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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.

AI Automation AI Automation 2025

Fig. 01 — AI Automation Austin, USA

Client
  MetricFlow
Year
  2025
Industry
  SaaS / Analytics
Location
  Austin, USA

01

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.

02

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

03

The results

R—01 32h Weekly ops hours reclaimed
R—02 9 min Lead response time (from ~26h)
R—03 41% Support tickets self-resolved
R—04 $0 Additional headcount needed at 2× volume

> 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

04

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