TetraScience Technical Showcases

Lead clone selection, media optimization, and culture monitoring share a common problem: the data is siloed, inconsistent, and hard to act on at speed. On October 15, we'll show what changes when you build a unified data foundation underneath them, and what AI-assisted workflows look like on top of it.

Live demos across three production use cases: clone and media titer prediction, image-based cell culture analysis, and natural language querying of bioprocess data. Real models, real data, real workflows.

Scientific AI applications are only as good as the data underneath them. When a model hallucinates, or when an AI-assisted workflow produces something you can't defend, the root cause is usually data that arrived without context.

On November 12, we'll show how to keep that from happening. The session focuses on the two steps that matter most for AI readiness: metadata extraction and scientific contextualization.

What you'll see:

• What the gap between raw instrument data and AI-ready data looks like in practice
• How to get more  context to show up with the data and avoid having to re-label the rest of your files one by one.
• How governed vocabularies keep labels consistent across instruments and sites and record where every value came from
• What well-contextualized data makes possible in search, analytics, and AI workflows

Who this is for: Scientific IT teams managing lab data infrastructure, data engineers building pipelines, and R&D leaders who want to understand what’s limiting their AI workflows.

Two sessions on November 12:
9:00 AM ET (3:00 PM CET) · 12:00 PM ET · 45 minutes · Live demo + Q&A

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