Post by Descartes & Mauss

In AI, the easy thing is to build a wrapper. Take a generic LLM. Add a nice interface. Add a few prompts. Generate a polished answer. Useful? Sometimes. Defensible? Not really. At Descartes & Mauss, we are building something different: an AI decision platform for growth strategy, designed around business logic, company context, and agent orchestration. The challenge we solve is not “generate more content.” It is this: How do you turn fragmented market signals into strategic options that are specific to a company’s markets, priorities, capabilities, and constraints? That requires more than an LLM. It requires a structured workflow: → ingesting and processing heterogeneous market data → building a contextual representation of the company → linking signals to products, channels, priorities and constraints → orchestrating specialist AI agents across the analysis process → consolidating outputs into decision-ready growth options Amazon Web Services (AWS) has now published a case study on how we migrated and industrialized this workflow on their infrastructure, supporting global enterprise clients including Colgate-Palmolive, Kraft Heinz and Danone. The results: ⚡ ×3 faster insight delivery ⬇️ −75% processing time 📉 onboarding reduced from 4 weeks to 2 ✅ 99.9% availability But the most important point is not speed. It is that strategy AI needs to move beyond generic answers. The future will not belong to companies that simply wrap LLMs. It will belong to companies that encode domain expertise, business context and decision logic into scalable systems. That is what we are building at Descartes & Mauss.