Playbook
AI Project Discovery Checklist
Use this checklist before asking for an AI software estimate. It helps define the business goal, data sources, model behavior, users, integrations, risks, and launch constraints.
Best reader
Founders, CTOs, operators, and product leaders preparing an AI build
Outcome
A clearer brief that can become an architect-reviewed AI software estimate.
Use this sequence
Define the business decision or workflow the AI must improve.
List the data sources the AI can use and who owns them.
Identify users, roles, permissions, and human review points.
Document required integrations, constraints, and compliance needs.
Decide what must ship in version one and what can wait.
Start with the business workflow
An AI project should begin with the workflow it improves, not the model it might use.
Name the decision or task
Define success in business terms
Separate automation from assistance
Map the knowledge sources
The quality of an AI system depends on the data it can access and the rules around that access.
Documents and PDFs
CRM, ERP, or database records
Website and support content
Data freshness and permission boundaries
Plan the review loop
Production AI needs human review when confidence is low, risk is high, or business impact is meaningful.
Approval points
Fallback behavior
Audit trail
Escalation owner
Start in 60 seconds
Turn this checklist into a scoped plan.
Answer a few questions and get an AI-assisted, architect-reviewed scope, cost range, and timeline for your software project.
Frequently asked questions
What should be ready before estimating an AI app?
You should know the workflow, target users, data sources, integrations, and review requirements. A perfect PRD is not required, but clear constraints make the estimate more reliable.
Is model selection the first step?
No. Model selection comes after workflow, data, risk, latency, cost, and governance needs are clear.