonstrations packaged as reviewer-ready reports.
- Academic adoption: early adopters via immunology/infectious disease communities and core facilities.
- Government pathway: reproducibility + auditability positioning for secure deployments.
- Content funnel: publish non-sensitive demos and technical notes using public datasets.
De-risking milestones
- Phase I: deterministic evidence objects, posterior confidence outputs, traceable reports, and usability KPIs.
- Phase II: scale to multi-dataset/multi-site cohorts, secure deployment options, UI automation, integrations.
- Commercial traction: convert pilots into paid subscriptions; expand to CRO/core facility customers.
Buyer confidence trigger: consistent ranking rationale across reruns + explicit support/conflict coverage + calibrated uncertainty.
Near-term KPIs
Product KPIs
- Time-to-report: <24h for accession-driven runs (dataset-dependent).
- Reproducibility: deterministic reruns with versioned artifacts and checksums.
- Traceability: every hypothesis links to evidence objects + cited sources.
- Uncertainty labeling: credible intervals + support/conflict/missing flags.
Commercial KPIs
- Pilot conversions: pilot → paid subscription conversion rate.
- Retention: repeat use across projects and cohorts.
- Willingness-to-pay: pricing validation by segment (lab vs core/CRO vs enterprise).
- Security readiness: roadmap progress toward VPC/on-prem deployments (Phase II).
Phase I → Phase II bridge: Phase I proves feasibility and reviewer trust (traceable Bayesian confidence + deterministic artifacts).
Phase II productizes secure deployment, scales cohort complexity, and expands integrations to drive recurring revenue.