Start with a job, not a model
The strongest AI SaaS products do not begin with “we should add AI.” They begin with a repeated job that is expensive, slow, or difficult to do consistently. The model is one part of the solution; the workflow, interface, data, and feedback loop are the product.
Define the quality bar
Before building, collect representative examples of good and bad outputs. Decide what “useful” means: accuracy, completeness, tone, speed, citation quality, or a human approval step. This gives the team something concrete to evaluate.
Design the system around change
Models, prices, context windows, and provider capabilities change quickly. Keep model calls behind a small application boundary, log the inputs and outputs safely, and make it possible to compare providers. Avoid coupling the entire product to one prompt or vendor.
Ground answers in the right data
For document and knowledge products, retrieval is usually more important than adding a longer prompt. Clean the source data, preserve useful metadata, retrieve the right passages, and show users where an answer came from when trust matters.
Build evaluation into the MVP
An AI feature needs a test set. Run it whenever prompts, models, retrieval, or code changes. Track failures by category and let users correct outputs. Those corrections become the roadmap for the next version.
Keep the first version narrow
One reliable workflow beats a collection of unreliable AI features. Start with the user action that creates the clearest value, then add automation, collaboration, and breadth after real usage shows where they matter.
Vellory helps founders move from AI product idea to a focused, production-ready application with the product and engineering decisions connected from the start.
