Enterprise AI concierge for customer, sales, and operational workflows

Build an AI agent platform that acts as the front door of a business, handling reactive and proactive customer interactions while following business processes across customer support, inbound sales, and operational workflows. The product should combine models, business logic, enterprise integrations, testing, compliance controls, conversation monitoring, and deployment support.

From a16z — Decagon’s Playbook for Building Enterprise AI Applications at 30:55

Problem: Frontier models alone do not provide the business logic, integrations, governance, testing, monitoring, and deployment processes required to run AI agents safely and effectively inside an enterprise. Businesses also have more demand for customer support and other service interactions than their human teams can economically supply.

For: Large enterprises, including banks, airlines, telecommunications companies, regulated financial-services companies, and other businesses with high-volume customer interactions and complex legacy systems.

Examples

Customer support: an early customer with about 50,000 monthly support tickets made support available on every page, more prominent where users were likely to get stuck, and available to free users after AI lowered the cost of providing support.

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Behind this: 15 build steps · 5 tools and how each is used · how to validate demand · 4 more real examples · 7 things the video never answers.

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