Compressing the R&D provisioning cycle from days to hours

An AI-enabled self-service provisioning platform for a global biopharma R&D organization

Client situation

The client is a global biopharmaceutical organization advancing the discovery, development, and delivery of innovative medicines and vaccines. Its R&D function operates a sizeable Databricks and Azure estate used by scientists worldwide for computationally intensive analyses across the drug discovery lifecycle. As experimentation volumes scaled, the existing process for standing up Databricks workspaces and supporting Azure resources had become a constraint on research velocity — manual, opaque, and dependent on a small group of platform engineers.

• Cycle time. Provisioning cycle time averaged more than five days per request.
• Manual effort. Approvals and configuration steps were carried out manually across several handoffs.
• R&D teams depended on a small group of platform engineers for every environment.
• Requesters had no real-time view of progress, status, or expected completion.

Our approach

Working alongside the client's platform engineering organization, the team designed a cloud-native, API-driven self-service portal for Databricks and associated Azure resources. The work was organized across four parallel workstreams.

1. Guided intake

Researcher-facing request forms were structured around the language scientists use, with real-time validation of inputs against platform standards. This shifted error detection from late-stage rework to point-of-entry, removing a major source of rejected requests.

2. AI-driven configuration

An AI agent was introduced between the intake layer and the provisioning APIs. The agent translates scientist-friendly inputs into the precise, API-ready configurations required by Databricks and Azure, removing the need for researchers to understand underlying platform constructs.

3. Secure, automated provisioning

Workspace and resource creation was fully automated through native APIs, with role-based access control integrated to Azure Active Directory, secrets brokered through Azure Key Vault, and audit-ready logs captured for every request from intake through completion.

4. Reusable platform framework

The portal was built as a reusable platform framework rather than a point solution, with a modular design that allows additional Databricks services, Azure resource types, and downstream integrations to be onboarded without rebuilding the request and provisioning core.

Outcomes

• Provisioning cycle time reduced from more than five days to under 24 hours.
• Approval and configuration handoffs eliminated from the standard request path.
• Platform engineering capacity redirected from repetitive provisioning to higher-value work.
• Configuration errors and downstream rework materially reduced through AI-driven validation.
• Researcher experience. R&D users gained a direct, self-service channel with real-time visibility into request status.
• Role-based access, secret management, and audit logging maintained end-to-end across requests.

Looking ahead

With the core provisioning experience in production, the client is positioned to extend the same model across the wider R&D platform estate. Near-term priorities include expanding the catalog of self-service resource types, deepening the AI agent's reasoning over platform policies, and applying the underlying framework to additional cloud services and research workloads — converting a single point of automation into a consistent self-service operating model for R&D.

Engagement snapshot