A mid-market SaaS company was losing 15+ hours per week to manual approval chains. Kovil AI built an n8n-powered automation layer that handles 80% of workflows automatically — saving $120K annually.
80%
Workflows Automated
Fully hands-off
15h
Saved Per Week
Per operations team
$120K
Annual Savings
Fully documented ROI
6 wks
Time to Value
From kickoff to go-live
Tech Stack
"What used to take our ops team half a day now happens automatically before anyone even checks their email. The ROI was obvious within the first month — and the Kovil AI team was meticulous about documenting everything so we can maintain it ourselves."
the client sells B2B data infrastructure to mid-market companies. Like many companies at their stage, they'd built internal processes through a combination of Slack messages, email chains, and spreadsheets. It worked — until it didn't.
As headcount grew past 200, their approval workflows became a serious bottleneck. Contract approvals, vendor onboarding, expense sign-offs, and new user provisioning all required manual routing through 2–4 different people. Nothing was automated. Everything was one Slack message away from falling through the cracks.
Before engaging Kovil AI, the client's operations team had tried to automate using Zapier. They got 30% of the way there before hitting walls: Zapier's logic capabilities weren't sufficient for multi-step conditional approvals, and their IT team didn't have the bandwidth to maintain a growing tangle of Zaps.
Specific pain points:
We started with a two-day process mapping exercise with the client's ops lead and IT manager. Rather than automating everything at once, we prioritized by: (1) frequency of occurrence, (2) time cost per instance, and (3) implementation complexity. This gave us a clear sequence.
We chose n8n as the automation backbone — self-hosted on their existing AWS infrastructure — for its ability to handle complex conditional logic, native integrations with their existing stack (HubSpot, Slack, PostgreSQL), and long-term maintainability without per-task pricing.
For approvals requiring judgment calls (e.g., contract risk assessment), we integrated GPT-4 to pre-classify requests and surface relevant context to approvers — reducing decision time even for the 20% of workflows that still needed a human touch.
Over 6 weeks, we built and deployed automations covering:
Every workflow was documented with a visual process map, a runbook for edge cases, and monitoring alerts in case a workflow fails silently. We also ran a 2-week parallel test (automation running alongside the old manual process) before full cutover.
Within the first 30 days after cutover, the client's ops team reclaimed 15+ hours per week. Contract cycle time dropped from an average of 5.2 days to 1.4 days. Vendor onboarding went from 2 weeks to 3 business days.
The fully-loaded annual savings — accounting for ops team time, reduced error rates, and faster contract close — came to $120K. The entire engagement paid for itself in under 90 days.
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