HypotheX produces the documented, FDA-traceable answer — the warrant chain behind a high-stakes R&D decision, written to hold up under audit.
Request a scoping call How it worksWhen a model's output becomes a $50M–$500M advancement decision — or part of a regulatory submission — the question is no longer what the model said. It is whether the reasoning behind the conclusion can be defended.
FDA's January 2025 draft guidance made this explicit: AI outputs that support regulatory decisions now require a risk-based credibility framework and a documented Context of Use. Most pipelines do not have that documentation.
We write it. We do not predict outcomes. We do not validate intent. We make decisions defensible — or we show you, precisely, where they are not.
A structured validation of a single high-stakes decision — an advancement choice, a candidate selection, an acquisition thesis. You receive the warrant chain, where it is strong, where it is exposed, and a remediation path: a defensible, documented rationale for a board or a reviewer.
A traceability matrix and warrant-chain documentation mapped directly to FDA's risk-based credibility framework and Context of Use — built for the moment an AI-supported claim approaches a regulatory submission, so the credibility work is done before the reviewer asks.
A pre-filing review of the warrant chain behind an AI-derived invention. We identify where the chain is thin, help strengthen or appropriately qualify claims, and document the path from AI-generated insight to human inventive contribution.
We make decisions defensible — or we show you, precisely, where they are not.
A conclusion is not stronger because a prestigious journal published it, a senior name signed it, or a sophisticated model produced it. It is stronger only if the evidence behind it is sound.
Our entire method holds that line — keeping the source of trust on the content of the evidence, not the channel it arrived through. It is grounded in the published epistemic-admissibility framework of Romanchuk & Bondar (2026) and in mature cross-domain standards for reasoning under uncertainty: intelligence analysis, decision analysis, and the communication conventions of credit-rating and radiology reporting.
We reconstruct the full decision context, surfacing hidden constraints, regulatory anchors, and failure modes the original brief left implicit.
Each piece of evidence is classed — a direct observation, a deterministic computation, or a model/consensus inference. Only the first two can carry an assertive claim.
Competing explanations are tested against the evidence; counter-evidence is actively sought; claims are checked for the failure where a system's authority is bootstrapped from its own outputs.
Each conclusion is labeled by how strongly the evidence licenses it. An AI-generated claim without external confirmation does not reach an assertive label, however fluent it is.
The result is not one number. It is a coordinated set of fields — process quality, evidence completeness, an identifiability tag for what cannot be recovered from public information, structural-gap flags, and a clear action class.
Retrospective, educational demonstrations using public information only. None asserts a finding against any party, predicts outcomes, or claims access to confidential information.
A large pharmaceutical company acquired a dermatology biotech for ~$1.1B, centered on a Phase 3 anti-IL-13 antibody — an asset that had underperformed in asthma, entering a market with an entrenched first-mover.
Our validation surfaced a sound bet whose real risk was commercial, not mechanistic: could a third entrant take share from an incumbent? We marked "comparable-to-incumbent" as a conditional, not established, claim and separated the strategic premium from intrinsic value where the public record could not.
The drug was later approved. Our assessment did not move because of that. This case shows we identify sound decisions — and exactly where they are exposed — not only failures.
Two identically-designed pivotal trials were halted for futility. Months later, one was reframed as positive on a data-dependent reanalysis; the other remained negative. The program advanced toward approval on a surrogate measure, over an adverse advisory-committee vote.
We flagged a structural warrant gap: using futility-stopped data to establish efficacy after the fact is not a question more analysis can repair — only a new, adequately-designed study could address it. We graded it structurally unrecoverable and capped the assessment accordingly.
The program received a (later-reversed) regulatory approval — and our method still graded the underlying decision fragile, ex ante. A favorable regulatory outcome is not a sound warrant chain, and our documentation says so before the record catches up.
A body of published oncology papers carried figure-level evidence that fails an image-authenticity screen — duplications and reuse detectable directly from the published images.
The published warrant for the affected claims is void — a duplicated figure cannot be made authentic after the fact. We separated that from the underlying biology, which becomes undetermined, not disproven. We made no finding of intent: a duplication can arise from manipulation, honest error, or systemic quality-control failure, and these cannot be separated from public information.
Peer review, journal prestige, and senior authorship all signaled trust. A structured authenticity screen surfaced what those channels did not — without crossing into accusation.
Request the full portfolio → Six cases across clinical advancement, M&A, AI-derived therapeutics, and research integrity. Available under NDA.
You have to pick one candidate from several, and your name is on the choice. We give you a documented rationale per candidate — defensible before a board, an investor, or a regulator — so a wrong turn is a diligent decision, not an indefensible one.
FDA's Jan 2025 framework expects a risk-based credibility assessment and Context of Use for AI-supported evidence. We produce the traceability matrix and Context-of-Use documentation mapped to it — before the reviewer asks.
You are evaluating AI and methodology tools and cannot afford to fall behind — but you need to know a method works before you stake a program on it. Start with a single scoped audit: a methodology test, not a leap of faith.
An AI-derived patent can be challenged years later on the strength of its foundation. We document the path from AI-generated insight to human inventive contribution — the record that supports a defense before the challenge arrives.
HypotheX produces structured, traceable documentation for high-stakes pharmaceutical R&D decisions — the warrant chain behind a conclusion, written to be defensible under audit.
Our method is grounded in the published epistemic-admissibility framework of Romanchuk & Bondar (2026) and in mature cross-domain standards for reasoning under uncertainty. We are deliberate about our own honesty: we disclose our operating mode on every report, we never let a later outcome grade an earlier decision, and we mark what cannot be known rather than guessing.
We are independent by design. We work for the integrity of the evidence, not for a predetermined conclusion — which is precisely what makes the documentation defensible.
Romanchuk & Bondar (2026): epistemic-admissibility framework (arXiv 2601.08333; 2601.15059; 2603.03119), cited as theoretical foundation. The HypotheX founder's surname (Bondar) is independent of Roman Bondar of that work; no co-authorship is implied.
A scoping call is 30 minutes. Bring a high-stakes decision — an advancement choice, a candidate selection, an acquisition thesis, an AI-supported claim heading toward submission — and we will show you what a defensible warrant chain looks like for it.