Real Outcomes

What actual automation looks like

Three anonymized client engagements — the problem, what we built, and the numbers that came out the other side.

Automation Build
40-person managed services firm · $8M ARR

IT Support Auto-Triage

L1 IT support was drowning in repeat tickets — 60% of resolution time spent on password resets and access requests that required no human judgment.

What we shipped
  • Zendesk + Okta + internal KB → LLM triage layer with confidence threshold routing
  • Auto-resolve path for password resets, MFA re-enrollment, and provisioning
  • Escalation queue with context-enriched handoff to L2 for anything below threshold
  • Slack digest for the support lead — daily auto-resolve summary + flagged anomalies
Outcomes
47% of L1 tickets auto-resolved, no human touch
$180K annual support cost avoided (yr 1)
+12 pts NPS increase from faster resolution
Delivered in 5 weeks — Automation Build
Automation Build
$12M ARR B2B SaaS · 60-person infra team

Monitoring Alert Noise Reduction

The on-call rotation was burning out — 80+ PagerDuty alerts per week, but fewer than 15% required any action. Engineers were ignoring the noise, and real incidents were getting buried.

What we shipped
  • PagerDuty + Datadog + GitHub → LLM alert classifier trained on 6 months of incident history
  • Severity-scoring layer with auto-suppress for known-safe transient patterns
  • Enriched alert cards in Slack with suggested runbook step and recent deployment context
  • Weekly noise audit report emailed to infra lead with suppression accuracy metrics
Outcomes
71% reduction in actionable on-call pages
4.2 hrs mean time to detect critical incidents (down from 22 hrs)
$95K eng time reclaimed annually
Delivered in 7 weeks — Automation Build
Fractional AI Leadership
120-person professional services firm · $22M ARR

Internal AI Playbook + First 3 Automations

Leadership wanted to "use AI" but had no coherent strategy — individual contributors were running ad-hoc ChatGPT experiments with zero governance, inconsistent quality, and no measurement.

What we shipped
  • Company-wide AI usage policy, approved tool list, and prompt governance framework
  • Automation #1: Proposal drafting pipeline — CRM data + past wins → GPT-4o first draft in <3 min
  • Automation #2: Weekly client status report generation from Jira + time-tracking exports
  • Automation #3: New hire onboarding knowledge base with AI Q&A layer over internal docs
  • Monthly AI ROI dashboard presented to C-suite, ongoing roadmap ownership
Outcomes
11 hrs saved per proposal (down from 14 hrs to 3 hrs avg)
$240K annualized labor savings across 3 automations
3 months to full deployment, still engaged at month 8
Ongoing since month 1 — Fractional AI Leadership

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