233
Executable rules
Five rule packs across four scoring models. Every rule carries machine-readable provenance, traced to a published standard, guideline, or regulatory requirement. No interpretive ambiguity.
Introducing Regain Accreditation
The operating system for clinical accreditation.
Clinical accreditation is a high-stakes, documentation-intensive process that most organizations still manage with spreadsheets, manual audits, and institutional memory. Regain replaces that entire surface area with infrastructure: a deterministic compliance engine, a clinical data pipeline, and an intelligence layer that turns accreditation standards into executable code.
A vertically integrated platform that evaluates your facility continuously, tells you exactly where you stand, and shows you the evidence trail for every finding. Request a demo →
The numbers
What the platform ships with today.
Multi-
Accreditation frameworks
Multiple accreditation bodies evaluated simultaneously on the same engine. One facility can maintain compliance across overlapping standards without duplicating work.
Multi-
Regulatory frameworks
Rule packs authored for multiple regulatory jurisdictions. The same engine, adapted to local requirements through swappable configuration, not custom code.
Platform
Three tightly integrated layers on one data backbone.
Most compliance platforms are overlays that sit on top of clinical systems and pull data through fragile integrations. Regain is not an overlay. Clinical data, compliance evaluation, and AI-assisted decision support share a single database, a single audit trail, a single RBAC system, and a single deployment.
Compliance Engine
Deterministic rule evaluation with 17 condition kinds. Same inputs produce the same outputs, no LLM in the evaluation path. Every verdict is auditable, reproducible, and traceable to its source standard.
Explore the Compliance Engine →Clinical Data Pipeline
FHIR R4-native data ingestion with automated sync, LOINC and CPT mapping, and SHA-256 evidence hashing. Clinical observations flow directly into compliance evaluation without manual abstraction.
Explore the Data Pipeline →Intelligence Surface
An agentic AI layer with accreditation-specific tooling: mock surveys, gap analysis, standards crosswalks, what-if evaluation, and automated remediation planning. Sub-agent orchestration across multiple facilities.
Explore the Intelligence Surface →Standards as Code
Accreditation standards encoded as version-controlled YAML rule packs with semantic versioning, priority-based composition, and conflict detection at load time. The engine is fixed; the standards are configuration.
Explore Standards as Code →Multi-Accreditor Runtime
One evaluation engine, multiple accreditor frameworks, simultaneous execution. Cross-framework mapping eliminates redundant compliance work across overlapping standards.
Explore Multi-Accreditor Runtime →Clinical Grounding
Every rule traces to an authoritative source through a five-layer hierarchy, from FDA medication labels to emerging evidence. Higher layers always override lower. Enforced programmatically.
Explore Clinical Grounding →The thesis
Compliance evaluation belongs next to the clinical data it measures — not downstream of it.
When compliance is downstream of the clinical system, every evaluation depends on a chain of integrations, exports, and manual abstractions that only stay aligned if someone is watching them. When compliance is co-resident with the clinical data, evaluation is continuous, automatic, and always current — because the data that drives clinical care is the same data that drives compliance evaluation.
This is the architectural choice that separates Regain Accreditation from the spreadsheet-and-survey world it replaces. Not a nicer dashboard. A different layer of the stack.
Use Cases
Built for accreditation bodies and the facilities they serve.
See how continuous compliance monitoring works for each audience — from real-time visibility for standards organizations to automated evidence collection for clinical programs. Explore Use Cases →
Insights
Research and analysis on the forces reshaping clinical accreditation.
Ten long-form articles from domain practitioners on the visibility gap, manual abstraction costs, voluntary accreditation erosion, AI governance, and the transition to continuous monitoring. Read the Insights →
Trust
How the platform earns institutional trust.
Deterministic evaluation with no LLM in the compliance path. Structural independence between reasoning and supervision. Open standards where it matters, proprietary depth where it counts. Building toward HIPAA compliance with an engineered separation of PHI and PII. See the trust architecture →
About
An infrastructure company, not a SaaS dashboard.
Regain, Inc. is a Delaware C-Corporation building clinical AI infrastructure for healthcare accreditation. Self-funded. Clinical systems deployed at medical centers. Early in an industry transformation that will take years to complete. Read about Regain →
See Regain Accreditation in action.
We will walk through your standards framework and show you what continuous compliance monitoring looks like for your program.
Request a demo