Find a useful place to start
Review the work, data, and available tools. Compare options and choose a focused AI use case with a clear way to judge whether it helps.
Enterprise AI consulting in Kansas City
Not sure where AI fits? We help Kansas City businesses find useful applications, connect them to existing systems, and improve pilots that aren’t working as expected.
Start with the work you want to improve
Maybe your team spends hours searching for answers or sorting through documents. Maybe you’ve tried an AI tool, but it hasn’t found a place in the way people work.
We help you evaluate the options, build a useful starting point, and connect it to your systems. Sometimes a straightforward integration or automation is enough.

Review the work, data, and available tools. Compare options and choose a focused AI use case with a clear way to judge whether it helps.
Build generative AI assistants and workflow automation connected to your existing software and permissions. Retrieval-augmented generation (RAG) helps an assistant use approved business information when answering questions.
Find what is limiting quality, reliability, cost, or adoption. Work through the changes needed to make it useful and supportable.
How we help
AI needs more than a good answer in a demo. We plan how it will use information, handle mistakes, and fit into your team’s work.
Agree on the problem, data access, and what improvement would look like.
Check answer quality and edge cases, then connect the useful parts to your systems.
Set permissions, human review, cost monitoring, and a fallback for mistakes, with someone responsible for handling exceptions.
Hands-on product engineering
CoffeeBreak is our AI orchestration product: it brings tools, automated steps, and people together around a task. Its product pillars are developing in stages.
Launch Check is the first available CoffeeBreak product, offering a readiness review for software built with AI.
A few common questions
We review a task with your team, the information it needs, and how you’d measure improvement. Then we compare AI with the tools and automation available to you.
Yes. We check it with real examples and plan the data access, integrations, monitoring, human review, and support it needs for everyday use.
Yes. We review what’s going wrong, from answer quality and cost to the way people use it, then recommend focused changes or a simpler approach.
Yes. We review your software, data access, and permissions before recommending tools or building connections.
We agree on what AI can access and do, where a person must review the result, and what happens if it makes a mistake. The controls should fit the task and its risks.