Why do SI features in SaaS products fail after launch?
SI features in SaaS products usually fail after launch because nobody is measuring them, so quality slips quietly as prompts, models and user behaviour change. A demo is tested on a handful of friendly examples, while production brings messy inputs, edge cases and new model versions. The common gaps are predictable: no evaluation set built from real user tasks, prompts edited ad hoc instead of versioned and reviewed like code, context windows that overflow in long sessions, and inference cost that grows with adoption. The fix is to build the evaluation harness before the feature, run it on every deployment, and track quality, faithfulness, latency, cost and safety with regression alerts. Usage analytics then show whether the feature lifts activation and retention or sits unused. Octalcode applied this approach to Cyest SIMS AI, embedding copilots across an engineering-ops console with a custom eval harness that runs regression suites on every model change.
