Past the limits of Zapier, ready for real integration
AI Automation at $3,000 a month. Senior engineer who can actually integrate with internal APIs. Monthly, cancel anytime. No agency markup.
Who this is for
Growth-stage (Series B+) COO or VP Operations.
The pain today
- Ops team of 20+ has overlapping manual workflows.
- Piecemeal no-code tools break at current scale.
- The internal engineering team cannot spare cycles for AI ops work.
- Every AI vendor pitch promises the moon; integrations never match the demo.
The outcome you get
- AI workflows that integrate with internal APIs, not screen scraping.
- Retirement of 2-4 overlapping no-code tools.
- Measurable ops cost per customer reduction across quarters.
- Monthly retainer with cancel-anytime terms.
Where no-code tools break at Series B
Zapier and its siblings are excellent up to about 50 tasks a day. Past that, three things start to break: rate limits, error handling, and data consistency. Growth-stage ops teams usually have 5-10 no-code tools in production, each with its own pricing and its own failure mode. The aggregate cost is $3,000-$10,000 a month in tool subscriptions alone, plus the ops time spent fixing breaks. A senior engineer on retainer at $3,000 a month replaces the brittle layer with real API integrations that scale and audit cleanly.
The senior engineer plus LLM pattern
Growth-stage AI automation needs a senior engineer who can code against internal APIs, not a consultant who drags and drops. The pattern I run: read the internal API docs in week one, write a small Node or Python wrapper that standardizes auth and data shapes, then wire Claude or OpenAI on top of the wrapper. Every subsequent automation builds on the wrapper, so new workflows ship in days instead of weeks. The wrapper becomes a reusable internal asset the team owns under Work Made for Hire.
Sequencing automation projects
Growth-stage ops teams have 20-30 potential automation targets. Doing them in the wrong order wastes the retainer. I sequence by ROI: automation that saves the most hours per week goes first, automation that unlocks a downstream automation goes second, automation with high error cost goes last (so the pattern is proven before it touches sensitive data). A typical 12-month engagement ships 10-15 automations in this order. Imohub's infrastructure story followed a similar prioritization playbook, landing at 70 percent infra cost reduction.
Metrics and executive reporting
Every growth-stage engagement includes an executive dashboard and a monthly report. The dashboard tracks automations live, tasks processed, hours saved, error rate, cost per workflow. The monthly report frames these as ops cost per customer and board-level ROI. Growth-stage CFOs love this because it makes the retainer a defensible line item, not a soft investment. The dashboard transfers to the team when the retainer ends.
Recent proof
A comparable engagement, delivered and documented.
Rescued a slow API that was blocking user growth
Refactored the backend architecture, making the system far more responsive and scalable for the growing user base.
Frequently asked questions
The questions prospects ask before they book.
- Can you work alongside our internal AI team?
- Yes. I take on the projects they do not have bandwidth for and integrate with their standards. No turf wars.
- What about compliance and audit?
- I work inside your SOC 2, HIPAA, or PCI posture. Explicit data boundaries, audit logs, BAA-compliant providers when needed.
- Do you use our existing LLM accounts?
- Yes. Your OpenAI, Azure OpenAI, or Anthropic accounts. API keys on your side. I never resell AI.
- Can you replace multiple no-code tools at once?
- I replace them in sequence, not all at once. Each replacement ships, proves itself for 30 days, then the next.
- What's the typical engagement length at growth stage?
- 6-18 months. Most growth-stage clients run the retainer for a year to ship 10-15 automations, then scale down to maintenance or off.
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