AI with Jen

I design enterprise AI language systems that improve clarity, adoption, and trust.

Carnegie Mellon trained data scientist and AI/ML data engineer.

I partner with product, engineering, and design leadership teams that need AI experiences to be understandable, dependable, and ready to scale.

Enterprise impact

  • Leads AI terminology and governance strategy for enterprise data product
  • Builds content systems for AI monitoring, terminology, and trust signals
  • Drives cross-functional alignment across design, PM, and engineering
  • Delivers frameworks teams can operationalize across products and releases

Teams I’ve worked with

Microsoft Fabric Microsoft Copilot AT&T Verizon FiOS SundaySky CaaStle UX Writing Hub Home Depot OTC Health Solutions CVS Health MinuteClinic RBC American Express John Hancock Financial Services Scholastic Ecommerce Pottery Barn Scholastic Books Kidsbooks GGwynnie Bee

What I do

I help enterprise teams ship AI experiences that are clearer to use, faster to adopt, and easier to govern.

Research and content strategy artifacts used to shape product language systems

Work

For proof from shipped enterprise product systems.

Jen Kelleman speaking at Microsoft x Red Bull Startup Innovation Labs

Speaking

For conferences, keynotes, and team workshops.

Audience participating in an AI content design workshop

Advisory

For embedded support on AI content systems and governance.

How I do it

Tools I use to design, evaluate, and ship world-class AI experiences with clarity, quality, and trust built in.

GCGitHub Copilot VSVS Studio AFAzure AI Foundry AOAzure OpenAI Service AIAzure AI Search OAOpenAI API CLClaude LMLangChain LGLangGraph LMLlamaIndex DKDSPy SBSemantic Kernel MLMLflow WBWeights & Biases HFHugging Face

“The purpose of abstraction is not to be vague, but to create a new semantic level in which one can be absolutely precise.”

— Edsger W. Dijkstra, Turing Award winner

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