AI Development for Modern Products

The single biggest difference between an AI feature that compounds value and one that quietly degrades is an eval harness. Without it, you ship a prompt that worked on five examples and discover six months later that quality regressed silently when a model provider shipped new weights. Every AI engagement we run starts with a golden dataset, automated scoring, and a CI gate — before the second prompt ships. See our deeper breakdown in the AI integration playbook.

By month six of production AI, your token bill is a real line item. We route by complexity (cheap models for 70–80% of traffic), cache aggressive, trim context windows, and use provider batch APIs for async workloads. The combined effect is typically 40–60% cheaper than the naive single-model implementation, with equal or better accuracy.

Use a vendor for the parts that are not your differentiation: observability, prompt registries, eval orchestration. Build the parts that are: your retrieval pipeline, your workflows, your human-in-the-loop UI. Most teams invert this and pay for it for years.