Mediforce x Databricks: Human-in-the-Lead AI Workflows While Your Data Stays in Databricks

The biggest tradeoff in pharma is the most obvious one: sensitive data must only leave the infrastructure if required. Regulatory requirements and security policies often prevent moving sensitive clinical data to external systems. Mediforce's new Databricks orchestration helps bridge all the good that Mediforce brings to this exact sense of security. The Databricks integration in Mediforce works quickly and uses your existing jobs so that you get: unified workflow management, human-in-the-lead conditional AI-enabled workflows, audit trails, and meta workflows all in one place, while your data stays where it is and never leaves your Databricks notebooks.

What This Means for Your Data Pipeline

Nothing changes in terms of what works for you already. Your notebooks, your jobs, and your Databricks setup remain unchanged. Mediforce acts as an orchestration layer that triggers jobs to your Databricks cluster, monitors execution, handles failures, and manages dependencies, all while your clinical trial data, patient information, and proprietary research never leave your network perimeter. You get the full power of Databricks' distributed computing, machine learning capabilities, and analytics tools, just without the data movement that keeps your compliance team awake at night with the Mediforce-powered AI-enabled workflows on top of it. It is a solution that bridges and enables instead of asking your teams to reinvent the wheel.

Why This Benefits You Today

Mediforce is an all-in-one, human-in-the-lead AI orchestrator that is built for Pharma. You can create incredibly diverse and detailed workflows with visibility, audit trails and each step completely isolated from the last. So, the AI only ever gets what you send it. The way this integrates with your Databricks jobs is that you only send what you truly need to send into an AI agent's step with extreme visibility and control over what it does, what frontier model enables it, what skills it has access to, and so on. You can also have human-review steps as well as decision steps.

The integration works with the following key pillars:

Job IDs & User Token

Mediforce strictly only needs your existing job ID and a user token passed as a secure secret. Once that is configured, the Mediforce workflow step for Databricks can just run your job which streams its final output to the workflow step. Once that is done, your workflow can then continue through the many ways Mediforce enables you: you can make automatic decisions, you can try re-runs, you can email stakeholders with automatically generated reports, you can have a complex branching workflow as well. Your existing Databricks access controls, table permissions, and network security policies remain in effect. Mediforce doesn't need elevated data access. It only needs permission to submit and monitor jobs.

Data Access

No data access is necessary except what the job streams back as the result. This is usually what a notebook or script emits as the final output. That is all Mediforce will ever have so as long as you keep the output curtailed to what is strictly necessary for the next steps to continue, your data security and compliance team can rest easy.

Monitoring and Control

Mediforce polls job status, collects logs, and provides real-time visibility into execution. Databricks jobs can fail for dozens of reasons: cluster startup issues, driver memory problems, dependency conflicts. Mediforce adds configurable retry logic and can route different failure types to appropriate remediation workflows.

Resource Management

The orchestrator can spin up job clusters on demand or use your existing interactive clusters, depending on your cost optimization and performance requirements. It works as it does currently so nothing changes as far as your resources are concerned. You own your workflow and with the interactive Co-Work workflow creator, you only need to describe what you want and watch as the Mediforce internal tooling understands your workflow and sets it up in real-time.

Scheduling Flexibility

Complex dependencies between clinical data processing, regulatory reporting, and research analytics become manageable through Mediforce's DAG-based scheduling, rather than trying to coordinate through cron jobs or Databricks' built-in scheduler.

Closing Note

The AI exists, the agents are ready, Mediforce is available today to enable your teams to have a human-in-the-lead AI solution where each step is isolated from the last, where you control every little detail of the workflow, and where you have absolute visibility in what flows where. If your team is ready to modernize their existing clinical data processing under tight security requirements, Mediforce's Databricks orchestration gives you a way to do it without trading performance for compliance and while keeping your data within the secure bounds of your own infrastructure.

Register for the Validated AI for Pharma Summit to see how Mediforce runs Databricks workflows with human-in-the-lead AI, without moving your data.