Building AI companies around problems people already have
A practical approach to finding a useful problem, validating the workflow, and turning a working solution into a business.
The business starts before the model
An AI company needs a customer with a problem worth solving. Start with a specific job: qualifying a lead, answering a repeated question, preparing an estimate, or moving information between systems. Learn how that job is handled today and what a better result would mean.
Nick Cornelius worked in production, project management, and sales in the pharmaceutical industry before starting his companies. After many businesses that failed, he built SimpliScale in November 2022. He later went on to build KingCaller AI and AIPhonez. That progression grounds this guide in practical business problems rather than a particular technology.
Validate the workflow with real users
Talk to the person who does the work and the person who pays for it. They may value different things. One wants fewer interruptions; the other needs a reliable outcome and an understandable cost. Look for a problem both can describe clearly.
A small pilot should test whether the solution fits the real process. Use representative examples, agree on what success means, and record where the system needs help. Interest in a demo is useful feedback, but continued use is a stronger signal that the product belongs in the workflow.
Build the smallest complete outcome
A useful first version should complete a narrow job from beginning to end. That may include receiving a request, producing a draft, asking a person to review it, and saving the approved result in the right system.
Avoid adding a broad collection of features before that first job works. Every integration and setting creates something else to support. A focused product gives customers a clearer reason to use it and gives the team a clearer way to evaluate it.
Design for the moments that go wrong
Incomplete information, unexpected questions, unavailable integrations, and misunderstood requests are normal parts of operating software. Decide how the product should respond: ask a clarifying question, retry safely, or pass the case to a person.
Keep a record of the cases that fail and use them to improve the product. Customers also need a clear way to reach support and understand which actions the system has taken. Reliability includes recovery, not just successful demonstrations.
Understand delivery costs and repeatability
Account for the full cost of serving a customer: model usage, telephony where relevant, hosting, integrations, onboarding, and human support. Check whether customers need mostly the same workflow or a different implementation each time.
Custom projects and repeatable products can both be valuable businesses. The delivery model should match the work customers actually need. SimpliScale focuses on custom systems and automation; KingCaller AI and AIPhonez address AI voice workflows.
Keep learning after launch
Watch what customers use, where they stop, and which tasks still require manual effort. Review support conversations alongside product usage. A feature request can reveal a broader problem, but it should be understood before it becomes a commitment.
Building an AI company is an ongoing process of improving a useful outcome. The next step is usually a clearer workflow, a more reliable handoff, or a better understanding of the customer.