KelenAIStart with the workflow
Define the trigger, accepted result, owner, evidence, and exception path before selecting the AI mechanism.
Founder & AI engineer
Kevin Li builds AI around the way a business actually operates.
Kevin is the founder of KelenAI, a forward-deployed AI engineering practice based in Los Angeles, California, and serving businesses across the U.S. His work focuses on connecting AI, software, people, controls, and measurable outcomes inside complete business workflows.
Engineering background
Experience operating AI at engineering-team scale.
Before founding KelenAI, Kevin worked in AI engineering at a global technology company with large-scale engineering operations. There, he helped a nine-person engineering team use AI to automate parts of software development. The team's measured development velocity increased by approximately 100%—roughly twice the output in the same amount of time.
Valued using fully loaded employer cost, that additional output was equivalent to approximately $1.5–$1.8 million in annual engineering capacity. This is a capacity estimate from Kevin's previous work—not booked savings or a promise of results for KelenAI clients.
Today, Kevin applies the same operating discipline to small-business workflows across sales, operations, customer service, orders, inventory, documents, and follow-up. The objective is not to insert AI everywhere. It is to determine where AI is justified, preserve human authority where it matters, and make the resulting workflow testable and maintainable. Read how KelenAI approaches AI workflow automation from Los Angeles for companies in California and across the U.S.
How that experience translates
The reusable lesson is the operating model—not the headline number.
Increase authority with evidence
Move from observation to assistance and controlled action only when tests and operating results support it.
Measure the complete result
Track cycle time, completion, exceptions, rework, adoption, and downstream quality—not isolated model output.
Selected field guides
Read the operating principles.
A practical first step
Bring one workflow and the evidence around it.
Bring us a workflow that is repetitive, cross-system, exception-heavy, or simply too dependent on one employee. We will help you decide whether AI belongs in it.