Technical Strategy and Model Application - Decagon allocates 90% of its workflow to open-source models to optimize voice agents and reduce latency, while utilizing frontier models for the remaining 10% of new projects [1] - Smaller, fine-tuned models outperform large state-of-the-art models on specific tasks while achieving better latency and cost efficiency [1][2] - Decagon Labs functions as a model factory to compress the time required to fine-tune new models for specific enterprise tasks [4] Enterprise Go-To-Market and Commercial Operations - Enterprises experience faster iteration speeds, enabling the deployment of 7 new customer journeys in a month compared to the previous rate of 3 journeys per year [61][62] - Decagon targets a product-driven approach with a glass-box model to provide transparency and control, contrasting with traditional black-box deployment models [61][62] - Founders dedicate approximately 80% of their time to sales and accelerating execution across large enterprise clients including global banks, airlines, and telecommunications companies [71] Industry Trends and Workforce Impact - AI adoption creates latent demand for customer support by expanding access channels and lowering operational costs rather than simply reducing headcounts [119][120] - Customer support ticket volumes reached 50,000 per month for an early customer before expanding support accessibility across all web pages [119][120] - Software-as-a-Service (SaaS) and application layers remain essential for capturing business logic, managing legacy integrations, and maintaining enterprise compliance [22][26]