Against the KPIs we define together before the project starts — typically time saved, error rate reduction, cost per task, or revenue impact. We track these metrics from day one of deployment.

Common KPIs include: hours of manual work eliminated per month, ticket deflection rate, lead response time, document processing accuracy, cost per automated task, and ROI payback period.

We can’t guarantee specific numbers — no honest agency can. But we define target outcomes upfront, build to achieve them, and don’t consider a project complete until the system is performing to spec.

We debug and iterate at no extra cost within the 30-day post-launch support window. AI systems sometimes need tuning once they encounter real-world data — this is expected and included.

Every system we build includes error handling, logging, and alerting. Edge cases are either handled by the AI with fallback logic, or escalated to a human with full context. Nothing silently fails.

2–3 weeks for focused automations. 4–8 weeks for complex multi-component systems. We’re transparent about timelines upfront and stick to them.

Across deployments: 62% reduction in SDR workload (lead qualification agent), 71% ticket deflection (support automation), 85% of invoices processed automatically, 120+ hours/month eliminated (ops automation). Full details available on request.

We tell you what AI can’t do just as clearly as what it can. If a process isn’t a good fit for automation, we’ll tell you in the discovery call — even if that means a smaller project or no project at all.