Little Planet LabsAI strategy consulting
There are times language models genuinely reduce effort, and times they only demo well. We help teams tell the difference, then turn the first kind into shipped software.
Who this is for
Teams with an existing product who want to know where AI genuinely helps — before committing budget to features that will not survive contact with real users.
Engineering leaders weighing AI-assisted development: software factories, agents in the workflow, and the human processes around them.
Founders and product owners who need an honest technical assessment of an AI idea — including the answer “not this one.”
How AI strategy engagements work
Most start with a short conversation about what you are building and where it is stuck, followed by a written scope with a fixed first milestone. From there, the work can stay advisory — an assessment, a set of recommendations, a roadmap — or continue into designing and building the feature itself.
When is AI worth adding — and when is it not?
AI earns its place when the task is genuinely open-ended — language, unstructured data, flexible interfaces — and when approximate answers are acceptable. It does not when a simpler deterministic solution does the job, when the task needs guarantees language models cannot give, or when the economics do not close. Part of the job is telling you honestly which one your idea is.
We wrote a full guide on this question, hiring included: how to choose an AI consultant in Austin — and when not to hire one.