Case study — SaaS / Analytics
MetricFlow: the build that paid for itself.
A 14-person SaaS team drowning in manual ops. We automated lead enrichment, support triage and reporting — and gave them their week back.
Fig. 01 — AI AutomationAustin, USA
The challenge
MetricFlow's ops ran on heroics: SDRs hand-researching every inbound lead, a founder copying metrics into investor updates, support triage done by whoever was least busy. Growth was limited not by demand but by the humans-as-middleware problem.
Previous attempts at automation had failed quietly — a graveyard of half-configured Zaps nobody trusted or maintained.
The approach
The automation audit surfaced 23 candidate workflows; we ranked them by hours-saved-per-dollar and shipped the top three in month one.
Lead ops became one pipeline: inbound enriched via firmographic APIs, scored by an AI model against closed-won history, routed with a drafted research brief attached. SDRs stopped researching and started calling.
Support triage moved to an AI classifier trained on 18 months of tickets — drafting replies from the knowledge base, escalating anything below a confidence threshold. Humans approve; the machine drafts. Reporting became a scheduled AI narrative pulled from real warehouse data.
- Lead enrichment + AI scoring + routed briefs
- Support triage with human-in-the-loop drafting (84% accuracy, 100% reviewable)
- Automated investor & board reporting narratives
- Full monitoring, alerting and prompt versioning
The results
We replaced a planned ops hire with systems that never sleep. The board update writes its first draft — I just edit the story.Dana Reyes — COO, MetricFlow
The gallery
Start a project
Your metric. Our move.
Fixed quotes in 48 hours. Senior team only. Zero hand-offs between disciplines.