Data
Where scientific data lives, how accessible it is, and how reliably it can be used.
Assess. Design. Plan.
Five dimensions of scientific AI readiness.
Knowledge
How experimental context, reasoning, decisions and learnings are captured and reused.
Workflows
How research moves from scientific question to experiment, analysis, interpretation and decision.
Infrastructure
How research systems, tools and data environments connect.
Governance
How access, provenance, model usage, approvals and human oversight are managed.
Assess. Design. Plan.
AI-Native R&D Blueprint
Know exactly what to build next.
A structured assessment and design engagement that gives you a concrete understanding of your current R&D environment, the architecture required for scientific AI, and a prioritized plan for implementation.
You get
- Current-state R&D & AI landscapeOverview of the relevant teams, workflows, systems, data sources and existing AI initiatives.
- Scientific workflow mapsA clear view of how selected scientific workflows operate today.
- Scientific knowledge-flow analysisIdentification of where experimental context, decisions and learnings are created, stored, transferred or lost.
- AI readiness & gap assessmentAssessment of the key blockers across data, knowledge, infrastructure, governance and organization.
- Target-state AI architectureA concrete design for how relevant systems, scientific knowledge, researchers and AI capabilities should connect.
- Integration requirementsDefinition of which systems and data sources would need to be connected during implementation.
- Governance requirementsDefinition of required access controls, human oversight, provenance and operational safeguards.
- Prioritized AI opportunity portfolioRanked implementation opportunities based on scientific value, feasibility, readiness and risk.
- First AI workflow specificationDetailed definition of the first recommended AI workflow to implement.
- 3 / 6 / 12-month implementation roadmapA sequenced plan covering priorities, dependencies and next steps.
What the first AI workflow specification covers
- Problem to solve
- Target users
- Required scientific context and data
- Systems involved
- Workflow steps
- Proposed AI actions
- Human review or approval points
- Expected outputs
- Success metrics
- Technical dependencies
- Governance requirements
- Engagement
- 2–3 weeks
- Investment
- Starting at CHF 12,500
The base engagement is scoped to one defined R&D area and typically includes 5–8 stakeholder interviews, up to 3 core scientific workflows, and the relevant systems and data sources required to assess that environment. Larger multi-team or enterprise-wide engagements are scoped separately.
The AI-Native R&D Blueprint is an assessment, architecture and planning engagement. Production integrations, custom agent implementation and OVAITY deployment are handled separately within Scientific AI Solutions.
Ready to put the blueprint into practice?
Once the architecture is specified, Scientific AI Solutions implements and validates the selected workflow with researchers.