In manufacturing, repetition is the key to excellence. Systems like Lean Manufacturing, Kaizen, and Just-In-Time production rely on continuous, incremental improvement of processes that are performed over and over again. But what happens when markets are thin—when transactions are rare,…
Cosolvent is not a finished product. It’s an experiment—an open-source playground designed to test the possibilities and limits of AI-enhanced market matching. The Unknowns: These are open questions. Cosolvent is being released not as a polished solution, but as a…
Thin Market Characteristics Assessment Information Asymmetry: HIGH Complex Matching Criteria: VERY HIGH Scattered Participants: MODERATE Economic Viability Analysis Transaction Value vs. Platform Costs: STRONG Friction Cost Reduction: VERY HIGH Risk Mitigation Value: EXTREMELY HIGH Revenue Model Potential Pricing Power: STRONG…
As powerful as Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) can be, they come with real risks—especially when applied to market matching in thin and high-stakes environments. Common Risks: How Cosolvent Minimizes These Risks: Cosolvent’s approach blends automation with…
When Nobel Prize winner Alvin Roth identified “thin markets” as a fundamental economic challenge, he highlighted a problem that has plagued commerce for centuries. These markets, where buyers and sellers are widely scattered and have difficulty finding each other, represent…
There are a lot of considerations that determine whether a given type of “thin market” is a good candidate for AI assistance. No market will score high on every metric, but a lot of markets may have enough of these…
