A hard gate between your AI and ungrounded output
LLMs confidently cite papers that contradict their own claims. Skippy Ground verifies every medical claim before it reaches your users — returning a calibrated verdict, source citations, and explicit contradictions in a single API call.
Simvastatin combined with clarithromycin significantly increases plasma statin levels via CYP3A4 inhibition.
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Claim verification, AI output grounding, and prior authorization evaluation — same API shape as production.
Clarithromycin is a potent CYP3A4 inhibitor and markedly increases simvastatin AUC — concomitant use is contraindicated.
Simvastatin is a major CYP3A4 substrate; strong inhibitors increase myopathy and rhabdomyolysis risk.
Macrolide CYP3A4 inhibition produces 8–12× increase in simvastatin plasma levels.
Simulated output representative of real API responses. ECE-calibrated confidence: 0.85 confidence = 85% empirical accuracy. Every call generates a SHA-256 hash-chained audit record.
Six endpoints. One API.
Claim verification, output grounding, prior authorization, audit retrieval, safety signal evaluation, and retraction checking — all callable from a single integration.
Verifies an individual medical claim against the evidence base. Returns a four-way verdict (SUPPORTED / CONTRADICTED / CONTESTED / NOT_COVERED), calibrated confidence score, full evidence chain with source tier labels, and conflict detail when sources disagree.
Accepts a block of AI-generated text and scores each extractable sentence independently. Returns per-sentence verdict, grounding score, source attribution, and an aggregate grounded_fraction — so you know exactly which claims in an LLM response are supported and which are hallucinated.
Evaluates a drug-disease pair for prior authorization eligibility against FDA-approved indications, CPIC guidelines, and payer NCD/LCD coverage policies. Returns APPROVE / DENY / HUMAN_REVIEW / INSUFFICIENT_EVIDENCE with a decision_id and SHA-256 snapshot_id for appeals.
Retrieves the full immutable audit record for any prior authorization decision — input claim, verdict, evidence chain, snapshot_id, and timestamp. Hash-chained for tamper-evidence. 7-year retention. Appeals-rationale via /v1/auth/appeals-rationale.
Evaluates whether a proposed drug-event association has post-market safety signal evidence. Draws on FAERS, SIDER, and ONSIDES. Returns signal strength with source citations — used for pharmacovigilance grounding and RWE claim verification.
Flags AI output that cites retracted or expression-of-concern literature. Queries the Retraction Watch database and CrossRef. Returns a retraction_flagged_count and per-citation status — so retracted evidence never silently reaches clinical decisions.
Four verdicts. No silent ambiguity.
Every verification call returns one of four verdicts — with explicit justification. Contested findings are never quietly dropped. Ungrounded claims are never passed through.
Multiple tier-weighted evidence sources agree on the claim. Confidence exceeds the calibrated threshold. Cross-validation confirms source convergence — no material contradictions detected.
The claim directly conflicts with high-confidence evidence. At least one T1 or T2 source returns an opposing finding with higher confidence than the supporting evidence. Confidence in the original claim falls below threshold.
Sources disagree without clear resolution. A confidence penalty (−0.10 to −0.20) is applied. Both sides are surfaced explicitly — with their sources and relative weights. Ambiguity is never silently dropped.
No findings exist in the evidence base for this claim. Reason codes: NO_BELIEFS (claim is outside current domain coverage), LOW_CONFIDENCE (findings exist but fall below threshold), or DOMAIN_NOT_SUPPORTED.
Four source tiers.
Confidence is tier-weighted — not source-counted. A single T1 regulatory label contributes more signal than multiple T4 spontaneous reports. Weights are calibrated against held-out data across 143 medical sources.
Authoritative regulatory labels and evidence-based clinical guidelines. Highest weight. Contradictions from T1 sources drive CONTRADICTED verdicts.
Manually curated small-molecule and pharmacogenomics databases with expert curation. High weight — structural and mechanistic depth that regulatory labels lack.
Systematic reviews and primary literature. Moderate weight — broad coverage but variable quality. Retraction-checked before inclusion in evidence chains.
Spontaneous adverse event and real-world safety signal databases. Lower weight due to reporting bias — informative for signal detection, not mechanism verification.
Sixteen medical domains.
Ground verifies claims across the core domains of clinical AI — drug interactions, pharmacogenomics, prior authorization, and beyond.
LLMs cite papers that don't exist. They confidently assert drug interactions that are contraindicated. They recommend doses outside FDA-approved ranges — and explain why with plausible-sounding mechanism detail.
These are not edge cases. In a clinical context, an unverified claim about a contraindication, a wrong dose, or a hallucinated drug interaction can cause direct patient harm. Skippy Ground is a hard gate — not a soft warning. Ungrounded output is rejected at the API level before it reaches your users.
“0.85 confidence means 85% of claims with this confidence score are empirically accurate — not that the model was 85% certain when it generated the response.”
Ground confidence scores are calibrated against held-out evaluation data. ECE = 0.09 on the 707-item Cochrane benchmark (Gate 1 confirmed passing). The score is a probability estimate derived from evidence quantity, source tier weights, and cross-validation — not a model logit. A model can be highly confident in a wrong answer. Ground is confident only when evidence agrees.
Teams deploying clinical AI
Regulatory-grade by design
Every verification request produces an immutable SHA-256 hash-chained audit record — input claim, verdict, evidence chain, source versions, and timestamp — suitable for regulatory submission without post-processing.
Prior authorization decisions carry a decision_id and snapshot_id traceable to the exact evidence versions used at decision time. Appeals rationale is retrievable via /v1/auth/appeals-rationale. 7-year audit log retention meets FDA 21 CFR Part 11 electronic records requirements.
HIPAA-ready with BAA available. AES-256 encryption at rest, TLS 1.3 in transit, VPC isolation. ECE-calibrated confidence (ECE = 0.09, Gate 1 confirmed) meets FDA SaMD AI/ML guidance requirements.
Other Skippy medical products
Drug-drug interactions cause 30% of adverse drug events. DDI checks all N×(N-1)/2 pairs in one call — explains the CYP mechanism, scores panel risk, and returns verified alternatives.
CPIC-grounded dosing recommendations for 11 pharmacogenes. Ground verifies PGx claims; PGx verifies the genotype-dose relationship that Ground is checking.
Post-market safety signal detection with vigiRank composite scoring and Naranjo causality assessment. Ground uses the same FAERS/SIDER data to verify RWE claims.
Clinical Decision Support · Not a Substitute for Clinical Judgment. Skippy Ground is an evidence-grounded verification tool designed to support clinical AI systems. Verification results, verdicts, and prior authorization evaluations are intended to support — not replace — the judgment of qualified clinicians, pharmacists, and payers. All findings should be evaluated in the context of individual patient history, comorbidities, and local clinical guidelines.
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We work with EHR vendors, health systems, clinical AI developers, and payers. Let's talk about your verification problem.