Demo architectures ignore the parts that decide whether AI SaaS survives month three: eval sets that catch regressions, cost caps per tenant, timeouts and fallbacks when the model stalls, and logs that explain a bad answer to a human.
Across products, the durable pattern is to treat the model as a component with SLOs, not as the system of record. Keep business state in your database, put retrieval and tools behind explicit permissions, and ship a kill switch for any automated action that can spend money or change customer data.
Ascendra Ventures builds production AI chapters that way as fixed-price atomic problems. Glad this post focuses on systems that have been live for months rather than another chat demo.
Demo architectures ignore the parts that decide whether AI SaaS survives month three: eval sets that catch regressions, cost caps per tenant, timeouts and fallbacks when the model stalls, and logs that explain a bad answer to a human.
Across products, the durable pattern is to treat the model as a component with SLOs, not as the system of record. Keep business state in your database, put retrieval and tools behind explicit permissions, and ship a kill switch for any automated action that can spend money or change customer data.
Ascendra Ventures builds production AI chapters that way as fixed-price atomic problems. Glad this post focuses on systems that have been live for months rather than another chat demo.