McKinsey25-35% task time
Operations, not just discovery
Agentic AI could support 75-85% of operations workflows, cutting task time 25-35% across supply chain, procurement, manufacturing, CMC and quality.
Program 06 · Domain specialisation
SCIKIQ Certified Data and AI EngineerLife Sciences / Pharma
In pharma the technology is rarely the hard part — proving it to a regulator is. A GxP-validated agent needs documented intended use, evidence of testing, change control and periodic review. This specialisation teaches you to build for that from the first commit, not to retrofit it before an audit.
- 80 hrstaught
- 40hands-on AI labs
- 8assessment tests
- +20 hrs2 E2E capstones
$60-110bnAnnual gen-AI value potential across pharma and medical productsMcKinsey
75-85%Of operations workflows agentic AI could supportMcKinsey
25-35%Time reduction in supply chain, CMC and quality tasksMcKinsey
2026FDA and EMA joint guiding principles for AI in life sciencesFDA/EMA
01
Part 1 · The business
The value chain, the people, the trends
The life sciences value chain is long, gated and expensive to get wrong. Part 1 maps it from discovery to pharmacovigilance, names who owns each gate, and sets out what the strategy houses see shifting.
The life sciences value chain
Six stages from molecule to market surveillance. Select one.
01
Discover & research
Target identification, screening, lead optimisation and preclinical work.
Decisions made here
- Which target is worth pursuing?
- Which compound advances?
- What does the literature already say?
Where it breaks today
- Literature review that cannot keep pace with publication
- Experimental data siloed per team
- Negative results never surfaced
What is measured
- Cycle time per iteration
- Hit rate
- Reproducibility
Where AI landsLiterature synthesis with citations, assay data structuring and hypothesis triage.
Cycle time per iterationHit rateReproducibility
02
Clinical development
Protocol design, site selection, recruitment, monitoring and data management.
Decisions made here
- Is this protocol feasible?
- Which sites will actually enrol?
- Is this a safety signal?
Where it breaks today
- Protocol amendments driven by avoidable design flaws
- Site selection based on relationships rather than data
- Monitoring queries generated late and in bulk
What is measured
- Enrolment rate
- Amendment count
- Query rate
- Time to database lock
Where AI landsProtocol analysis against precedent, site scoring on historical performance and query triage.
Enrolment rateAmendment countQuery rateTime to database lock
03
CMC & manufacturing
Process development, tech transfer, batch manufacture and release.
Decisions made here
- Is this batch within specification?
- What caused the deviation?
- Can we release?
Where it breaks today
- Deviation investigations that take weeks
- Tech transfer knowledge trapped in individuals
- Batch records reviewed page by page
What is measured
- Right-first-time
- Deviation cycle time
- Batch release time
Where AI landsDeviation investigation support, batch record review and tech-transfer knowledge retrieval.
Right-first-timeDeviation cycle timeBatch release time
04
Quality & compliance
Quality management, CAPA, audits, inspections and validation.
Decisions made here
- Is this a CAPA?
- Is this system validated?
- Are we inspection-ready?
Where it breaks today
- CAPA backlogs
- Validation documentation written twice
- Inspection readiness assembled reactively
What is measured
- CAPA closure time
- Audit findings
- Validation coverage
Where AI landsCAPA drafting, validation documentation assembly and inspection-readiness Q&A.
CAPA closure timeAudit findingsValidation coverage
05
Regulatory & submission
Dossier authoring, health-authority queries, labelling and lifecycle maintenance.
Decisions made here
- What does this authority require?
- Is this consistent across the dossier?
- How do we answer this question?
Where it breaks today
- Module authoring duplicated across markets
- Consistency checks done manually across thousands of pages
- Query responses assembled under deadline pressure
What is measured
- Submission cycle time
- Query volume
- Consistency findings
Where AI landsDocument drafting from source, cross-dossier consistency checking and query response assembly.
Submission cycle timeQuery volumeConsistency findings
06
Commercial & safety
Medical affairs, field engagement, market access and pharmacovigilance.
Decisions made here
- What can we say, to whom?
- Is this an adverse event?
- What does this payer require?
Where it breaks today
- MLR review as the commercial bottleneck
- Adverse event intake from unstructured sources
- Field teams without answers to medical questions
What is measured
- MLR cycle time
- Case processing time
- Field response time
Where AI landsMLR pre-check against claims library, adverse event triage and grounded medical information.
MLR cycle timeCase processing timeField response time
Who you are building for
In pharma, the person who can stop your deployment is usually Quality. Select one to light the stages they own.
What the strategy houses are seeing
The 2026 read on where AI value lands in life sciences.
McKinseyLoops > gates
From linear gates to learning loops
R&D productivity improves when stage gates become continuous learning loops — which requires data infrastructure most organisations have not built.
McKinsey$60-110bn/year
The value is spread across the chain
Gen AI could unlock $60-110bn annually for pharma and medical products, distributed from discovery through commercial engagement rather than concentrated in one place.
BCGModel under pressure
Innovation is strong, the business model is strained
BCG’s 2026 read is that biopharma’s innovation engine is performing while pricing, patent and cost pressure force operating-model change.
FDA/EMAIntended use first
Regulators set expectations before adoption peaked
Joint guiding principles in early 2026 emphasise intended use, context of use, reliability, transparency, human oversight and GxP adherence.
ISPERisk-based validation
GAMP 5 now speaks AI/ML directly
Updated guidance reinforces risk-based planning, data strategy and continuous monitoring for AI components inside GxP systems.
Where these numbers come from — 4 sources
Consulted when this program was written, in July 2026. Each entry names the claim on this page that it backs, so the pairing stays checkable if the copy is later edited.
- McKinseyGen AI: a game changer for biopharma operations$60-110bn annual value across the value chain
- McKinseyReimagining life science enterprises with agentic AI75-85% of operations workflows, 25-35% task time reduction
- McKinseyFrom linear gates to learning loops: rewiring biopharma R&D with AIStage gates giving way to continuous learning loops
- IndustryValidation Strategies for Agentic AI Platforms in GxP Clinical Data EnvironmentsRisk-based validation expectations for AI components
02
Part 2 · Data and meaning
Domains, ontology, taxonomy and governed terms
A GxP architecture is one where every output can be traced to a validated process. That single requirement drives almost every design decision you will make here.
The data landscape
Six families, split sharply between GxP and non-GxP — know which side you are on.
Clinical data
EDC, CTMS and eTMF — regulated, auditable, and slow to change.
Medidata, Veeva Vault, CDISC SDTMManufacturing & quality
MES, LIMS, batch records, deviations and CAPA.
MES, LIMS, QMSRegulatory content
Dossiers, labelling, health-authority correspondence and submissions.
eCTD, Veeva RIMScientific literature
Publications, patents, conference abstracts and internal reports.
PubMed, internal repositoriesSafety & PV
Adverse event cases, signals and periodic safety reports.
Argus, safety databaseCommercial & access
Field activity, MLR-approved claims, payer and HCP data.
Veeva CRM, claims libraryThe semantic layer
In a regulated environment the vocabulary is part of the submission. Terms that drift between study, dossier and label become findings, so the semantic layer here is a compliance artefact as much as an engineering one.
Data domains
Six subject areas, each with the entities it holds, its critical data elements and the function accountable for it.
Substance & product
The molecule, its formulations and the products marketed from it.
SubstanceDrug productPresentationLabel
- Critical data elements
- Substance identifier; Product code; Strength and form
- Owned by
- Regulatory affairs
Study & subject
Clinical studies, their design, sites and the subjects enrolled.
StudyProtocolSiteSubjectVisit
- Critical data elements
- Study identifier; Subject identifier; Visit number
- Owned by
- Clinical operations
Clinical data
What was observed and measured, standardised for submission.
EndpointObservationAdverse eventConcomitant medication
- Critical data elements
- SDTM domain; MedDRA preferred term; Severity grade
- Owned by
- Data management & biostatistics
Manufacturing & quality
How it is made, tested and released, and what went wrong.
BatchSpecificationTest resultDeviationCAPA
- Critical data elements
- Batch number; Specification limit; Deviation classification
- Owned by
- Manufacturing & QA
Regulatory
Submissions, health-authority interactions and approvals by market.
SubmissionDossier moduleAuthority queryApproval
- Critical data elements
- Submission identifier; eCTD section; Approval date
- Owned by
- Regulatory affairs
Safety & pharmacovigilance
Adverse event cases, signals and periodic reporting.
CaseSignalPeriodic report
- Critical data elements
- Case identifier; Causality assessment; Seriousness criteria
- Owned by
- Pharmacovigilance
Ontology & knowledge graph
The entities and the typed relationships between them — what the agent traverses instead of guessing joins. Select any entity.
Core entityEventReference / classification
Core entity
Substance
The active molecule.
- Drug product contains Substance
Core entity
Drug product
A formulated, presented medicine.
- Drug product contains Substance
- Study investigates Drug product
- Adverse event assessed against Drug product
- Batch manufactured of Drug product
- Submission covers Drug product
Core entity
Study
A clinical investigation of a product.
- Study investigates Drug product
- Study defined by Protocol
- Study measures Endpoint
- Site participates in Study
- Submission reports Study
Reference / classification
Protocol
The design document governing a study.
- Protocol specifies Endpoint
- Study defined by Protocol
Core entity
Site
An investigational location enrolling subjects.
- Site participates in Study
- Subject enrolled at Site
Core entity
Subject
An enrolled participant.
- Subject enrolled at Site
- Subject experiences Adverse event
Event
Adverse event
An untoward occurrence in a subject.
- Adverse event assessed against Drug product
- Subject experiences Adverse event
Reference / classification
Endpoint
A pre-specified measure of study outcome.
- Protocol specifies Endpoint
- Study measures Endpoint
Event
Batch
A discrete quantity manufactured to one specification.
- Batch manufactured of Drug product
- Batch raises Deviation
Event
Deviation
A departure from an approved process.
- Deviation resolved by CAPA
- Batch raises Deviation
Event
CAPA
The corrective and preventive action from an investigation.
- Deviation resolved by CAPA
Event
Submission
A dossier filed with a health authority.
- Submission covers Drug product
- Submission reports Study
Taxonomies
The classification hierarchies that make records comparable across systems.
Adverse event coding
- System organ class
- High level term
- Preferred term
Cardiac disorders → Cardiac arrhythmias → Atrial fibrillation
Submission structure
- Module
- Section
- Document
Module 3 → 3.2.P Drug product → Specification
Deviation classification
- Category
- Criticality
- Type
Process → Major → Out-of-specification result
Governed business terms
The definitions an agent must use rather than invent. Most wrong answers in this industry are a term used loosely.
- Substance vs product
- A substance is the molecule; a product is a formulated, presented medicine. They are not interchangeable.
- Batch
- A discrete quantity produced to one specification in one cycle — the unit of release and recall.
- Deviation
- Any departure from an approved procedure, whether or not it affected quality.
- CAPA
- Corrective and Preventive Action — the documented response to an investigated deviation.
- Intended use
- The documented purpose of a system, which sets the scope of its validation.
- Serious adverse event
- An event meeting defined regulatory seriousness criteria, triggering expedited reporting.
- Endpoint
- A pre-specified outcome measure. Post-hoc measures are not endpoints.
- Right first time
- The proportion of batches produced with no deviation requiring investigation.
03
Part 2 · The architecture
How it is actually built
The layers a deployment needs, what each one holds, and what the engineer owns there.
The reference architecture
Seven layers, with validation evidence generated as a by-product of the build.
01/07
Sources & systems
EDC, LIMS, MES, RIM, safety and literature sources — each with its own qualification status.
Takes in
Nothing. This is the upstream edge.
Hands on
Effective-dated, version-pinned extracts, each traceable to the controlled record it came from.
SystemWhat it doesKey business entities
Clinical trial systems
EDC, CTMS and the trial master file. Study data under change control, where every correction carries a reason and an audit entry.
StudySubjectVisitAdverse event
Laboratory systems
LIMS and ELN across discovery, development and QC. Instrument-adjacent, with results that mean nothing without their method and batch.
SampleBatchResultMethod
Regulatory information management
Submissions, health-authority commitments, product registrations and their status by market. Deadlines here are legal, not aspirational.
SubmissionRegistrationCommitment
Quality management
Deviations, CAPA, change control and validation records. The system that decides whether anything you build is allowed to be used.
DeviationCAPAChange controlValidation record
Safety and pharmacovigilance
Case intake, coding to MedDRA and expedited reporting, on clocks measured in days.
Safety caseAdverse eventSignal
Controlled document repositories
SOPs, protocols, study reports and labelling — versioned, effective-dated and approved, with superseded versions retained rather than deleted.
SOPProtocolStudy reportLabel
What the engineer owns here
Knowing which source systems are GxP-qualified and what that means for yours.
Where this layer fails
Building against the current version of a document. In a GxP estate the question is always "what was effective on the date of the event", and a system that cannot answer it is not validatable.
02/07
Ingestion & data integrity
ALCOA+ data integrity, audit trails and controlled transformation into the analysis layer.
Takes in
Read-mostly access paths, change feeds and document streams from the systems of record.
Hands on
Replayable, resolved, quality-checked records with their lineage back to the source row.
SystemWhat it doesKey business entities
Change data capture
Reads the source’s own change log rather than polling it, so the platform sees every state a record passed through instead of only where it ended up.
Change eventOperation typeCommit timestamp
Landing and raw zone
Every extract kept as it arrived, immutable and timestamped. The thing you replay from when a downstream definition turns out to have been wrong.
Raw extractBatchWatermark
Document processing
Layout-aware extraction over the unstructured half of the estate: sectioning, tables, signatures, and the page reference every later citation depends on.
DocumentSectionPage reference
Entity resolution
Decides that two records are the same real-world thing, with a survivorship rule and a confidence, so downstream joins are a decision rather than an assumption.
Match candidateSurvivorship ruleCluster
Data quality and contracts
Schema, freshness and volume expectations asserted at the boundary, so a bad load fails loudly here rather than quietly two layers later.
ContractExpectationViolation
What the engineer owns here
Data integrity evidence: attributable, legible, contemporaneous, original, accurate.
Where this layer fails
Snapshot loads instead of change capture. It looks identical in a demo and it silently loses every intermediate state, which is exactly what an audit later asks you for.
03/07
Governance & standards
CDISC standards, controlled vocabularies, entitlements and the validated data catalogue.
Takes in
Replayable, resolved records with lineage from ingestion.
Hands on
Typed, classified, defined data an agent can be grounded on without inferring meaning.
SystemWhat it doesKey business entities
Business glossary
One agreed definition per term, owned by a named person, so the number an agent quotes means what the business means by it.
TermDefinitionOwnerSteward
Ontology and knowledge graph
The typed entities and relationships the domain actually has, so an agent can traverse "which customers are exposed to this" rather than guess from adjacent text.
EntityRelationshipNodeEdge
Semantic and metrics layer
Metrics defined once, in one place, with their filters and grain. Removes the class of error where the agent computed something plausible and wrong.
MetricDimensionGrainFilter
Classification and policy
Sensitivity labels and the purpose each classification permits, applied at the field level and inherited by everything downstream.
ClassificationPurposePolicy
Lineage and catalogue
Where a value came from and what it touched on the way. The layer that makes an answer defensible rather than merely correct.
Lineage edgeDatasetColumn
Validation and change controlthis domain adds
Computer system validation evidence, versioned prompts and models, and the change control that makes a modification allowable rather than merely deployed.
Validation recordChange controlVersion
What the engineer owns here
Making terminology and standards machine-usable rather than documented-only.
Where this layer fails
Letting the model infer meaning from column names. It will, it will be plausible, and nobody will notice until the number reaches a regulator or a board pack.
04/07
Retrieval & grounding
Retrieval over literature, protocols, dossiers and the approved claims library with citations.
Takes in
Typed, classified, defined data from the governance layer.
Hands on
Ranked, permission-trimmed, citable evidence scoped to the caller and the moment.
SystemWhat it doesKey business entities
Hybrid index
Vector and lexical retrieval together, because exact identifiers, codes and clause numbers are the thing semantic search is worst at.
ChunkEmbeddingTermScore
Chunking and metadata
Splits on the document’s own structure and carries effective dates, version and source into every chunk, so a retrieved passage knows when it was true.
ChunkEffective dateVersionSource reference
Security trimming
Applies the caller’s permissions inside the query rather than filtering results afterwards, so the model never sees what the user may not.
PrincipalEntitlementFilter
Graph retrieval
Traverses the knowledge graph for questions that are joins rather than similarity — exposure, genealogy, ownership, causation.
PathHopTraversal
Reranking and citation
Reorders candidates on relevance and returns the passage identifier behind every sentence, so the answer can be checked rather than trusted.
CandidateRankCitation
What the engineer owns here
Citation fidelity strong enough for a regulatory reviewer to follow.
Where this layer fails
Filtering results after retrieval instead of constraining the query. The model has already seen the rows you removed, and it will use them.
05/07
Agent & orchestration
Document drafting, consistency checking, deviation support and case triage under review gates.
Takes in
Ranked, permission-trimmed, citable evidence from retrieval.
Hands on
Actions taken or proposed, each with the evidence, the identity and the trace behind it.
SystemWhat it doesKey business entities
Agent runtime
Plans, calls tools and holds the loop. Where the step budget, the timeout and the stopping condition are enforced rather than hoped for.
TaskPlanStepTrace
Tool and MCP servers
Typed, permissioned tools with declared schemas. An agent’s real capability surface is this list, which is why the list is a security artefact.
ToolSchemaScopeInvocation
State and memory
Durable task state with checkpoints, so a run that dies mid-way resumes instead of restarting and re-doing side effects.
CheckpointSessionMemory record
Human-in-the-loop gates
Approval steps on the actions that need one, carrying enough context for the approver to actually decide rather than rubber-stamp.
ApprovalThresholdDecision
Workflow integration
Writes back into the systems people already work in, under a service identity with its own audit trail.
Work itemWrite-backActor
What the engineer owns here
Ensuring the agent drafts and proposes; a qualified person decides.
Where this layer fails
An agent that acts under a service account rather than on behalf of the user. It will eventually do something the requesting user had no right to do, and the log will not show it.
06/07
GxP validation & guardrails
Intended use, risk assessment, IQ/OQ/PQ evidence, change control and periodic review.
Takes in
Proposed actions and drafted responses from the agent layer.
Hands on
Permitted, grounded, logged output — or a refusal with a stated reason.
SystemWhat it doesKey business entities
Policy enforcement
The rules that decide whether an action is permitted at all, evaluated before the action and independently of the model that proposed it.
PolicyDecisionObligation
Input and output screening
Injection detection on the way in, and on the way out the checks for leakage, unsupported claims and content the domain forbids.
PromptResponseViolation
Groundedness checking
Verifies each assertion resolves to retrieved evidence, and fails the response rather than shipping the sentence that does not.
AssertionSupportVerdict
Model risk management
Model inventory, intended use, validation evidence and the sign-off that lets a model be used for a purpose. Not optional in a regulated estate.
ModelIntended useValidationApproval
Audit log
Immutable record of what was asked, retrieved, decided and done — the artefact a reviewer reads when they do not take your word for it.
Audit entryActorEvidence reference
Part 11 audit trailthis domain adds
Attributable, legible, contemporaneous, original and accurate records of every automated action, retained for the record’s lifetime.
Audit entrySignatureRetention
What the engineer owns here
The validation package — generated as you build, never reconstructed afterwards.
Where this layer fails
A prompt change shipped without change control. In a validated estate that is not a deployment, it is a deviation.
07/07
Evaluation & monitoring
Performance evals, continuous monitoring, drift detection and requalification triggers.
Takes in
Permitted, grounded, logged output from the guardrail layer.
Hands on
Measured quality, cost and latency — and the evidence to change any of the three.
SystemWhat it doesKey business entities
Evaluation harness
Held-out sets and graded runs on every change, so a prompt edit is a measured change rather than a hopeful one.
Eval setCaseScoreBaseline
Regression gate
Blocks a release when quality drops, in CI, on the same evidence for everyone. The difference between a system and a demo.
ThresholdRunGate decision
Tracing
End-to-end spans across retrieval, model and tool calls, so a bad answer can be opened and read rather than argued about.
TraceSpanLatencyToken count
Cost and capacity
Cost per task and per tenant, against throughput and quota. The number that decides whether the pilot can become the rollout.
Cost per taskQuotaThroughput
Drift and feedback
Watches quality against production traffic rather than the test set, and routes real corrections back into the eval suite.
Drift signalFeedbackIncident
What the engineer owns here
Defining, in advance, the drift threshold that triggers revalidation.
Where this layer fails
Evaluating once, before launch. Quality moves with the data, the model and the traffic, and a system with no live measurement has no idea which of the three moved.
Typical tech stack
Models
- Enterprise-agreement frontier models
- Domain models for biomedical text
- Frozen, versioned model endpoints
Grounding
- Literature and dossier retrieval
- Approved claims library
- Controlled vocabulary expansion
Agents
- Drafting and consistency agents
- MCP over RIM, LIMS and safety
- Qualified-person review gates
Data
- CDISC-standardised datasets
- ALCOA+ audit trails
- Versioned, immutable datasets
Assurance
- GAMP 5 risk-based validation
- IQ/OQ/PQ evidence
- Change control and periodic review
Runtime
- Qualified environment
- Frozen model versions
- Full retention and traceability
Constraints that shape the design
Intended use is the anchor
Validation scope follows the documented intended use. Broaden what the agent does and you re-open validation.
Model version freezing
A model that silently updates invalidates your qualification. Pin versions and treat upgrades as change control.
ALCOA+ data integrity
Every transformation needs an audit trail. Retrofitting one is the most common GxP failure in AI projects.
Human decision authority
A qualified person makes the decision. The agent may assemble, draft and check — never release.
04
Part 3 · Getting it done
Where the business and the technology meet
Start where GxP does not bite — literature, commercial, medical information — and earn the right to move into validated territory with the evidence you generated on the way.
Use cases, mapped to the chain and the architecture
Filter by stage or by earned autonomy. Selecting a use case jumps the value chain and the architecture to the stage and layer it depends on.
Value chain stage
Autonomy earned
12 use cases
No use cases match that combination.
The delivery playbook
How a life sciences deployment actually runs.
01
Week 1 — decide GxP or not
Establish whether the workflow is GxP-regulated before anything else. It changes the timeline, the evidence burden and the team.
02
Week 2 — write intended use first
Document intended use and context of use up front. Everything in validation follows from it, and scope creep re-opens it.
03
Weeks 3-6 — build with the audit trail on
Generate validation evidence as a by-product: versioned data, pinned models, logged runs. Retrofitting this is the classic failure.
04
Weeks 7-9 — qualify against real cases
Run IQ/OQ/PQ against cases quality helped select, and define the drift threshold that would trigger requalification.
05
Weeks 10-12 — change control and hand over
Put the system under change control with a periodic review schedule and a named system owner.
The curriculum
8 modules, 80 taught hours, 40 hands-on labs and 8 assessments — every lab provisioned and graded by the SCIKIQ Agentic AI Playground. Open a module to see its labs.
01The SCDAI delivery model in life sciencesWorking inside a regulated organisation where Quality can stop anything.10 hrs · 5 labs
Delivery mandateGxP vs non-GxP scopingIntended useStakeholder mapValue sizingValidation timeline reality
01
GxP triageClassify five workflows as GxP or non-GxP and justify each.
02
Intended useWrite an intended use and context of use statement for one agent.
03
Value sizingQuantify the cost of a deviation investigation and an MLR cycle.
04
Stakeholder mapMap Quality, Regulatory, IT and the business owner for a use case.
05
Thin sliceScope a non-GxP slice that builds evidence for a later GxP one.
Assessment 01 — Operating model & GxP scoping
02Life sciences data foundationsEDC, LIMS, MES, RIM and literature — plus the standards that make them comparable.10 hrs · 5 labs
CDISC SDTM/ADaMeCTD structureLIMS and MES dataSafety dataLiterature sourcesALCOA+
01
CDISC mappingMap raw clinical data to SDTM domains and validate the result.
02
eCTD traversalNavigate a dossier structure and locate a fact across modules.
03
Batch record parsingExtract exceptions from an electronic batch record.
04
Literature ingestionBuild a deduplicated literature corpus with metadata.
05
ALCOA+ audit trailInstrument a pipeline to meet data integrity expectations.
Assessment 02 — Life sciences data landscape
03Context engineering for scientific and regulatory textScientific claims must be attributable, hedged accurately and never overstated.10 hrs · 5 labs
Claim attributionHedging and certaintyStructured outputsTerminology controlRefusalContext budget
01
AttributionForce every scientific claim to carry a resolvable citation.
02
Certainty calibrationPreserve hedging language rather than flattening it.
03
Structured extractionExtract endpoints and results into a validated schema.
04
Terminology controlConstrain output to approved controlled vocabulary.
05
RefusalMake the agent decline off-label or unapproved claim territory.
Assessment 03 — Context engineering
04Grounding on literature, dossiers and claimsThree corpora, three retrieval strategies, one citation standard.10 hrs · 5 labs
Literature retrievalDossier-scale retrievalClaims library matchingRecall over large corporaCitation contractsRetrieval evals
01
Literature RAGBuild retrieval over publications and measure recall on labelled questions.
02
Dossier retrievalRetrieve consistently across a multi-thousand-page dossier.
03
Claims matchingMatch a promotional statement to approved claims and flag near-misses.
04
Recall tuningImprove recall on a corpus where a single miss matters.
05
Retrieval evalsBuild labelled sets for each corpus and gate on recall@k.
Assessment 04 — Retrieval & grounding
05Agent design in regulated workflowsDraft, check and assemble — and stop short of deciding.10 hrs · 5 labs
Workflow vs agentQualified-person gatesDocument generationConsistency checkingMulti-agent reviewContainment
01
Drafting agentDraft a regulatory document section fully sourced from submitted material.
02
Consistency agentDetect factual inconsistency across document sets.
03
Review gateRequire qualified-person approval before any output is filed.
04
Multi-agent reviewSplit drafting, checking and citation verification across agents.
05
ContainmentPrevent the agent from asserting anything not in the source corpus.
Assessment 05 — Agent design & autonomy
06Integrating with regulated systemsRIM, LIMS, safety and document systems — where write access is a validation event.10 hrs · 5 labs
MCP over regulated systemsRead-only patternsAudit metadataIdentityChange windowsError handling
01
MCP over RIMExpose read tools over a regulatory information system.
02
Audit metadataAttach complete audit metadata to every agent action.
03
Read-only designDesign a workflow that produces value without write access.
04
IdentityBind agent actions to the qualified user behind them.
05
Failure handlingDesign retries that never duplicate a regulated record.
Assessment 06 — Systems integration
07GxP validation, safety and securityGAMP 5 risk-based validation for an AI component, done properly.10 hrs · 5 labs
GAMP 5 for AI/MLRisk assessmentIQ/OQ/PQChange controlPeriodic reviewOWASP LLM Top 10
01
Risk assessmentProduce a risk-based validation plan for an AI component.
02
Qualification protocolWrite and execute an OQ/PQ protocol against real cases.
03
Change controlDefine what model or prompt change triggers revalidation.
04
Periodic reviewDesign the monitoring that supports periodic review.
05
Red teamRun an OWASP LLM Top 10 pass over a document pipeline.
Assessment 07 — GxP validation & security
08Production, monitoring and costContinuous monitoring, drift thresholds and the economics that justify year two.10 hrs · 5 labs
Eval suitesContinuous monitoringDrift and requalificationJudge calibrationCost per documentHandover
01
Eval gateGate deployment on citation-accuracy regression.
02
MonitoringImplement continuous monitoring that feeds periodic review.
03
Drift thresholdDefine and test the drift level that triggers requalification.
04
Cost per documentMeasure and reduce cost per generated document.
05
HandoverProduce runbook, validation summary and system owner assignment.
Assessment 08 — Production & operations
Two capstones — 20 hours
End-to-end agent design, development, deployment and testing, 10 hours each. Reviewed by a Senior SCDAI Engineer against a published rubric.
Capstone A — Regulatory query response agent
Build an agent that assembles evidence from a dossier and drafts a health-authority query response with complete traceability — plus the validation evidence package for it.
Capstone B — Deviation investigation assistant
Build an agent that retrieves comparable deviations and prior CAPAs, structures the evidence and drafts the investigation record for qualified-person review, under change control.
What you deliver, and how it is marked
- A running agent, deployed on the program's target stack, reachable by a reviewer.
- An architecture note: boundaries, tools, autonomy level and the trade-offs you took.
- A versioned eval set with a baseline and your final scores.
- A trace walkthrough of one success and one deliberate failure.
- Cost per task, measured — not estimated.
- A runbook and rollback path a client team could operate on Monday.
Problem fit15%Is this a decision worth automating, scoped to a defensible boundary?
Architecture20%Right pattern for the constraints; autonomy earned, not assumed.
Grounding & accuracy20%Measured groundedness against the eval set, with the failures named.
Safety & compliance20%Domain controls actually enforced, and demonstrably so.
Operability15%Traces, runbook, rollback, cost — could someone else run this?
Communication10%Can a client executive follow the reasoning in ten minutes?
One lab, start to finish
Every lab in this program follows the shape below. This is lab 05 in full — the brief you are given, the environment that is provisioned for you, the code you start from and the assertions that decide whether you passed.
Module 05 · Agent design in regulated workflows
Grounded-assertion drafting
Make ungrounded assertion a build failure rather than an output.
The brief
Draft a regulatory document section from a submitted source set. The agent must assert only what is retrievable, and fail the run when it cannot ground a statement. Hedging language in the source must survive into the draft unflattened.
Provisioned for you
- Source corpus: 3 study reports, 1 protocol
- Citation resolution service
- Certainty-calibration checker
- 20 labelled section briefs
You start from this
def draft_section(brief, corpus):
"""Draft, then verify. Any sentence without a resolvable citation
fails the run -- it is not silently dropped."""
draft = generate(brief, retrieve(brief, corpus))
unsupported = [s for s in sentences(draft) if not resolves(s, corpus)]
raise NotImplementedErrorWhat the grader asserts
- Every factual sentence carries a citation that resolves to a source location
- Run fails, with the offending sentences named, when grounding is impossible
- "May be associated with" is never rendered as "causes"
- No numeric value appears that is not present verbatim in a source
- Output is deterministic enough to diff across two runs of the same brief
05
Assessment
Assessed, not attended
Every module ends with a timed, randomised assessment delivered through the SCIKIQ Agentic AI Playground. The certificate requires a pass on all of them plus two reviewed capstones.
Assessment blueprint
What each test covers, how long it runs, and how many items are currently in the versioned bank behind it.
| # | Assessment & coverage | Items | Time | In bank |
|---|---|---|---|---|
| 01 | Operating model & GxP scopingDelivery mandateGxP classificationIntended useValue sizing | 25 | 35 min | 4 |
| 02 | Life sciences data landscapeCDISC and eCTDManufacturing dataLiteratureData integrity | 25 | 35 min | 4 |
| 03 | Context engineeringAttributionCertainty and hedgingStructured outputsScope refusal | 25 | 35 min | 4 |
| 04 | Retrieval & groundingLiterature retrievalDossier retrievalClaims matchingRetrieval metrics | 25 | 40 min | 4 |
| 05 | Agent design & autonomyDrafting vs decidingReview gatesConsistency checkingContainment | 25 | 40 min | 4 |
| 06 | Systems integrationMCP designAudit metadataRead-only patternsIdentity | 25 | 35 min | 4 |
| 07 | GxP validation & securityGAMP 5 risk-based validationQualificationChange controlLLM security | 30 | 45 min | 4 |
| 08 | Production & operationsEval gatingContinuous monitoringRequalificationEconomics and handover | 30 | 45 min | 4 |
Single best answerOne defensible option among plausible distractors drawn from real field mistakes.
Scenario judgementA client situation with constraints; you pick the action a Senior SCDAI Engineer would take.
Architecture selectionGiven non-functional requirements, choose the pattern and justify the trade-off.
Failure diagnosisA trace, an eval report or a cost curve — identify the root cause.
- Each module ends with a timed assessment; the program certificate requires a pass on every one.
- Items are drawn at random from a versioned bank, so no two learners sit an identical paper.
- Pass mark is 70%. Two retakes are included, each with a fresh draw.
- Scenario items carry double weight — they are the ones that predict field performance.
- Capstones are reviewed against a published rubric by a Senior SCDAI Engineer, not auto-graded.
Try four questions from the bank
Real items, drawn from 32 in this program's bank — weighted toward the scenario and diagnosis types, because those are the ones that predict field performance. Instant feedback, nothing saved.
Four sample items — one attempt each, then the reasoning is shown.
Q1Why is the GxP/non-GxP classification the first question on a pharma engagement?
GxP scope drives everything downstream. Discovering it late means re-doing the work with evidence you did not capture.
Q2An eCTD dossier spans thousands of pages across modules. What retrieval property matters most?
When one miss is material, recall dominates. Precision can be recovered by a reviewer; a miss cannot.
Q3Why must scientific claims preserve hedging language?
Certainty calibration is a scientific accuracy requirement. Overstating a finding is a substantive error, not a style issue.
Q4Your literature synthesis omits a contradicting paper. Why is this worse than no synthesis?
Selective evidence presented confidently is actively misleading. Contradiction surfacing is a design requirement here.
06
Career & outcomes
What this makes you, and where it goes next
The same ladder whichever specialisation you enter through — what changes is the domain you go deep in. Below: how the program is delivered, the skills it moves, the roles it leads to, and the specialisations closest to this one.
Available two ways
The same labs, assessments and capstones, delivered to an enterprise cohort or to individual professionals.
B2B
Direct to enterprise
Cohorts of 20 to 2,000+ on your own tenancy, with your data patterns and your cloud. Skill-gap baselining up front, per-team mastery reporting throughout, and capstones scoped against your real backlog so the output is deployable work.
- Private cohort on your tenancy
- Baseline skills assessment before kick-off
- Capstones scoped to your backlog
- Per-team mastery dashboards for L&D
- Optional Senior SCDAI Engineer architecture reviews
B2C
Direct to individual professionals
The same labs, assessments and capstones for individual engineers and analysts, run on shared infrastructure with a fixed cohort calendar. You leave with a graded portfolio, not a certificate of attendance.
- Fixed-date cohorts, self-paced within them
- Same graded labs and item bank
- Portfolio-ready capstones you own
- Certificate on a pass in every module
- Community and office-hours access
Skill matrix
Find your row and aim one column right. The Playground scores you against this after every module.
| Skill | Beginner | Intermediate | Advanced |
|---|---|---|---|
| Life sciences fluency | Knows the stages. | Maps the chain to gates and owners. | Sizes value and navigates Quality early. |
| GxP validation | Aware of GxP. | Writes intended use and qualification evidence. | Designs validatable AI systems from the start. |
| Scientific grounding | Retrieves papers. | Guarantees attribution and certainty. | Achieves recall on corpora where a miss matters. |
| Context engineering | Writes clear prompts. | Structures retrieval, tools and state deliberately. | Designs context strategy for reliability and cost at scale. |
| Retrieval & grounding | Builds basic vector search. | Tunes chunking, hybrid search and reranking. | Designs graph + vector grounding with measured recall. |
| Agent orchestration | Runs a single tool-calling agent. | Builds supervised multi-step and multi-agent flows. | Designs autonomy boundaries and failure containment. |
| Tool & system integration | Calls a documented API. | Writes an MCP server over a system of record. | Designs a least-privilege tool estate across systems. |
| Evaluation | Eyeballs outputs. | Builds labelled eval sets and regression gates. | Runs online evals with drift and judge calibration. |
| Observability & cost | Reads logs. | Traces runs, tracks tokens and latency. | Owns cost per task and capacity planning in production. |
| Security & guardrails | Adds output filters. | Mitigates the OWASP LLM Top 10 in a build. | Threat-models an agent estate and proves controls. |
| Client delivery | Takes notes in a workshop. | Runs discovery and scopes a thin slice. | Owns the account technically, from scope to handover. |
Where it takes you
The SCDAI ladder is the same whichever specialisation you enter through — what changes is the domain you go deep in.
01
Data and AI EngineerEntry
Python, LLM APIs, first RAG build, prompt and context basics.
02
Associate Data and AI EngineerPractitioner
Client-facing discovery, tool integration, agent build under supervision.
03
SCIKIQ Certified Data and AI EngineerSpecialist
Owns a deployment end to end — scoping, build, evals, handover.
04
Senior Data and AI Engineer / Domain LeadSenior
Multi-agent architecture, regulated deployments, reference patterns for the practice.
05
AI Architect / Practice LeadLeadership
Portfolio of accounts, delivery standards, hiring and capability strategy.
Terms worth knowing
- GxP
- The family of Good Practice regulations (GMP, GCP, GLP) governing regulated life sciences work.
- GAMP 5
- The risk-based framework for validating computerised systems, now covering AI/ML explicitly.
- IQ/OQ/PQ
- Installation, Operational and Performance Qualification — the validation evidence chain.
- ALCOA+
- Data integrity principles: attributable, legible, contemporaneous, original, accurate, plus.
- CDISC SDTM
- The standard model for submitting clinical study data.
- eCTD
- The electronic Common Technical Document format for regulatory submissions.
- CAPA
- Corrective and Preventive Action — the quality response to a deviation.
- MLR
- Medical, Legal and Regulatory review of promotional material.
- SCDAI
- SCIKIQ Certified Data and AI Engineer — the credential this program awards.
- SCDAI engineer
- An engineer embedded with the customer who turns their constraints into a working system.
- Thin slice
- The narrowest end-to-end path through a workflow that still produces business value.
- Agentic RAG
- Retrieval where the agent decides when, what and how often to retrieve.
- MCP
- Model Context Protocol — open standard connecting an agent to tools and data.
- A2A
- Agent2Agent — protocol for agent-to-agent delegation across vendors.
- Eval set
- A labelled, versioned set of cases an agent must pass before release.
- LLM-as-judge
- Using a model to score outputs, calibrated periodically against human ratings.
- Guardrail
- An enforced constraint on an input, output or action — not a prompt request.
- Human-in-the-loop
- A required human approval step before a consequential action executes.
- Cost per task
- Total token, tool and compute cost of one completed unit of work — the engineer's unit economic.
- Golden path
- The reference architecture a practice standardises on so deployments stay reviewable.
- Handover
- The point at which the client can operate, evaluate and change the system without the engineer.
Related programs
Skill a team, or join a cohort
B2B cohorts run on your tenancy with capstones scoped to your backlog. B2C cohorts run on a fixed calendar.
07
Enrolling
What you need, what you get, and what it costs
Stated plainly enough to rule yourself in or out without a sales call: the prerequisites, how the program runs, exactly what the credential is worth, and the questions everyone asks.
Before you start
Stated plainly so you can rule yourself in or out without a sales call. Nothing here is a formal qualification — it is what the first lab assumes you can already do.
Technical
You should already be able to do these
- Comfortable in Python: functions, typing, virtual environments, reading a stack trace
- Have called an LLM API and handled a structured response
- Git, the command line, and reading someone else’s code without panic
Domain
What we assume, and what we teach
- A general sense of how a drug reaches market helps
- No GxP experience assumed — validation is taught from first principles
Time and access
What the program asks of your week
- A machine that can run a browser and a terminal — labs run in the Playground, not locally
- Roughly 8 hours a week for the taught weeks, plus 20 hours for the two capstones
How it runs
Cohort dates and pricing are confirmed on enquiry rather than printed here, because both move with the intake.
- Format
- Self-paced within a fixed cohort window, delivered in the SCIKIQ Agentic AI Playground
- Taught hours
- 80 hours across 8 modules
- Capstones
- 20 hours — two end-to-end builds, reviewed not auto-graded
- Typical duration
- 10–12 weeks at around 8 hours a week
- Level
- Intermediate → Advanced
- Built for
- Engineers and analysts deploying AI in GxP and commercial pharma environments
- Next cohorts
- — confirmed on enquiry
- Pricing
- — confirmed on enquiry
The credential
The credential is awarded per specialisation, so it names the domain or stack you were assessed in rather than claiming general competence. On this program the badge reads SCDAI — Life Sciences / Pharma.
How it is earned
- A pass at 70% or above on every module assessment — not an average across them
- Both capstones reviewed against the published rubric by a Senior SCDAI Engineer
- All labs submitted and machine-graded green
- Awarded as
- A verifiable digital badge with a public credential record
- Valid for
- Two years from award
- Recertification
- One current-year assessment plus a refreshed capstone
- Retakes
- Two included per assessment, each with a fresh item draw
- Named specialisation
- The badge states the domain or stack, e.g. SCDAI — Healthcare
- Verification
- Third parties confirm a holder from the credential record, no login required
A certificate that cannot be checked is decoration. Every award resolves to a record showing the specialisation, the award date and the assessments passed.
The certificate you leave with
This is the credential the program awards, shown exactly as it is issued — with the specialisation named, the assessment record attached and a verification link anyone can check without an account.
SCIKIQ Data Academy
Certificate of Professional Certification
This is to certify that
Your name
has been assessed and certified as
SCIKIQ Certified Data and AI Engineer
Life Sciences / Pharma
- Specialisation
- Life Sciences / Pharma
- Assessments passed
- 8 of 8, each at 70% or above
- Labs graded
- 40 of 40 machine-graded green
- Capstones reviewed
- 2, against the published rubric
- Taught hours
- 80 across 8 modules, plus 20 capstone hours
AwardedOn completion
Valid untilTwo years from award
Credential IDSCDAI-LIFE-SCIENCE-<issued>
Senior SCDAI Engineer, Certification Board
- 01The specialisation is on the certificate
Not “Data and AI Engineer” but the domain or stack you were actually assessed in. A general claim would be a weaker one.
- 02The assessment record travels with it
Modules passed, labs graded and both capstones reviewed — so the credential states what was measured rather than that you attended.
- 03Anyone can verify it, without a login
The credential ID resolves to a public record showing the specialisation, the award date and the assessments passed.
Share it on LinkedIn
Add it to your LinkedIn profile in one step. The link pre-fills the certification fields from the credential record, so the entry on your profile matches the record a reader can check.
Add to LinkedIn profile The button is live on your real certificate; here it opens LinkedIn pre-filled with this specialisation so you can see exactly what the profile entry will say.A certificate that cannot be checked is decoration. Every award resolves to a record showing the specialisation, the award date and the assessments passed.
Questions we get asked
The objections that come up in every conversation about this program, answered without the brochure voice.
Is this a course or a certification?
Both, and the second is the point. Eight timed assessments and two reviewed capstones stand between you and the credential, so a pass means someone measured the skill rather than recorded your attendance.
How is it self-paced without falling apart?
Every lab is provisioned, graded and unblocked by the Playground rather than by an instructor. That is what lets a cohort of 2,000 cost the same faculty time as a cohort of 20 — and why you are never waiting on someone to mark your work.
What if I fail an assessment?
Two retakes are included per assessment, each drawing a fresh item set from the bank, so a retake is a genuinely new paper rather than the same questions again.
Do I need my own cloud account?
For the domain specialisations, no — labs run in provisioned sandboxes. For the four tech-stack programs you will want access to that platform, since deploying into a real subscription is much of the point.
Can we run this privately for our team?
Yes. B2B cohorts run on your own tenancy with your data patterns, a skills baseline before kick-off, per-team mastery reporting, and capstones scoped against your actual backlog so the output is deployable work rather than an exercise.
Which specialisation should I take?
Take the domain you deploy into. If you move across industries, take a tech-stack program instead and pick up domain context on the engagement. The chooser on the programs page will narrow it.
How current is the material?
The trends, platform capabilities and regulatory positions are reviewed each quarter, and every external claim on these pages links to its source so you can check the date yourself.
What do I actually leave with?
A graded portfolio: forty machine-graded labs, two reviewed end-to-end agent builds with measured evaluation and cost per task, and a verifiable credential naming your specialisation.
Still deciding?
Tell us the systems you deploy into and we will say plainly whether this specialisation is the right one — or which of the 21 is.