Uraion Labs researches and builds adaptive AI systems. We focus on efficient models, persistent memory, and reliable execution.
Source build · in development
Turn maintenance tickets into checked, reviewable changes. A bounded workflow for owners of small software consultancies.
Research gives us new methods. We turn them into systems, put them into products, and learn from how they perform.
Frontier models handle difficult planning. Smaller models handle focused tasks.
Store useful procedures and past failures so the system does not start from scratch.
Use tests, tool output, and structured review before accepting a result.
Support private and local execution where it is useful.
We study how agents plan, delegate, remember, and check their work. We also study how to run them efficiently.
We use open models, local deployment, and alternative architectures when they offer a clear advantage.
Research finds methods. Systems make them usable. Products put them to work.
How agents plan, split work, run tasks in parallel, recover, and escalate.
How systems store and retrieve useful procedures, context, and past failures.
How smaller models can handle focused tasks within clear limits. See a local-model example; the demonstration uses Qwen, not a Uraion research model.
How to use the smallest capable model, including on private infrastructure.
How to test real workflows with tools, structured checks, and model review.
How systems can learn from validated experience without retraining for every new lesson.
We research the methods.
We build the systems.
We ship the products.
Tell us what you are working on. For a Fabric paid-pilot application, choose the pilot option and describe one recurring maintenance workflow.