Seed
The AI Phase-Gate Review is the only fixed fee in the ladder. Advance, redesign, or terminate: we are paid the same whichever verdict the evidence supports, so the recommendation is never contingent.
Pharmacovigilance · quality · regulatory · clinical operations
No molecule advances without evidence.Neither should your AI.
You phase-gate every candidate: predefined endpoints, stopping rules, kill early and kill cheap. We bring that same discipline to your AI portfolio: governed agents on your document-heavy workflows, live in weeks, run to protocol, designed to support validation for a defined intended use, and measured in hours returned to science.
Self-funding: nothing from your budget. The AI pays for itself from measured value.
Value and Savings Estimator
See details on bottom line below01The estimate · three levers, four inputs
What is a governed agent program worth across your regulated workflows?
Specialist labor is not one-size-fits-all, so the estimate starts with geography. Three levers where supervised agents already earn their keep: safety case processing, batch record review, and deviation handling. Defaults are deliberately conservative and every assumption is visible.
1
Specialist labor is not one-size-fits-all, so the estimate starts with geography. Three levers where supervised agents already earn their keep: safety case processing, batch record review, and deviation handling. Defaults are deliberately conservative and every assumption is visible.
02How it is priced · AI always earns itself
Nothing from your budget. Everything from measured value.
Self-funding, by protocol: no standing license waiting to justify itself. One small fixed engagement sets the endpoints and the terms; from then on, the deployment pays its own way.
Earn
Deployment is performance-priced: a small per-case, per-batch, or per-document slice of the work the agents actually complete, paid from the hours they return. Nothing sits in your budget hoping to be used.
Prove
Endpoints predefined, baselines measured first, stopping rules always armed. Performance pricing only works when the measurement is independent, and independence is what we sell.
Performance terms are structured per deployment with our delivery partners; the review stays fixed-fee and vendor-neutral so advance-redesign-or-terminate is never contingent on what gets deployed.
03The candidates · where supervised agents earn their place
Four document-heavy workflows with clean endpoints.
01
Safety case processing
Intake, triage, duplicate detection, coding suggestions, and narrative first drafts across every source. Agents prepare; a person releases every case.
02
Batch record review by exception
The record is checked continuously against the recipe; reviewers see only the exceptions. Hours fall, scrutiny rises, and the release decision never leaves quality.
03
Deviations & CAPA
Evidence retrieval, similar-event trending, and investigation first drafts inside your templates, so investigators investigate instead of formatting.
04
Regulatory & medical writing support
Dossier assembly, document QC, and first drafts under strict templates and terminology. Authors keep authorship; agents keep the version control.
All four are designed to support validation for a defined intended use: documented behavior, decision-level audit trails, human release at every gate that matters, and controlled change. Nothing here asks your quality unit to take anything on faith.
04Simulation intelligence · rehearse the process, prove the change
Run it in silico before you run it in the suite.
The frontier of regulated AI has moved past documenting what happened. The shift underway is from backward-looking records to simulation intelligence: processes, cases, and changes rehearsed in synthetic environments before they touch a batch or a patient record — and decided deterministically where it matters.
01
Rehearse the process, not just document it
Process twins run batches, campaigns, and tech transfers in silico first. The change arrives at the gate with a simulated history attached, not a hope.
02
Synthetic data, reduced patient-data exposure
Generated datasets may support training and stress-testing while reducing reliance on patient records. They do not eliminate privacy risk; each use still needs appropriate privacy, security, governance, and re-identification-risk assessment.
03
Stress-test the rare event
The deviation you have seen twice in ten years, generated on demand. Investigators and agents rehearse it before it happens again, not after.
04
Deterministic where it matters
The July 2025 draft Annex 22 covers static, deterministic models in critical GMP applications and says generative AI and large language models should not be used there. Following stakeholder feedback, EMA was still considering possible risk-based controls for adaptive, probabilistic, and generative AI in July 2026.
The rehearsal happens inside the deployment window, in days, not as a research program, and the meter only starts on work measured against an agreed baseline. One honesty note: rehearsal may improve right-first-time performance and reduce deviations, but any gain is site-specific and must be measured, so we deliberately left it out of the estimate above; the Phase-Gate Review quantifies it against your operation.
05Selected rulebook · EU and US status, dated
AI-specific medicines guidance is evolving. Existing GxP obligations still apply.
7 Jul 2025 · draft
The European Commission opened consultation on revised GMP Chapter 4 and Annex 11 and a new draft Annex 22 for AI in medicines manufacturing. The draft covers static, deterministic AI/ML models in critical GMP applications; it says generative AI and large language models should not be used there, while non-critical use requires qualified human review.
Official source ↗6 Jan 2025 · 14 Jan 2026
FDA's draft proposes a risk-based credibility assessment for a defined context of use. FDA and EMA later published ten guiding principles for good AI practice across the medicines lifecycle.
Official source ↗30 Jun–1 Jul 2026
An EMA workshop considered risk-based controls for adaptive, probabilistic, and generative AI. Feedback remains under consideration, and no confirmed final Annex 22 publication date had been announced as of 19 July 2026.
Official source ↗The practical through-line is clear context of use, risk-based governance, fit-for-use data, documented validation, lifecycle monitoring, and human accountability. Scope and status differ across instruments, so every deployment still needs a use-case-specific assessment.
06The method · run it like a protocol
You phase-gate every asset in the pipeline.
When did you last phase-gate the AI?
01
The AI Candidate Briefing
45 minutes, free. You leave with your first candidate workflow, its endpoints, and its stopping rules, written the way your organization already writes protocols.
02
The AI Phase-Gate Review
Fixed fee, four to six weeks, vendor-neutral by contract. Every funded AI initiative graded like a pipeline asset: advance, redesign, or terminate. Plus the endpoints, a validation plan for a defined intended use, and the deployment blueprint.
03
Deploy and Measure
Run like a first-in-human study: one site, one workflow, small, controlled, heavily instrumented. Predefined endpoints, stopping rules, an audit trail, and results at the gate by quarter end.
“Autonomy is not a reward for ambition. It is a consequence of evidence.”
If the Phase-Gate Review does not find a candidate worth advancing, we will say so in writing.
Book the briefing →07Start here
Bring one regulated workflow. Leave with a measurable first gate.
Tell us where evidence work is slowing science. We will respond with a focused candidate briefing, not a generic transformation pitch.
Your current estimator result will be included with this requestNot calculatedEstimate details sent with this request: region, case volume, batches, deviations, specialist rate, assumptions, and calculated impact.