AI built for regulated environments
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.
Where life sciences teams lose time
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.
Validation documentation is eating engineering time
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.
Regulatory submissions are slower than they should be
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.
Audit preparation is still done manually
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.
Four workflows that reduce documentation burden
Each workflow is designed for regulated environments and includes the validation documentation required to support its use in GxP or ISO-controlled contexts.
Validation protocol & report generation
AI drafts IQ, OQ, and PQ protocols from structured inputs, including 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.
Deviation & CAPA management support
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.
Regulatory submission document assembly
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.
Audit readiness & inspection preparation
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.
TurnoverIQ: GMP turnover package QA for capital projects
Building or expanding a GMP facility? TurnoverIQ checks contractor and vendor documents against your turnover package index and the spec before they reach your system, and cites the page behind every finding. Nothing is written into your Kneat or Veeva Vault, and a qualified person signs every package. We're taking a small number of pilot projects.
How TurnoverIQ works →Validation is not an afterthought here
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.
The QP validation approach
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.
What documentation overhead is costing life sciences organizations
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 went to administration in 2017, roughly double Canada's 17 percent
Himmelstein et al., Annals of Internal Medicine, 2020
average time physicians spend in the EHR per outpatient encounter, across roughly 100 million encounters; time that clinical AI workflows are designed to compress
Overhage & McCallie, Annals of Internal Medicine, 2020
of physician and staff time per physician each week spent on prior authorization, across an average of 40 requests
American Medical Association, 2025 Prior Authorization Physician Survey (released 2026)
Who this is built for
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.
- Pharmaceutical and biologics manufacturers (GMP)
- Medical device companies (FDA 21 CFR Part 820, ISO 13485)
- CROs and CDMOs with GCP or GMP obligations
- Biotech and gene/cell therapy organizations
- Healthcare providers with regulated documentation requirements
- Organizations looking for a quick AI demo without validation requirements
- Teams unwilling to include QA in the implementation process
- Non-regulated healthcare businesses where compliance rigor isn't required
Common questions from life sciences organizations
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 carry additional complexity due to validation requirements and documentation standards. Those factors shape both the scope and the investment. For organizations that need a CQV perspective on AI decisions before committing to a full implementation, advisory work with no long-term commitment is available. The best starting point is a 30-minute discovery call.