Investor Relations
The system of record for changing claim supportability in life sciences.
NextConsensus builds the first platform that evaluates approved medical claims against changing evidence, identifies which claims need re-review, and produces source-traced assessments for qualified human review. The compounding dataset of how institutions respond when evidence moves is the asset.
Pre-seed stage. No external capital raised yet. Building in public with Refract (open core).
The thesis
Medical evidence moves faster than the institutions that rely on it. Approved claims circulate across field materials, payer decks, clinical pathways, and compliance records. Between review cycles, new studies, label updates, guideline shifts, and safety signals change what the evidence supports. Teams lose track of which relied-on claims are still current.
The result: stale claims persist, overbroad wording survives longer than it should, and qualified reviewers spend time searching instead of deciding.
The wedge: claim maintenance
The first product helps teams identify which approved claims may deserve re-review as evidence changes. Send the approved claims your team already uses. NC maps the source record, compares new evidence movement against the prior record, and returns a ranked re-review list with claim-specific assessments. Each assessment traces what changed, which claim it may affect, and why.
NC owns evidence-impact assessment and re-review recommendation. The customer owns final disposition and action. This is a structural boundary, not a disclaimer.
The compounding asset
Every assessment produces a labeled record of what changed, how institutions responded, and what the evidence supported at that point in time. The dataset of how institutions respond when evidence moves is the asset.
The model it enables is the company. Manage for labeled transitions per month, not ARR.
- 1 Monitoring wedge
Identify when changing evidence may affect approved claims. Produce source-traced assessments for qualified review.
- 2 Reliance memory
Record how institutions respond when evidence moves. Build the dataset of claim-state transitions across the industry.
- 3 Reference layer
Become the system of record for the changing supportability of evidence-backed claims institutions rely on.
Why now
Evidence generation accelerating (RWE, adaptive trials, AI-discovered targets)
Review capacity static — widening gap
FDA RWE framework, EU HTA regulation, payer scrutiny all tightening
Medical Affairs, MLR, Market Access, Compliance all mandated to defend claims
No existing system owns claim maintenance end-to-end
Technical moat
Refract (deterministic claim-history engine): Open-core observation pipeline, temporal snapshotting, claim-provenance primitives
Temporal integrity: Evidence outside review window rejected; no hindsight leakage
7-dimensional support state: Evidence strength, scope fit, wording fit, authority alignment, uncertainty, review urgency, institutional disposition
Canonical claim identity: Reconciles "Drug X improves outcomes in Y" (2019) vs "Drug X demonstrates benefit in subgroup Y" (2024)
Deterministic reproducibility: Every assessment SHA-256 hashed; same claim, same data, same window = same result
Open-core leverage: Refract engine open source → developer adoption, standards influence, talent pipeline, integration surface
Business model
Pilot: flat-fee approved-claim re-review across 100–300 claims. Fit check confirms measurable output before pricing.
Persistent assets after pilot: claim history, evidence map, decision history. Renewal driven by labeled transitions per month.
Open-core Refract creates compounding leverage: developer adoption → standards influence → talent pipeline → integration surface.
Team
Rare domain × technical depth: Medical Affairs + Regulatory + Evidence Synthesis + ML Engineering + Systems Engineering.
Not AI tourists. Built Refract (deterministic claim-history engine), evidence-ingestion pipelines, measurement pipelines, operator workbench.
Open-core leverage (Refract)
Refract is the open-source deterministic claim-history engine. It provides the observation pipeline, temporal snapshotting, and claim-provenance primitives that NC builds on.
Developer adoption → standards influence → talent pipeline → integration surface. Commercial layer captures value on top.
Request the investor deck.
We'll send the full thesis, traction metrics, and technical architecture.