Why Pharma Manufacturing Can't Risk AI Without Evidence Governance—How Michelangelo Provides It

Executive Summary

AI-assisted decision tools promise massive efficiency gains in pharmaceutical manufacturing—from deviation investigations to batch release decisions. But they also create a new compliance risk: how do you prove the AI stayed within approved boundaries when it made a critical decision?

Traditional quality systems (QMS, eDMS, LIMS) log what happened. But they don't enforce what's allowed before an AI output gets used. When a cleanroom deviation occurs and an AI tool suggests root cause analysis, or when batch release requires AI-assisted decision support, regulators want proof that the process followed validated, approved constraints—not just a log entry saying it happened.

Michelangelo is governance infrastructure for high-consequence AI workflows in regulated manufacturing. It enforces admissibility constraints at runtime—before outputs can be used—and produces machine-readable evidence artifacts that survive toolchain handoffs and inspection scrutiny.

The Problem: Evidence Breaks in Regulated Manufacturing

Why traditional compliance tools fail for AI-assisted workflows

Modern pharmaceutical manufacturing uses multiple systems: QMS for deviations and CAPAs, eDMS for documentation, LIMS for testing data, ERP for batch records, and increasingly, AI tools for decision support. Each system has its own audit trail. But the evidence breaks between systems.

Real-world scenario: A contamination event occurs in a Grade A cleanroom during aseptic filling. Quality opens a deviation in the QMS. An investigator uses an AI tool to analyze environmental monitoring data, HVAC logs, gowning records, and historical deviations. The AI suggests three potential root causes with supporting evidence. The investigator copies the AI output into the deviation report. QA reviews and approves in the QMS.

The evidence gap: The QMS has an audit trail of the deviation workflow. The AI tool might have logs. But nothing proves the AI analysis followed approved constraints. The copy/paste step is ungoverned. If the investigator manually edited the AI output or ran it with unapproved parameters, there's no enforcement preventing it and no evidence proving what actually happened.

Where evidence breaks (typical failure modes)

• Multi-system handoffs: Data moves between QMS, LIMS, ELN, AI tools, and spreadsheets. Each has audit trails, but no single evidence chain proves end-to-end compliance.
• Ungoverned transformations: Copy/paste, exports, LLM prompts, manual curation. These steps are invisible to traditional audit trails.
• Post-hoc logging: Systems log what happened, but don't enforce constraints before outputs are used.
• AI introduces new risks: Non-deterministic outputs, hallucinations, prompt injection, data leakage. Traditional validation approaches assume deterministic systems.

What Michelangelo Does

Michelangelo is runtime enforcement infrastructure for AI-assisted manufacturing workflows. It sits between your AI tools and your quality systems, enforcing boundaries before use rather than logging violations after.

Traditional approach: AI tool generates output → operator uses it → audit trail logs it happened

Michelangelo approach: AI tool generates output → Michelangelo checks admissibility constraints → if compliant, output is sealed with evidence artifact → operator can use sealed output → QMS receives evidence pack proving governance

How it works (high-level, pre-NDA)

1. Admissibility constraints defined upfront: Before any AI tool is used, quality defines the boundaries: approved data sources, allowed transformations, required decision criteria, output formats. These constraints are machine-readable and version-controlled.

Example (cleanroom deviation): Root cause analysis AI tool can only access validated environmental monitoring data from last 90 days, must use approved statistical methods, cannot suggest causes outside validated failure mode catalog.

2. Deterministic control layer enforces constraints: When an AI tool generates an output, Michelangelo intercepts it before the operator can use it. The enforcement gate checks: Did it use approved inputs? Did it follow approved methods? Is the output format valid? If any constraint is violated, the output is blocked.

3. Evidence artifacts generated automatically: Every governed step produces a structured evidence pack: inputs received, constraints applied, enforcement decision (pass/fail), timestamps, identities. These artifacts are machine-readable and tamper-evident.

4. Replay and verification available: An auditor or regulator can take an evidence pack and independently verify that the same governance process occurred.

5. Controlled export to quality systems: Once an AI output passes all constraints, Michelangelo seals it with its evidence pack and exports it to the QMS via controlled channels. No copy/paste, no manual transcription.

Use Cases in Pharmaceutical Manufacturing

Michelangelo targets high-consequence workflows where AI assistance is valuable but evidence requirements are strict:

• Deviation and CAPA investigations: AI summarizes historical deviations, suggests root causes, drafts investigation reports. Michelangelo ensures approved data sources only, no hallucinated references, human review gates, and evidence packs for the quality system.
• Batch release decision support: AI compares batch data against specifications and historical trends. Michelangelo enforces approved specification sources, validates inputs, requires human approval, generates evidence proving the decision stayed within boundaries.
• Validation impact assessments: AI evaluates whether a system change requires revalidation. Michelangelo ensures only validated assessment models are used, approved change categories applied, escalation triggers for edge cases.
• Cleanroom environmental monitoring: AI analyzes particle count trends and recommends investigations. Michelangelo enforces calibrated sensor data only, approved alert thresholds, investigation triggers per SOPs.

Why This Matters for Pharma Manufacturing

AI is already being used in regulated manufacturing. The question isn't whether to adopt AI—it's how to adopt it responsibly with defensible evidence.

For Manufacturing Operations: Deviation investigations complete faster with AI-assisted root cause analysis—but only if evidence is defensible. Batch release decisions supported by AI trending tools—but regulators want proof the AI followed approved methods.

For Quality/Compliance: Risk-based audit trail review becomes manageable—Michelangelo generates decision-level evidence packs instead of system-level log files. Inspection readiness improves—evidence packs are structured, verifiable, and inspection-ready by construction.

For Executive Leadership: Board-level liability reduced—defensible process proof, not just narratives about AI governance. Competitive advantage—adopt AI responsibly before competitors, without regulatory risk.

Commercial Model and Integration Path

Michelangelo is designed for system integrators, consulting firms, and technology partners to deploy as governance infrastructure.

Deployment Models:

• Lab evaluation license: Paid pilot scope (1-2 workflows, defined constraints, 90-day evaluation) with NDA-protected mechanics disclosure
• Production license: Annual site license with variable participation fees based on governed decision volume
• OEM/integration license: For consulting firms or technology vendors to embed Michelangelo in their solutions

Contact

For engagement discussions, lab evaluation scoping, or NDA execution:

Phil Cheevers
Pink House Technology
905-321-2291 • 242-809-1832
info@pinkhouse.tech