Buy the team back 30 percent of their week
AI Automation at $3,000 a month. One senior engineer, Claude and OpenAI wired into the tools you already use. One client cut 40 hours a month of manual document work.
Who this is for
Seed-stage ops lead buried in manual document processing, onboarding, or support triage. The team of 5-8 is spending 30 percent of its time on repeatable workflow.
The pain today
- The team is too small to absorb the onboarding or support load manually.
- Every new customer triples the document workload.
- Zapier flows broke three times this quarter and nobody has time to fix them.
- The board asked about 'AI strategy' and the answer is fuzzy.
The outcome you get
- Three automations live inside 30 days, each with measurable time-saved metrics.
- Integrations with HubSpot, Linear, Slack, Notion, and your support tool.
- Vendor-neutral stack (I do not resell AI products).
- A clear answer for the board on AI spend and ROI.
The three automations seed startups run first
The first three automations for a seed-stage team are almost always the same. Customer onboarding: a new customer triggers a Claude-generated personalized walkthrough, account setup, and internal Slack note to the CSM. Support triage: incoming tickets are classified, routed, and pre-drafted by Claude. Sales follow-up: inbound leads are enriched, scored, and get a first-touch email drafted from the sales-voice I train on your existing emails. These three free up 20-40 hours a week for a 5-8 person team, which at seed-stage salary rates is $8,000-$15,000 a month in recovered capacity.
ROI math for a seed-stage team
The retainer is $3,000 a month. The recovered capacity is typically $8,000-$15,000 a month. Net gain is $5,000-$12,000 a month. I track this for every automation I ship because the retainer has to pay for itself every month or you cancel. Most seed-stage clients see full payback in month one. The compound effect over a year is 150-300 percent ROI before the team grows into the freed capacity.
Data and privacy for regulated verticals
Fintech, healthtech, and legal tech seed-stage startups worry about AI data handling. Rightly. I configure every automation with explicit data boundaries: what leaves the walled garden, what stays in, what gets redacted before it reaches the model. For highly regulated data I use self-hosted models (Llama, Mistral) or Azure OpenAI with a BAA. This is senior engineering work, not a no-code drag-drop. That is why a retainer with one senior engineer beats a generic automation vendor.
When to build vs buy
Seed startups get pitched by 10 AI vendors a week. Most pitches fail the integration test: the tool does not plug into the CRM the way the workflow needs. I read every vendor pitch the founder is considering and give a build-vs-buy recommendation. Sometimes the vendor wins (if the integration is clean and the price is right). More often the right answer is: buy a platform (OpenAI or Claude API, plus a vector DB) and let me build the wrapper in a week. That wrapper is yours forever under Work Made for Hire.
Recent proof
A comparable engagement, delivered and documented.
A prompt library that works with every AI tool
A home for your best AI prompts. Save them once, then use them in Claude, Cursor, or any AI tool you work with. No more copy-paste.
Frequently asked questions
The questions prospects ask before they book.
- How does this compare to hiring an AI engineer?
- An AI engineer costs $250K+ all-in and takes 90 days to hire. The retainer is $3,000 a month, starts in 1-2 weeks, no equity. Works as a bridge or a permanent ongoing capability.
- Can the team run the automations after you ship?
- Yes. Every automation comes with documentation and a simple dashboard. The team can monitor and trigger. The retainer covers maintenance and new builds.
- Do you use our existing OpenAI account or yours?
- Yours. API keys and billing stay on your account. I do not resell AI or take vendor commission.
- What if the model pricing changes?
- I monitor model pricing and capability monthly. If a workflow should move from Claude to GPT or vice versa, I re-wire at no extra charge.
- How fast until the first automation is live?
- One to two weeks. Week one: workflow mapping and integration setup. Week two: build and deploy. Most clients see the first time-saved metric inside 30 days.
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