Servitor designs, validates, and deploys task-bound AI inside GMP operations. Deterministic where it must be. Governed everywhere. Working systems, not slide decks.
Six capabilities, one standard: everything must survive an inspection, not just a demo.
Risk-based validation frameworks for AI and ML systems in regulated operations, aligned to GAMP 5 and computer software assurance thinking. Documented intended use, defined performance gates, and evidence a QA reviewer can actually follow.
RAG-grounded drafting agents paired with a deterministic compliance engine. Every generated record carries its lineage: what the model wrote, what the rules checked, and what a human approved.
Interpretation of evolving FDA and EMA GenAI guidance, Annex 22 readiness, and 21 CFR Part 11 implementation for AI-generated records. Regulation as an engineering constraint, not a surprise.
Audit trails, record lineage, and governance controls built for AI-generated content from the start. If a record cannot be traced, it cannot be trusted, and it does not ship.
Appliance and private-tenant deployments that keep models and data inside your security boundary. Built for sites where "just use the cloud" is not an acceptable answer.
Deviation and CAPA practice from sixteen years on the floor, plus hands-on AI training for quality teams: how to validate it, how to audit it, and how to say no to it when the evidence is not there.
In April 2026, FDA issued its first AI-in-cGMP warning letter. Unverified AI-generated SOPs are now a 483 waiting to happen. The question is no longer whether to govern AI in the quality system. It is how fast you can.
Ungoverned tools writing uncontrolled content on your floor right now, with no record of what was generated or who approved it.
Confident, well-written prose with no basis in your actual procedures. It reads right. It is wrong. Nobody catches it at 4:55 PM.
The 42-second rubber stamp: reviewers approving AI output they did not truly read, because the draft looked finished.
See the Compliance Engine catch all three in the live demo →
ENFORCEMENT REFERENCES ARE DESCRIBED AS REPORTED. VERIFY CITATIONS AGAINST CURRENT FDA PUBLICATIONS BEFORE RELYING ON THEM IN REGULATORY FILINGS.
A 15-rule gate that checks every SOP against your procedures before a person ever sees it. The flags are deterministic, the severities are explicit, and nothing passes on a model's word alone.
Task-bound documentation agents that interview your subject-matter experts and draft SOPs, work instructions, and protocols. Human-in-the-loop at every gate: SMEs, area management, and Quality sign off. The agent drafts. People decide.
Document in, flags out. The deterministic Compliance Engine audits your existing SOPs. No language model in the loop, which is exactly why auditors like it.
Governed autocomplete for your authors, grounded in your approved procedures and checked by the engine as they write.
SME interview to approval package: the agent captures knowledge, drafts, lints, and routes every gate for human sign-off.
One process, one document family, success criteria agreed up front. You will know by week six whether this earns a place in your quality system.
Three deliberately flawed sample SOPs and the deterministic linter, running in your browser. Sixty seconds, no signup, nothing leaves your machine.
Missing materials, vague contact times, and one step that needs a human expert.
22 flagssample data Run the review → SOP-LAB-0203 · 11 STEPSThe messiest of the three. Passive voice, undefined acronyms, unmeasured additions.
26 flagssample data Run the review → SOP-CLN-0305 · 9 STEPSShort and sharp. Fifteen flags hiding in nine steps of cleaning procedure.
15 flagssample data Run the review →Illustrative sample data only. The production engine runs against your procedures, on your network.
Practical sessions for quality teams, built from real enforcement trends and real floor experience. No theory without application.
The 2026 enforcement picture and what it means for your AI plans. Built for busy weeks and skeptical audiences.
Ask about a sessionGovernance strategy for quality and site leadership: where AI belongs in the quality system, and where it does not.
Ask about a sessionHands on: run the linter, audit AI-generated records, and build your validation approach with your own documents.
Ask about a session"Compliant AI must be bound to a single, verifiable task."
Sixteen years in biotech quality: cleanroom manufacturing tech, technical operations, deviation and CAPA investigations, training, and audit support, most recently in cell therapy manufacturing in the Philadelphia corridor. I have written the investigations, sat through the inspections, and once traced a cleanroom full of randomly shutting-down PCs to static buildup in the gowning room.
Servitor exists because AI is coming for the quality system whether we govern it or not. I would rather build the governed version: task-bound, deterministic where it counts, and honest about what it cannot verify.
The premium-consultant pitch is simple: I am the person thinking hardest about your problem. Here is the receipts drawer.
Tell me where AI is pressing on your quality system. I will tell you plainly whether I can help, and what it would take.