Building, integrating and scaling AI-powered workflows (www.nunodonato.com)

An AI engineer at Trippz describes a year-long journey from a two-week prototype to a production-grade AI workflow, illustrating why early wins don’t equal production readiness. The team got a useful result in minutes during the first iteration, but variability in inputs and model behavior meant real reliability took much longer: it took about 10 months to reach a "zero" failure rate and a full year of iterative work to optimize performance and cost. Concrete gains: average runtime fell from ~10 minutes to ~60 seconds, and per-run cost dropped from roughly $4.00 to $0.50. The story highlights lessons for the AI/ML community and decision-makers: prototypes can mask operational fragility, and turning them into scalable systems requires deep business knowledge, repeated experimentation, input engineering, and system-level optimizations that LLMs alone won’t provide. These optimizations materially improve usability, enable confident scaling, and cut unit economics — but they demand time and engineering discipline. The author warns against "snake-oil" promises of turnkey AI; involve technical teams and prioritize rigorous integration work unless your goal is a trivial automation like piping emails to an LLM.