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.