Operate & Evolve
AI systems degrade when they stop learning from production reality. Rozeta continuously monitors performance, retrains against live operational data, and evolves workflows as the business changes. The goal isn’t a dependency on Rozeta. It’s an AI system your team can operate, improve, and scale internally over time.
FEATURE 01
We track the metric, not the dashboard.
Vendor dashboards measure LLM API uptime and token usage. Those numbers don't tell you whether the agent is doing its job. We track the operational outcome the agent was scoped against, throughput, payback, exception rate, whatever was the success metric on day one. If the number drifts, we catch it before you do. Output: a monthly performance report against the metric that actually matters to your business.
FEATURE 02
Continuous improvement on production data.
The agent sees real edge cases in production that no design phase can predict. We catch them, retrain or refine, version the change, and ship the update without service interruption. Every change is audited. Every update has a rollback path. The agent gets sharper every quarter on the data your business is actually generating. Output: a quarterly optimization log with every change, the data behind it, and the impact.
FEATURE 03
We work ourselves out of the day-to-day.
The named production owner on your team starts running the agent in week one. By month six, they've fully owned the operating cadence. We stay involved on strategic optimization and the next workflow, but the day-to-day stops being our job. The goal isn't a vendor relationship that compounds forever. It's a team that doesn't need us. Output: documented operational fluency on your side, with Rozeta moving from operator to advisor.
BY THE NUMBERS
Numbers that speak for themselves
12 mo
Standard engagement length
99.5%
Agent uptime SLA
4
Optimization cycles per quarter
6 mo
Time to team self-sufficiency
Find the medtech workflows AI should rebuild first
Rozeta runs a 4-week operational audit across your commercial, regulatory, quality, and field workflows — then ships production AI systems into the ones where coordination overhead, submission cycles, and manual handoffs are actually slowing the business down.
