Start with
the business.
An AI tool is not a business system. The useful question is what work needs to happen, what decision it supports and how you will know it has worked.
Aaron combines commercial strategy with hands-on AI implementation. Paloren is the agency he is building around that connection. Lighthouse is a separate marketing data product, while Louder.Online focuses on marketing and growth.
Define the job before the model
Start with a commercial objective and the current workflow. Identify the inputs, the decisions, the handoffs and the cost of an error. That makes it possible to distinguish an automation opportunity from a task that still requires human judgment.
Connect intelligence to execution
Research only matters if it informs a decision. An agent only helps if its output moves useful work forward. The system needs source data, interfaces and ownership of the next step, not just a convincing demonstration.
Build in verification
Quality checks, cost limits and human review belong in the design. Decide which outputs can progress automatically, which need evidence, and which require approval. When a system fails, the team needs a visible route to recovery.
Practical areas of interest
Aaron's hands-on work and exploration include research workflows, prospect intelligence, lead qualification, coding agents, model routing and orchestration. These are areas of practice, not a promise that every application suits every business.
Bring a business problem
A useful first conversation starts with the process you want to improve, the people involved, the systems already in use and the constraints you cannot ignore.