Managed Automations

Every automation we run is observable end-to-end — input volumes, output quality, model and tool errors, latency, and cost. We watch for drift: when accuracy drops, when prompts start producing edge cases, when an upstream API changes, when a model vendor updates and behavior shifts. Issues are caught before users notice, not after.

Prompts, tool definitions, retrieval indexes, and evaluation rubrics are versioned and tuned monthly. As foundation models evolve, we test newer versions in a sandbox, validate against your rubric, and graduate them only when they beat the incumbent on your data — not just in someone else's benchmark.

Every retainer includes capacity for new automation development. Each month we prioritize together — based on roadmap, observed bottlenecks, and feedback from your team — and ship. Most clients add 1–3 small automations per quarter on top of the existing portfolio, without renegotiating scope.

We keep your AI use policy, data classification rules, and audit trails up to date as the surface area grows. When auditors, clients, or insurers ask "what AI is in use, on what data, with what oversight?" you have a current, documented answer.

Every quarter we sit down with leadership and walk through what shipped, what improved, what is still on the roadmap, and what the data says about ROI. The review is also where we re-scope the retainer up or down — we would rather right-size with you than pretend nothing has changed.