Clinical research sites are moving fast on AI. Across the industry, teams are looking for ways to use large language models to forecast enrollment, analyze visit trends, and surface operational insights from their own data.
The challenge? Most site technology platforms don’t give you a way to connect your data to AI tools. Your data is locked inside proprietary systems with no clean path out.
CRIO is different. Every CRIO customer already has access to a modern data infrastructure, powered by Google BigQuery and Looker, that makes your site data AI-ready today. No custom integrations. No data engineering project. No waiting for a vendor roadmap.
This post walks through exactly how to connect your CRIO data to an AI assistant like Claude, and why CRIO’s architecture gives you a head start that other platforms simply can’t match.
Why This Matters for Research Sites
AI tools are only as useful as the data you feed them. If your operational data lives in a system that doesn’t expose it cleanly, you’re stuck. You end up manually exporting spreadsheets, copying and pasting, or waiting months for your vendor to build an integration.
CRIO’s data platform eliminates that bottleneck. Your visit data, SiteApp activity, enrollment metrics, and operational data all flow into BigQuery, and Looker gives you a governed, user-friendly way to access and schedule reports from that data.
That means you can connect your data to AI tools right now, on your terms, while maintaining full control over what gets shared and how.
Two Paths to Connect CRIO Data to AI
There are two approaches, depending on your team’s technical comfort level and what you’re trying to accomplish. Most sites will find the Looker path faster and easier, but BigQuery offers more flexibility for advanced use cases.
Comparison at a Glance
| Looker (Recommended) | BigQuery (Advanced) | |
| Best for | Business users, operations leads, site managers | Data analysts, technical teams, custom reporting |
| Technical skill required | None. Point-and-click setup. | Moderate. Requires BigQuery access and familiarity with SQL or API connectors. |
| Setup time | Minutes | Hours to days, depending on approach |
| Data control | You choose exactly which Looks (reports) to share. Nothing else is exposed. | Full dataset access. Potentially requires BAA with your AI provider, and/or careful scoping to avoid exposing sensitive data. |
| PHI considerations | Strong. You control the data at the report level. Only share de-identified or aggregated Looks. | Requires more care. BigQuery contains all data available in your account, so you will need to take appropriate precautions such as having your AI LLM provider sign a BAA, managing access controls, and ensuring queries exclude PHI. |
| AI connection method | Schedule Looker exports to Google Drive, then connect your AI tool to that folder. | Use the AI tool’s BigQuery connector (if available) or export query results to a shared location. |
| Automation | Built-in. Looker schedules keep your data fresh automatically. | Possible, but requires scheduled queries or external orchestration. |
| Flexibility | Limited to existing Looks and reports. | High. You can write any query against your full dataset. |
Our recommendation: Start with Looker. It’s faster to set up, easier to manage, and gives you strong data governance out of the box. If you outgrow it or have advanced use cases, BigQuery is always there.
How to Set It Up: The Looker Approach
Here’s a step-by-step walkthrough using Claude as your AI assistant. The same general approach works with other AI tools that support Google Drive or file-based input.
- Identify the Looker Look you want to analyze. This could be a SiteApp visit summary, enrollment tracker, revenue forecast, or any existing report. If you need a new Look, your CRIO account team can help.
- Schedule the Look to export to Google Drive. Open the Look, click the gear icon, and set up a scheduled delivery to a dedicated Google Drive folder. Choose a refresh frequency that matches your needs (daily, weekly, etc.).
- Connect your AI tool to that Google Drive folder. In Claude, for example, you can create a Project or use Cowork and point it to the Drive folder. Instruct it to always use the most recent file.
- Ask questions. Once connected, you can prompt your AI assistant to summarize trends, forecast metrics, flag anomalies, or generate shareable dashboards from your data.
- Iterate and expand. Start with one Look, prove the value, then add more. Each additional data source you connect makes the AI more useful.
The entire setup takes minutes, and because Looker handles the scheduling, your AI assistant always has access to up-to-date data without any manual effort.
What Can You Do With It?
Once your CRIO data is connected to an AI assistant, the use cases are wide open. Here are a few examples sites are already exploring:
- Revenue forecasting from SiteApp visit data
- Enrollment trend analysis across studies and sites
- Operational dashboards generated and shared as interactive artifacts
- Data quality checks that flag missing or unusual values in your trial data
- Ad hoc analysis in plain English, without writing SQL or building custom reports
A Note on Data Governance and PHI
Any time you connect data to external tools, governance matters. The Looker approach gives you strong, practical controls:
• You choose exactly which reports to export. No bulk access, no open pipelines.
• Use aggregated or de-identified Looks to avoid exposing PHI.
• Google Drive access controls let you restrict who can see the exported files.
• AI tool data policies should be reviewed to confirm how data is handled. For example, Anthropic’s Claude does not train on customer data.
This approach puts your site in full control. You decide what data leaves your environment, where it goes, and who has access.
A word of caution about free AI tools. Many free-tier AI products, including free versions of ChatGPT, Gemini, and others, use your inputs to train their models by default. That means any data you paste or upload could be retained, learned from, and potentially surfaced in outputs to other users. For clinical research data, this is a serious risk.
Before connecting any data to an AI tool, review two things:
1. Training policies. Does the tool use your data to train or improve its models? Free tiers almost always do. Paid and enterprise plans typically do not, but read the fine print.
2. Data retention policies. How long does the tool store your inputs? Is your data deleted after a session, or retained indefinitely? Understand where your data lives and for how long.
The bottom line: if you’re working with site operational data, enrollment information, or anything that could include PHI, use a paid or enterprise-grade AI tool with clear contractual commitments around data handling. Don’t cut corners with a free account.
Why CRIO Customers Have a Head Start
This isn’t a feature on a future roadmap. It’s available to CRIO customers right now, using infrastructure you already have.
Most competing platforms don’t offer anything comparable. They lack the modern cloud data layer that makes this kind of integration possible. Their data is locked inside closed systems, and connecting it to AI tools would require significant custom development, if it’s possible at all.
With CRIO, your data is already structured, governed, and accessible through BigQuery and Looker. That foundation makes AI adoption a practical, low-risk next step rather than a major IT project.
Ready to Get Started?
If you’re a CRIO customer interested in connecting your data to AI, we’re here to help. Reach out to your Customer Success Manager or [email protected] and we’ll walk you through the setup. It’s faster than you think.
Want to see this in action? Join us at CRIO Connect Live in Raleigh, North Carolina, where we’ll be demoing how sites are using AI today in their practices. We’d love to see you there.
Your site’s AI strategy starts with the data you already have.