Most AI consultants treat compliance as a constraint to work around. For life sciences and healthcare organizations, validation and documentation aren't obstacles — they're the job. QP builds AI workflows designed for regulated environments from the ground up.
Regulated industries carry a documentation burden that doesn't exist anywhere else. The organizations that use AI to handle that burden — without creating new compliance risk — gain a genuine competitive edge.
IQ/OQ/PQ protocols, validation reports, deviation documentation, change control packages. The paperwork surrounding regulated systems is extensive, and much of it follows predictable templates. Every hour a validation engineer spends formatting a protocol is an hour not spent on actual qualification work.
Compiling submission packages, cross-referencing study data against requirements, formatting technical documents for agency review — these are structured tasks that AI handles well. Manual compilation introduces errors and adds weeks to timelines that are already under pressure.
Tracking open deviations, gathering supporting documentation, confirming CAPA closure, assembling inspection-ready packages. Most organizations still do this with spreadsheets and tribal knowledge. The risk isn't just preparation time — it's that something gets missed.
Each workflow is designed for regulated environments and includes the validation documentation required to support its use in GxP or ISO-controlled contexts.
AI drafts IQ, OQ, and PQ protocols from structured inputs — system description, test parameters, acceptance criteria — and generates validation report templates pre-populated with test data. Your validation team reviews, executes, and approves. They stop writing from scratch on every system qualification.
AI assists with deviation investigation write-ups, root cause analysis structuring, and CAPA documentation — pulling relevant SOPs, batch records, and prior deviations for context. Quality staff spend their time on judgment calls, not on assembling the supporting package.
AI assembles submission packages — NDA, BLA, 510(k), PMA, or IND components — by pulling relevant data, cross-referencing study summaries, and formatting sections to agency specifications. Submission teams stop spending weeks on manual compilation.
AI monitors your quality systems for open deviations, past-due CAPAs, expiring documents, and gaps in training records — generating an inspection readiness dashboard your QA team can act on daily rather than scrambling to produce during a pre-inspection sprint.
Greg Stone built his career inside FDA-regulated pharmaceutical and biologics manufacturing — commissioning and qualifying manufacturing systems, writing validation protocols, executing IQ/OQ/PQ testing, and delivering the documentation packages that satisfied regulatory inspections.
That background means QP doesn't need to be educated on what a regulated environment requires. When the question is "can we use AI for this process, and how do we validate it," the answer comes from someone who has already sat on the regulated side of that question — not from a generalist consultant who learned about FDA compliance on the way to your kick-off call.
Every AI workflow QP builds for a life sciences client includes a validation package structured to support your QA review and regulatory obligations. The workflow doesn't go live until it passes.
Scope & Risk Assessment
Define the intended use, regulatory context, and risk classification before any design decisions are made. GxP vs. non-GxP processes are handled differently from the start.
Build & Test
Build against your actual data and systems. Execute IQ/OQ/PQ-style testing. Document pass/fail against defined acceptance criteria. Fix failures before any production use.
QA Review & Release
Validation package delivered for QA review and sign-off. System releases to production only after your quality team approves.
Change Control & Periodic Review
Documented change control procedures and a periodic review schedule built in. The system stays validated as it evolves.
These figures reflect the scale of administrative and compliance burden in life sciences and healthcare. They are industry research benchmarks, not projections.
of total US healthcare expenditures attributed to administrative costs — the highest share of any developed nation's healthcare system
NEJM, 2019
average time physicians spend on EHR documentation per patient encounter — time that clinical AI workflows are designed to compress significantly
Annals of Internal Medicine, 2017
estimated annual cost of FDA regulatory compliance across US pharmaceutical manufacturers — a significant portion driven by documentation and reporting requirements
PhRMA / Tufts CSDD estimates
QP's life sciences practice is built around organizations that operate under FDA oversight or equivalent regulatory frameworks. The CQV background is a direct fit for these environments.
Greg Stone's professional background is in CQV engineering inside FDA-regulated pharmaceutical and biologics manufacturing. Every AI workflow QP builds for a regulated environment follows the same validation framework: defined requirements, IQ/OQ/PQ-style testing against those requirements, and a documented audit trail. AI that touches regulated processes is validated before it goes live — the same standard applied to any GxP system.
It means the workflow is tested against a defined set of requirements before deployment, and those test results are documented. For AI tools touching GxP processes, that documentation becomes part of your validation package — reviewable by QA and inspectable by regulators. The AI doesn't go live until it passes.
Yes, with the right design and validation approach. FDA's guidance on Computer Software Assurance (CSA) provides a risk-based framework that applies to AI-assisted workflows. The key is documentation, validation, and change control — the same disciplines that govern any software in a GxP environment. QP builds workflows that meet these requirements rather than working around them.
Pharmaceutical manufacturers, biotech and biologics companies, medical device firms, contract research organizations (CROs), contract development and manufacturing organizations (CDMOs), nutraceutical companies, and healthcare providers with regulated documentation requirements. If your organization operates under FDA oversight, GMP, GCP, GLP, or ISO 13485, QP's background is directly relevant.
Life sciences engagements typically run higher than general SMB work due to validation requirements and documentation standards. A focused implementation with full validation package typically falls in the $25,000 to $75,000 range depending on scope and regulatory context. Hourly advisory starts at $200/hr for organizations that need a CQV perspective on AI decisions without a full implementation. The best starting point is a 30-minute discovery call.