Your regulators won't accept "the AI said so."
We built AI that can prove it.
Production AI is 80% harness, 20% prompt. CLAiRE is the harness, purpose-built for regulated life sciences. iQuality and MAiGRATE are the productized solutions that run on it. Every claim traces back to your organizational truth — not a model's training data.
AI is everywhere. Defensible AI is rare.
Most life sciences teams have already run the generic AI pilots. The hard part isn't capability. It's defensibility.
- Audit trail reviews still take days because someone has to connect the finding to the regulation, manually
- Annual product quality reviews still run a month per product, even with good data
- Regulated migrations take 12–18 months and $1–3M because the evidence work is bespoke every time
- Validation document packages get re-drafted from scratch every release, with no traceability
- A regulator asks "how do you know?" and the answer requires an interview, not a file
- Generic LLMs answer from training data. Regulators require answers from your organizational truth
The constraint that changes everything
In pharma, biotech, and medical device, every AI decision has to be defensible to a regulator. Every deployment has to fit inside your governance. Every piece of evidence has to trace back to your records — not to a model's training data.
Generic AI hallucinates users that don't exist, SOPs that were never approved, certifications that weren't in scope. In a regulated environment, that's not an inconvenience. It's an audit finding.
This is why every regulated AI decision must be grounded against your knowledge graph before the model reasons — and why every claim traces back to a specific node with timestamps and version fingerprints.
"The hard part of regulated AI is not finding a capable model. It is making the model's answers defensible. That requires grounding against your organizational truth — not training data. Generic AI cannot do that."— The design principle behind CLAiRE, iQuality, and MAiGRATE
Built for the one constraint everyone else ignores.
Each product runs on your identity, your AI, with your evidence in your custody. Deployed as a single-tenant Compliance Group SaaS realm or on-premise inside your data center.
The platform your quality team actually runs on.
Next-generation Quality, Document, and Training Management — purpose-built for medical device manufacturers. Three integrated modules built on 21 CFR Part 11, with CLAiRE AI baked in across every workflow.
Explore iQuality →Regulated migration in weeks. Not quarters.
Productized migration for regulated data — TrackWise, MasterControl, and SAP into Veeva Vault QMS or ServiceNow GRC. Runs on your own AI, inside your governance, ships with per-record audit-grade evidence.
Explore MAiGRATE →The harness around the model.
Production AI is 80% harness, 20% prompt. CLAiRE is the harness: retries, validation, fallbacks, observability, four-memory architecture, MCP for tools, A2A for agents, 4-layer guardrails, and a cryptographic evidence chain wrapped around any LLM.
Explore CLAiRE →The model is 20% of the system. The other 80% is the harness.
Pilots fail in production not because the model is wrong, but because nothing around the model is built for regulated work. CLAiRE is what you build when the regulator gets to see the inside.
The model vendor sells the engine. CLAiRE sells the cockpit, the flight controls, the black box, the maintenance log, and the audit certificate.
Nine agents. Each solving a specific problem.
Customers buy the agent for the pain it addresses. Each is a standalone product. Each deploys on your infrastructure or as Compliance Group SaaS, with bring-your-own-LLM. Reads only what you authorize, produces an audit-grade evidence pack on every run.
100% continuous coverage across every connected audit source. Replaces the 90-day quarterly sampling blind window with nightly review of every event — Veeva Vault, Jira, Polarion, ServiceNow, TrackWise. Nine standard checks per event, each tagged Critical, Major, or Minor with 21 CFR Part 11 and EU Annex 11 citations.
Read-only by designSix specialized agents compile your APQR across TrackWise, JMP, ERP, and SharePoint. Every claim in the draft is cited back to a source record. Cuts a month-per-product process to one or two days.
Every claim cited to sourceDetects drift between your source-of-truth (Workday, Okta, Active Directory) and downstream regulated systems. Per-record diff with severity, reason, and source evidence. Drives quarterly User Access Reviews with risk-flagged accounts, dormant identities, and SoD conflicts.
Detect-only by defaultContinuous monitoring of live quality data — deviations, CAPAs, OOS, complaints, change controls — measured against your corporate SOPs. Surfaces trends and anomalies between event-by-event QMS review and the year-end APQR cycle.
Catches issues between cyclesDrafts and reviews validation document packages (URS, FS, DS, IQ, OQ, PQ) grounded in your requirements knowledge graph. Every claim cited to the requirement it satisfies. Draft and review in one engine.
Draft and review in one engineContinuously ingests FDA enforcement actions: Warning Letters, 483 observations, Untitled Letters. Finds comparable past cases for any new observation and drafts response material grounded in actual precedent.
Find precedent before they doImports engineering and regulatory standards (IEEE, ISO, IEC, ASME, USP), decomposes them into atomic requirements, and assembles a typed knowledge graph. Optionally pushes work items into Polarion ALM linked to source clauses.
New standard to requirements in hoursTracks controls across GxP, 21 CFR Part 11, SOX, Cybersecurity (NIST CSF, CIS, MITRE ATT&CK), NIST 800-53, ISO 27001, and SOC 2. Continuous evidence collection from Splunk, Sentinel, Okta, AWS Config, and your QMS.
Audit prep as a continuous processEmbedded AI copilot inside Polarion ALM — installed as a native plugin, no extra infrastructure. AI chat, compliance dashboard, Smart Trace gap detection, AI quality check, regulatory knowledge base. All data stays on your Polarion server.
Data never leaves your serverYour installation. Your network. Your data. Your evidence to keep.
Every AI call grounds against your knowledge graph before it reasons. Every claim the model produces traces back to a specific node with timestamps and version fingerprints. When a regulator asks "how do you know?" the answer is a path through the graph — not an interview.
The CLAiRE ontology encodes your compliance structure as a typed graph: users, roles, training records, SOPs, batch records, deviations, CAPAs, change controls, and the cross-references among them. Models cannot invent users, certifications, or approvals that do not exist in your source systems.
iQuality's QMS, DMS, and TMS modules write to the same graph. A CAPA in QMS is linked to the SOPs it affects in DMS and the training records in TMS that prove competency.
One security posture. Two deployment modes.
Either mode: your identity provider runs access, your AI tenant does the inference, your audit log stays in your custody. Data never goes to public models.
Option 1 — Compliance Group SaaS: Single-Tenant Dedicated Realm
Your realm is a hard tenancy boundary — separate schema, separate secrets vault, separate knowledge graph, separate audit log. No shared caches. No cross-realm read paths. Customer SAML SSO, Compliance Group zero standing privilege, per-realm encryption keys.
Option 2 — On-Premise: Inside Your Data Center
Installed as a Docker stack on your infrastructure. An optional Sync Agent makes every flow outbound-only from your network. Your Azure OpenAI, AWS Bedrock, or self-hosted model. Air-gapped deployment supported for the strictest environments.
30 minutes. A concrete plan for where to start.
We'll walk through iQuality, MAiGRATE, and CLAiRE in your context — your systems, your audit risk profile, your compliance targets. No generic demo. A real plan.