KelenAIObserve before proposing
We study the work employees actually do before selecting a platform or drawing the future process.
About the studio
Engineering discipline for the messy middle of AI adoption.
KelenAI is a founder-led forward-deployed AI engineering practice based in Los Angeles, California, and working remotely with businesses across the U.S. We work directly with owners and operators to turn fragmented AI experiments into complete, testable, and maintainable business workflows.
Why this studio exists
The tools are getting easier. Making them operational is not.
Small businesses can now access the same language models and automation tools as much larger companies. But access does not answer the difficult operating questions: which process to change, what data to trust, what AI may decide, what employees should review, and who owns the workflow when something fails.
We bring a software-engineering approach to those questions. That means clear interfaces, explicit rules, representative tests, observable failures, and a design that fits the tools and people already inside the business. Our guide to what a forward-deployed engineer owns explains how that responsibility differs from advice, configuration, or a technical handoff.
This is an early-stage, founder-led practice. We take on a small number of design partners, keep the first scope narrow, and earn the right to expand by proving one useful workflow in the real operating environment. See our implementation process and workflow services for the operating boundaries behind that approach.
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, he applies the same practical engineering approach to workflows across sales, operations, customer service, orders, inventory, documents, and follow-up. Learn more about Kevin and read his field guides.
Working principles
What clients should expect.
Use the simplest sufficient tool
Native features and well-supported products come before custom code when they can do the job well.
Make risk visible
Assumptions, decision boundaries, exception paths, and ownership are documented instead of hidden in a prompt.
Measure reality
We prefer cycle time, exception rate, completion rate, and adoption data over impressive but unverified demos.
Best fit
Founder-led U.S. businesses without an internal engineering team.
A real workflow crosses email, documents, spreadsheets, and business software.
Employees spend meaningful time coordinating, copying, checking, or following up.
The owner wants an operating result—not another disconnected AI subscription.
A responsible employee can confirm rules, exceptions, and successful outcomes.
A practical first step
Start with one workflow worth fixing.
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.