We Teach AI · Applied AI consulting
Bring the recurring task. We’ll help define, build, and test a better way through it.
Electrotoast combines product thinking, software development, and hands-on work with AI. The starting point is a real workflow: who uses it, where it gets stuck, and what a good result looks like.
From a task to a useful result
The task, its inputs, and the current workaround.
Examples, application access, and clear boundaries.
Working output, human review, and a repeatable test.
What we have put into practice
These are Electrotoast product and prototype examples. Each shows a different role for AI, with its current limits kept visible.
Mobile capture, AI analysis, and connected services help bring object research and listing preparation into one workflow.
A conversational interface for asset search and bounded scene tasks in 3ds Max and Maya, including changes to placement and orientation.
Structured synthetic interviews and comparison exercises help surface assumptions, alternative workflows, and questions worth taking to customers.
AI-assisted coding and visual development support a geography-game prototype with train travel, crystals, and a connected five-city world.
Ways to work together
A focused first engagement makes the work easier to evaluate and the next decision easier to make.
Map a recurring workflow, inspect representative inputs, and identify where AI could help.
A scoped opportunity and a testable success measure.
Connect the relevant tools and data. Create a small, usable flow with review points and visible failure cases.
A demonstrable prototype and a repeatable evaluation.
Refine the interface, document the instructions, and establish who checks, maintains, and operates the workflow.
A practical handoff and a clear next-step plan.
How we evaluate the work
A convincing demonstration should survive more than the example it was built around. We look at a baseline task, inspect the output, and try a new constraint to see what changes—and what should stay intact.
Useful measures depend on the job: completion time, correction effort, errors, or whether someone returns to use the workflow. We agree on the measure before claiming an improvement.
Let’s look at the task
Tell us what you are trying to finish, the tools you already use, and where the process breaks down. A short description is enough to start.
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