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Your compliance rules, running as code.

Not a policy binder. Not a quarterly review. A deterministic rules engine that evaluates every operational event against your actual regulatory requirements, before the record is committed, not after the audit finding.

We codify the regulations you operate under into executable rules, then layer a governed AI copilot that translates every outcome into plain-language guidance with a citation attached. Delivered in four-week cycles, production-ready at the end of each one.

Compliance fails in the gap between the regulation and the workflow.

The regulation is clear. Your SOP documents it. Someone trained the team on it. Then a technician on a Tuesday afternoon hits a case the SOP does not quite cover, and the only thing between your organization and an audit finding is whether that person remembers correctly and has time to check.

That is not a training problem. It is an architecture problem.

  1. 01

    The rule nobody applied

    The requirement exists in a policy document nobody opens. The event gets recorded without the required field, the record is committed, and the gap surfaces months later when an inspector asks for the trail.

  2. 02

    The right fact, the wrong rule

    Staff correctly recall a requirement and apply it to a case where a different rule governs. A Schedule II documentation standard applied to a Schedule III shipment. Both facts accurate. The conclusion is wrong.

  3. 03

    The trail that was never assembled

    An inspector asks how a decision was made. The evidence exists across an EHR, a spreadsheet, an email thread and someone’s memory. Reconstructing it costs a week of senior staff time under deadline pressure.

Deterministic where it must be. Explanatory where it helps.

Compliance Intelligence has two layers, and the separation between them is the entire point.

01

The rules engine decides

We codify your regulatory requirements into deterministic, versioned rules: executable logic that evaluates each operational event and returns a pass, warn or fail outcome with an immutable audit record. No probability. No inference. If a rule fires, you can see exactly which requirement was evaluated and why.

02

The AI copilot explains

When a rule fails, your team does not see an error code. They see what failed, why it matters and the specific steps to resolve it, in operator language, with a citation to the exact regulation behind it. The copilot never interprets regulation and never decides what is compliant. It translates an outcome the rules engine already validated.

That division of labor is why this architecture holds up in a regulated environment when a general-purpose AI assistant does not.

Frameworks we have codified.

Every engagement starts with your actual requirements, not a template. These are frameworks we have already modeled into executable rules.

FrameworkScopeWhat the rules engine enforces
DEA / Controlled Substances ActFederal: all controlled substance handlingReceipt completeness, perpetual inventory, biennial count timing, record retention windows, custody chain integrity
DSCSAFederal: prescription drug supply chainLot-level traceability, transaction history completeness, trading partner verification, suspect product handling
HIPAAAll PHI-handling entitiesRole-based access enforcement, minimum necessary access, audit log completeness, disclosure tracking
USP Chapter 797Sterile compounding operationsEnvironmental monitoring cadence, beyond-use dating, batch release documentation, personnel qualification currency
State Board of PharmacyState-by-state requirementsInventory cadence, technician supervision ratios, policy and procedure compliance, licensure currency
FDA 21 CFR Part 820 / QMSRMedical device manufacturersDesign history file completeness, CAPA closure integrity, document control, supplier qualification
FDA 21 CFR Part 803 (MDR)Medical device adverse event reportingReportability determination, 30-day and 5-day window tracking, event classification consistency
SOC 2SaaS handling sensitive client dataSecurity, availability, processing integrity and confidentiality control evidence

Operating under something not on this list? The architecture is framework-agnostic. If the requirement can be written down, it can be codified.

Luveo HealthEnterprise pharmacy operations platform

28 rules, 7 workflows, two delivery cycles.

The rules engine. 28 rules across seven workflow families (Receiving, Movement, Waste, Audit, Expiration, Ordering and Credential) covering DEA, DSCSA, HIPAA, USP 797 and state Board of Pharmacy requirements. Load-tested at 35 events per second with p95 latency of 2.9 seconds.

The AI copilot. Plain-language guidance for pharmacy technicians on every outcome: what failed, why it matters, how to fix it, and the regulation behind it. Validated across a 30-scenario structured evaluation.

Read the case study →
  • 28Compliance rules codified
  • 7Pharmacy workflows covered
  • 97%Explanation accuracy · 100% on chat
  • 0Guardrail violations across 30 scenarios

The rules engine determined what was compliant. The copilot only explained an outcome that had already been validated, with a citation, every time.

Built for organizations where an audit finding has consequences.

  • Pharmacy operations platforms

    Software companies serving pharmacies that need compliance evaluation embedded in their product: DEA, DSCSA, USP 797 and state board requirements enforced at the transaction level.

  • Specialty pharmacy and infusion

    Organizations handling controlled substances or sterile compounding where documentation timing and completeness carry direct regulatory exposure.

  • Medical device companies

    Pre-production and scaling manufacturers facing QMSR documentation requirements, MDR reportability determinations and post-market surveillance obligations that are outgrowing manual review.

  • Digital health platforms in regulated workflows

    Companies whose product touches a regulated process and who need the compliance layer to be defensible, not just present.

One workflow family. Four weeks. Production, not pilot.

We scope each cycle to a single workflow family or regulatory framework. Narrow enough to finish completely. Real enough to matter operationally.

  1. Week 1

    Requirements + architecture

    We work with your compliance team to map the actual requirements, connect to source systems and establish the governance model.

    You getA working data pipeline and a documented rule specification for review.
  2. Week 2

    Rules engine

    We codify requirements into deterministic, versioned rules and validate outcomes against your real operational records.

    You getA live rules engine evaluating your events, and a first demo to your team.
  3. Week 3

    Copilot + hardening

    We layer cited explanations, add audit logging and access controls, and run the structured evaluation set.

    You getA governed copilot layer plus an evaluation report on accuracy and guardrails.
  4. Week 4

    Production

    Deployment into your environment, team walkthrough, documentation and the roadmap for expansion.

    You getA production system your team owns, plus a written scale roadmap.

At the end of week four you decide whether to run another cycle. Luveo ran two. Most clients run three to six as they expand coverage across workflows and frameworks. How we work →

Reasonable questions.

How is this different from a GRC platform?
GRC platforms are systems of record for compliance documentation: they store your policies, track your attestations and manage your audit calendar. This evaluates operational events against those requirements in real time, before records are committed. Different layer. Most clients keep their GRC platform and add this underneath it.
Are you giving us legal or regulatory advice?
No. We codify the requirements your compliance team defines and validates. Your compliance officer owns the interpretation; we make that interpretation executable, consistent and auditable. The system cites authority. It does not become authority.
What happens when a regulation changes?
Rules are versioned. When a requirement changes, the affected rules are updated and the change is timestamped, so you can demonstrate exactly which version of a requirement was in force when a given event was evaluated. Ongoing rule monitoring and updates are available as a managed operations engagement after the first cycle.
Can the AI hallucinate a regulation?
Structurally, no, and we test for it. The copilot can only reference rule keys that exist in the engine catalog and citations attached to those rules. In the Luveo evaluation across 30 scenarios we recorded zero hallucinated rule keys and zero fabricated regulatory citations. When the system lacks grounding for a case, it says so rather than generating a plausible answer.
Does this touch PHI?
Where required, yes, and it is built for that. We execute a BAA before any data access, work in isolated environments in your cloud, and tokenize PHI at the ingestion boundary so protected data never reaches the AI layer directly. More on security & PHI →

Bring us one workflow. We’ll tell you what four weeks looks like.

If we can scope it into a cycle, we will tell you exactly what that includes and what it costs. If we cannot, we will tell you that too.

Book a 30-minute scoping call →
  1. We’ll ask three questions
  2. 01Which workflow carries the most regulatory exposure?
  3. 02What systems hold the data behind it?
  4. 03What would have to be true to call it solved?