AI decisions that survive a regulatory audit
Every Skippy output carries source lineage, calibrated confidence, and a cryptographic audit trail — traceable from the response all the way back to the primary source. Built for the EU AI Act, FDA, CMS, OMB M-24-10, and court-of-record evidence standards.
What LLMs can't provide — and why bigger models won't fix it
These are not accuracy bugs. They are properties of how transformer training works. Regulators don't ask “how accurate is this?” — they ask “show me your evidence chain.”
No evidence chain
When an LLM produces a regulated claim — clinical, legal, or financial — there is no traceable path to a source. The claim came from blended training data, millions of documents indistinguishably mixed. There is no source document ID to show a regulator, judge, or auditor.
No calibrated confidence
LLM confidence is expressed through hedging language, not evidence. A claim backed by 1,000 randomized trials and a claim backed by one case report — or a controlling Supreme Court holding versus a single district-court dictum — produce equally confident-sounding outputs. There is no way to tell them apart.
No knowledge boundary
LLMs have no structural "I don't know" state. They generate plausible text past the edge of their knowledge, silently. In any regulated context — clinical, legal, financial, federal — confident hallucination is more dangerous than an honest refusal.
What a Skippy response provides
Every output is gated by a verifier before delivery. The citation target is not a URL or a document chunk — it is a specific, versioned finding that can be walked all the way back to the evidence it rests on.
Built for the person who gets audited
Audit-ready output, every time
- Every claim carries a source ID and evidence count — no assembling evidence after the fact
- EU AI Act Article 13 and CMS-0057-F documentation generated automatically
- Cryptographic audit trail satisfies court-of-record and regulatory review standards
Evidence you can show the clinician
- Recommendations link to primary sources — PMID, guideline version, and date
- Calibrated confidence distinguishes a strong RCT from a case report
- NOT_COVERED returns honestly when evidence is insufficient — no fabrication
Drop-in verifier, not a model replacement
- REST API with structured JSON — verdict, confidence, source lineage, audit ID
- Open-source skippy-verify runs offline to check any response cryptographically
- P99 latency < 80ms; no PHI retained beyond the API call
What Skippy will not do
Trust is built as much by what a system refuses as by what it claims. These are not configuration options — they are structural constraints baked into the verifier contract.
When evidence is insufficient, Skippy returns NOT_COVERED — a real API response, not a hedged paragraph. It does not generate plausible-sounding text past the evidence boundary.
Skippy verifies and grounds claims — clinical, legal, financial. It does not create de novo recommendations or final decisions. Professional judgment stays with the clinician, lawyer, or regulator — always.
Output that fails the verifier gate is blocked at the API level — not softened, not flagged for review. The contract is hard. There is no override path.
Request context is not stored beyond the API call. PHI, MNPI, privileged communications, and classified content stay in the request — they do not enter Skippy's logs, training pipeline, or storage layer.
What Skippy ships
Each product is a specific API for a specific regulated problem — backed by the same verifier, the same evidence base, and the same audit trail. Across medical, legal, and government deployments.
CMS-0057-F compliant prior authorization with traceable rationale — every denial cites a specific, versioned clinical criterion.
Verified case citations, accurate quotation, and on-point precedent application — no hallucinated case law in your filing.
FedRAMP-authorized deployment for federal health agencies — shared evidence base, agency-scoped isolation, OMB-ready audit trail on every decision.
FDA CDS, EU AI Act Article 13, ONC HTI-1, CMS-0057-F, and HIPAA — checked automatically. Structured certificate, not a report.
From API key to audit-ready in a week
Skippy is a REST API — not a platform migration. You bring the query; Skippy returns the verified finding, confidence score, source lineage, and signed audit trail. No model fine-tuning, no RAG pipeline to build, no evidence base to maintain.
Active deadlines across regulated domains.
The nearest is 3 months away.
EU AI Act Article 13 enforcement begins August 2026. These are not future risk — they are active enforcement timelines with penalties up to 3% of global annual revenue. LLMs cannot structurally satisfy any of these requirements.
EU AI Act — Article 13
EnforcingTechnical documentation of knowledge base, how outputs are derived, and audit records for every decision. Non-compliance: up to 3% of global annual revenue.
CMS Prior Authorization Rule
ActiveAI-driven PA rationale must be traceable to specific, publicly available, versioned clinical criteria. A black-box model cannot satisfy this requirement.
OMB M-24-10
ActiveFederal agencies deploying high-risk AI must maintain a compliant audit trail and provide citizens the right to request human review and the evidence basis for AI-assisted decisions. Applies to CMS, VA, NIH, FDA, OPM, and 10+ other agencies.
FDA SaMD AI/ML Action Plan
ActiveSoftware as a Medical Device must support independent clinician review of the evidence basis. A system that cannot show its evidence chain loses the CDS exemption.
Explore by domain
331 sources · 3 live domainsMedical, Legal, and Government are live with verified evidence bases. Finance, Scientific Research, and others are in active development.
Medical
LiveClinical evidence, genomics, pharmacology, oncology, and rare disease.
Legal
LiveUS Code, CFR, EUR-Lex, ECHR, UNCITRAL, and international treaty bodies.
Government
LiveFedRAMP-authorized federal deployment for CMS, VA, NIH, FDA, OPM, and 15+ health agencies. OMB M-24-10 audit trails on every AI-assisted decision.
More coming
In devFinance, Knowledge, Science, Linguistics, Psychology, and Mathematics — each domain built to the same depth before shipping.
See roadmap →Ready to see it in your domain?
We work with health systems, life sciences companies, legal teams, federal agencies, and financial institutions. Let's talk about your evidence problem.