Texas Advanced Computing Center
REDI (Reusable Environments & Digital Infrastructures) is an AI-enabled data ingest tool that enables climate scientists to efficiently upload, describe, and refine their data.


Ingested data can predict and solve future-facing climate-driven problems
Comprehensive data upload with near 100% accuracy
Using the model researchers’ metadata to drive every feature decision
Efficient data upload
Metadata input, description, and refinement capabilities
Editable YAML experience
Embedded AI
Using a conversational AI interface

Connecting a single datapoint to the bigger picture


Contextual recommendations are used to increase the impact a scientist’s data can have, ultimately addressing future climate-driven events.

Leveraging feedback loops


Use of this experience goes into a feedback loop, which improves the accuracy of the algorithm’s recommendations over time and enables TACC to better forecast and address climate-driven events.

An accessible look & feel


We leveraged open source assets for TACC as they pursue additional funding.

Strategic underpinnings driven by research & heuristic analysis


We arrived at the design by conducting research with scientists, performing a heuristic analysis of their existing tools, and synthesizing our findings.









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