AI Startup Book: How to Build, Pitch and Scale AI Businesses

Want to turn an AI idea into a paying business without reinventing the wheel?
AI Startup Book gives founders an operational playbook — not theory — with plug-and-run templates, pitch decks, MLOps checklists and a 90-day launch sprint.

Tired of expensive pilots that don't convert, ballooning inference costs, and regulatory surprise-storms? This book cuts straight to the operational steps that actually move deals: how to validate hypotheses faster, design low-risk pilots, control inference economics, and present an investor-grade narrative that VCs can act on. Inside you'll find actionable artifacts you can paste into playbooks or hand to your first hire:

Validate → Build → Pitch → Scale loop with a 90-day sprint and week-by-week tasks LaTeX-ready unit-econ & P&L templates and a live spreadsheet to project margins Seed & Series A pitch decks, VC Q&A scripts, and investor diligence checklists Complete MLOps & monitoring playbook: drift detection, canary deploys, alert runbooks Region playbooks for US, UK/EU and India plus 5 sector case studies (Health, Fintech, Retail, EdTech, Manufacturing) Downloadable companion files: code snippets, YAML alert templates, pitch PPTX and a metrics toolkit

This is a practical field manual for founders, product leads and ML engineers — built for real customers, not hypothetical labs. Each chapter contains step-by-step templates, checklists and real examples so you can run pilots that pay for themselves and scale profitably. What you will be able to do after reading:

Design a pilot in 30 days that proves customer ROI Build a defensible pricing and token-cost strategy that protects margin Prepare board-ready metrics, investor decks and a reproducible diligence pack Deploy safe, auditable models into production and measure drift

Includes an optional downloadable companion ZIP with editable templates and scripts to speed execution.
— Himanshoo Jaiswal