HEOR · Payers · RWE
Build the value story without exposing the evidence.
HTA dossiers, payer value narratives, cost-effectiveness and budget-impact models, real-world evidence on patient-level data: the work that lives under NDA and can't legally be handed to an AI. Untraceable cloaks every confidential value before the model sees a thing, so you get frontier AI on the evidence payers actually scrutinize.
The suite, in your workflow
Every tool, mapped to the work you already do.
The same eight-tool suite that runs on patent-pending cloaking: pointed at HEOR, payer and RWE deliverables.
Cloaking
Cloak patient-level RWE, endpoints and proprietary model inputs before any AI sees them.
VaultScribe
Draft and rewrite HTA dossiers, payer value narratives and systematic-review write-ups on cloaked evidence.
VaultStat
Generate pseudo-IPD and auto-produce statistical code on cloaked patient data: survival, indirect comparisons, NMA, descriptive statistics.
VaultDelphi
Pressure-test high-stakes assumptions, such as comparator choice, extrapolation, ICER drivers, across up to four models.
Blueprint
Beat the blank page on HTA and payer dossiers with compliance-aware structure.
VaultCanvas
Assemble cost-effectiveness and budget-impact reporting from a single Excel source of truth.
VaultChat
Ask quick questions across a cloaked cost-effectiveness report or SLR.
VaultAudit & Untraceable Protocol
Certify cloaked AI use for HTA and payer scrutiny; keep an audit trail of every value, cloak and submission.
The Grounding Codex
Grounded in your field, not just a model's training data.
VaultScribe writes on Hybrid RAG plus a Grounding Codex, a human-curated source corpus where critical guideline information is organized to augment AI intelligence while remaining human-anchored. Every entry is AI-drafted, then reviewed and iterated against a health economist's direct judgment until it holds up, not machine-scraped, not left unchecked. For HEOR that means a reference-preserving layer of 200+ entries on HTA guidelines and core HEOR methods, so the AI reasons from curated, human-vetted field references rather than training alone. The template corpus behind Blueprint is live for the United States and Canada: 1,000+ documents, including 50+ guidelines and local best-practice publications. In our controlled multi-model study, adding this knowledge layer lifted mean writing quality and, critically, improved the payer-relevant dimensions, comparative-effectiveness and completeness, while reducing hallucination on the claims payers examine most closely.
The evidence
We measured it, and published the numbers.
Cloaking keeps the writing quality
In a controlled comparison of de-identification methods, Semantic Cloaking retained 91–94/100 writing quality against a 96/100 baseline. Hiding the values instead of preserving their meaning fell to 42 out of 100 on a page with 5 hidden values, and to 18 with 22: the more you hide, the less the model has left to reason with. Meaning preserved, values never exposed.
Read the preprint, Tremblay & Harricharan (2026) →Field grounding lifts quality
Across four models, grounding in a Canadian HTA knowledge layer improved mean writing quality +2.4/100 (up to +10.0 on writing), judged by a blinded AI Delphi panel: the same consensus method behind VaultDelphi.
Read the RAG / HTA preprint →Put frontier AI to work on evidence you couldn't share before.
See it on your own HEOR and payer workflow; the AI never sees a confidential value.