AI / Quality / Training
AI-based SOP knowledge assistant
An assistant that answers procedural questions from effective SOPs, with citations.
Illustrative use caseCapability
AI Across the Pharmaceutical Lifecycle
Practical AI where it helps pharmaceutical work — research, development, manufacturing, quality, regulatory, laboratory and supply chain — governed from day one.
What we do
AI earns its place in pharmaceutical work when it reduces review effort, surfaces patterns people miss, or makes knowledge easier to find — and when a qualified person still owns the decision.
We identify use cases, assess data readiness and risk, design the human-in-the-loop controls, and build or configure the solution with the validation evidence your quality unit needs.
Where it fits in the pharmaceutical lifecycle
Typical activities
Each activity shows who performs it. We do not claim facilities we do not own.
DGXP-led Led by the DGXPComplianceLab team Partner-coordinated Performed by a qualified partner, coordinated by us
Use-case discovery
DGXP-ledWorkshops to find where AI adds measurable value.
Data readiness assessment
DGXP-ledData sources, quality, access and integrity.
Solution design
DGXP-ledModel approach, retrieval design and human oversight.
Build and integration
DGXP-ledAssistants, analytics and document intelligence tools.
Validation
DGXP-ledFit-for-intended-use testing and performance criteria.
Monitoring
DGXP-ledPerformance, drift and change control after go-live.
AI across the pharmaceutical lifecycle
Select an area to see where AI can reasonably help today.
AI in research
- Literature intelligence
- Research knowledge discovery
- Formulation research support
Every use case starts with an intended use and a risk assessment. Outputs support a qualified person; they do not replace the decision.
How we govern AIAI in development
- Development data analysis
- Process optimisation
- Knowledge management
Every use case starts with an intended use and a risk assessment. Outputs support a qualified person; they do not replace the decision.
How we govern AIAI in manufacturing
- Process monitoring
- Predictive analytics
- Deviation analysis
- Manufacturing intelligence
Every use case starts with an intended use and a risk assessment. Outputs support a qualified person; they do not replace the decision.
How we govern AIAI in quality
- Investigation support
- CAPA intelligence
- Document intelligence
- Quality trend analysis
Every use case starts with an intended use and a risk assessment. Outputs support a qualified person; they do not replace the decision.
How we govern AIAI in regulatory
- Regulatory document intelligence
- Submission knowledge management
- Regulatory content assistance
Every use case starts with an intended use and a risk assessment. Outputs support a qualified person; they do not replace the decision.
How we govern AIAI in laboratory
- Data review support
- Analytical trend analysis
- Laboratory knowledge systems
Every use case starts with an intended use and a risk assessment. Outputs support a qualified person; they do not replace the decision.
How we govern AIAI in supply chain
- Forecasting
- Inventory intelligence
- Supplier intelligence
Every use case starts with an intended use and a risk assessment. Outputs support a qualified person; they do not replace the decision.
How we govern AIAI in digital quality
- AI governance
- AI risk assessment
- AI validation
- AI change management
- AI lifecycle management
Every use case starts with an intended use and a risk assessment. Outputs support a qualified person; they do not replace the decision.
How we govern AIFrom idea to governed AI
- 1Idea
- 2Use case
- 3Risk assessment
- 4Design
- 5Development
- 6Testing
- 7Validation
- 8Deployment
- 9Monitoring
- 10Change control
Technologies and processes
- Large language models
- Retrieval-augmented generation
- Machine learning
- Statistical process control
- Document intelligence
- Knowledge graphs
Example projects
Illustrative use cases showing how this capability is typically applied.
AI / Regulatory
AI regulatory document intelligence
Using AI to locate, compare and summarise content across regulatory documents.
Illustrative use case
AI / Quality / Manufacturing
AI deviation investigation support
Surfacing similar past deviations and likely contributing factors to support investigators.
Illustrative use caseAI / Quality
AI quality risk assessment
Classifying proposed AI use cases by risk and defining the validation each one needs.
Illustrative use caseOur project execution model
Every engagement follows the same eight steps, scaled to the size and risk of the project.
-
01
Understand
Understand business and technical requirements.
-
02
Assess
Assess scientific, technical, regulatory and operational requirements.
-
03
Design
Design the appropriate solution.
-
04
Identify
Identify appropriate internal and external capabilities and partners.
-
05
Execute
Coordinate and execute the project.
-
06
Verify
Test, verify, qualify or validate where applicable.
-
07
Document
Create technical, quality and regulatory documentation.
-
08
Improve
Monitor, optimise and continuously improve.
Related services
AI Governance & Validation
AI is not implemented simply because it is available. In pharmaceutical work it needs an intended use, a risk assessmen…
Digital Transformation
Converting paper-based and fragmented pharmaceutical processes into controlled, connected digital workflows that stay c…
Pharmaceutical Software
Software designed around pharmaceutical workflows — requirements, workflow design, validation and documentation include…
Frequently asked questions
Can AI make GxP decisions?
In our approach AI assists; a qualified person reviews and decides. The level of oversight is set by risk assessment.
Does our data leave our control?
Deployment options and data flows are decided during design. Confidentiality and data-residency requirements are captured as requirements.
How do we know an AI tool is working correctly?
Through acceptance criteria defined before testing, validation against representative data, and ongoing performance monitoring.
Discuss a ai for pharma requirement
Share the product, stage and timeline. Capability is confirmed after technical assessment and partner availability.