Stop paying thousands to consultants for custom AI solutions—learn to build reasoning models yourself and own your competitive advantage.
This is a practical guide that teaches small business owners, technical founders, and in-house developers how to construct reasoning models from the ground up without relying on pre-built SaaS platforms or expensive consulting firms. Written by Sebastian Raschka, a respected machine learning expert, the resource walks you through building AI systems that can solve your specific business problems—whether that's automating customer decision-making, improving product recommendations, or analyzing complex operational data.
Instead of adapting your business needs to existing tools, you'll learn to build models tailored to your exact workflows. This puts you in control of your data, reduces long-term costs, and eliminates vendor lock-in. For small businesses running on tight budgets, the ability to build custom reasoning systems in-house can save tens of thousands in consulting fees and monthly subscription costs while delivering better results.
Technical founders, software development teams within small businesses, e-commerce owners needing custom product ranking systems, marketing agencies building proprietary analytics tools, SaaS companies embedding AI into their products, consultancies offering custom solutions to clients, and any small business with $50,000+ annual spend on external AI tools or consulting.
Pricing varies by format—paperback, eBook, and video editions available through Manning Publications, typically ranging from $30–$60 depending on the edition. This is a one-time purchase with no recurring fees, making it extremely cost-effective for building internal capabilities.
A small business paying $2,000–$5,000 monthly for AI consulting or SaaS platforms can recover the course cost within a single month by building one in-house system. Development teams spend an average of 200–400 hours annually integrating multiple AI tools; building custom reasoning models eliminates fragmentation and saves 15–20 hours per integration. Companies that successfully build proprietary reasoning models report 30–40% improvement in decision accuracy for their use case compared to generic platform solutions, directly impacting revenue through better customer targeting, faster problem-solving, and reduced operational waste.