ClientSynth

Cosolvent leverages Large Language Models with Retrieval-Augmented Generation (RAG) to create intelligent marketplace experiences. The system draws insights from rich user document collections—text descriptions, certifications, test reports, images, and endorsements. To fine-tune search algorithms and optimize report construction, developers need substantial datasets that reflect real-world users and usage patterns. Yet convincing real users to contribute valuable documentation requires, at the very least, that we demonstrate platform capabilities – what can it do for me? Deeperpoint’s ClientSynth utility directly addresses this dilemma. Rather than launching with minimal data, ClientSynth makes it possible to launch realistic, populated simulators from day one.The process begins with seed materials representing 10-20 examples of each user type. ClientSynth’s generative AI analyzes these patterns to create hundreds of synthetic client profiles with complete document portfolios that maintain authenticity while featuring altered names and details.

Beyond Simple Data Generation

ClientSynth generates coherent client stories—ensuring synthetic grain producers’ certifications align with their facilities, test reports reflect claimed capabilities, and endorsements come from appropriate sources. This narrative consistency creates believable marketplace participants that withstand stakeholder scrutiny. The synthetic populations enable comprehensive testing of Cosolvent deployments. Developers can refine algorithms against diverse collections and optimize workflows without compromising real user privacy.