Unconnected instruments, new assay types, and custom CRO formats create constant technical bottlenecks. Instead of waiting weeks on engineering queues, developers are now using AI to build custom schemas, connectors, and pipelines directly on top of Tetra OS.
On September 17, join TetraScience co-founder Spin Wang and solutions architect James Davies for a live build session. Starting with a raw plate reader dataset, they’ll pair AI coding assistants like Claude Code and Cursor with Tetra MCP and platform APIs to explore raw data, construct custom schemas, and deploy a production pipeline live from a local IDE.
Bring your questions about models, tooling, and governance and see how to scale data development velocity without vendor lock-in or policy risk.
Who this is for: Data engineers, lab informatics leads, and scientific IT teams looking to eliminate integration backlogs, onboard instruments faster, and deliver AI-ready scientific data across the enterprise.