We build AI systems that operate inside the software professionals already depend on — where the work is exacting, the data is real, and a wrong answer has someone's name on it.
Most AI tools sit beside professional work rather than inside it. They answer questions about a document, produce something to paste, and leave the operator to reconcile the two.
Working inside the application is a different discipline. A system has to read state it did not create, act through interfaces built for humans, and be accountable for changes to files other people own. Correctness stops being a quality metric and becomes the product.
Reading real state, making real changes, and being answerable for them.
State, memory and recovery measured in hours — including what a system owes the user once its own knowledge has gone stale.
What serious AI work costs once it runs for forty steps instead of one, and which levers make it viable rather than impressive.
Small, applied, and shipping. The lab exists to put working systems in front of people who do exacting work for a living, and to learn from what happens next.
Research follows the building rather than preceding it. What we publish is what we learned making something hold up in production — written for anyone facing the same class of problem, not as a tour of our own machinery.
An AI analyst that works inside Excel: it reads the workbook, builds the model, traces the logic, and answers for what it changed. Built and operated by Velora.
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