AI is treated like infrastructure, not decoration
Constrained, workflow-ready AI instead of vague automation claims.
AI commerce proof
Auto-Ecom
Automated e-commerce workflow that scores product opportunities, structures market analysis, and turns launch prep into a repeatable operating path.
AI-driven product evaluation and launch prep, all in one workflow.
Role
AI-assisted commerce workflow proof
System proof
Structured product scoring, market review, and content generation
Stack
FastAPI, GPT-4, Pydantic, e-commerce workflow design
Live scope
Opportunity evaluation through launch-oriented commerce outputs
Business problem
Most AI commerce ideas fall apart when the workflow stops at generic recommendations
Commerce operators need help deciding what deserves attention and moving from evaluation to launch prep. Auto-Ecom structures product review, viability analysis, and content generation to support launch decisions instead of acting like a prompt box.
System choices
Constrain the AI layer so the next decision gets easier
Structured inputs and predictable scoring keep evaluation consistent.
Operating proof
Why this matters
Commerce decisions can be productized
Product review and launch prep become a single operating system, not scattered manual tasks.
Typed outputs improve trust
Structured analysis and generated content live in the same system without collapsing into AI theater.
What I learned
Narrow AI workflows outperform broad promises
Better AI comes from solving one problem really well.
Need AI to support a real business workflow?
I build AI workflows around evaluation, structured outputs, and operational decisions -- not chatbot skins.