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AI Workflow Automation

Most "AI automation" is wiring n8n templates. That is not what I do. I productize complete AI workflows where deterministic input goes in, LLM reasoning happens in the middle, and a structured artifact (PDF, Excel, JSON, dashboard) comes out. Same pattern I would build for your internal ops, sales workflow, or content production.

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Who This Is For

  • B2B teams drowning in manual research, data entry, or content production
  • Sales and marketing leaders who tried Zapier and hit its limits
  • Operations teams with repetitive workflows that need engineering depth, not low-code wiring
  • Founders who want their ops to run without hiring 5 people

What You Get

  • >Workflow architecture (deterministic inputs, LLM reasoning, structured outputs)
  • >n8n or Make orchestration where appropriate
  • >Custom Python pipelines where n8n hits limits
  • >Eval harness for LLM-dependent steps
  • >PDF, Excel, or dashboard artifact generation
  • >Cost monitoring + token budgeting
  • >Documentation + team handover

Engagement Tiers

Each tier scoped on a discovery call. Most clients start with a pilot to test the fit, then expand from there.

Workflow Pilot·1 to 2 weeks

One simple productized workflow end-to-end. Test the productization pattern on a real internal need before scaling.

Single Workflow Build·2 to 4 weeks

One productized workflow end to end. Input definition, LLM steps, output artifact, deployment.

Workflow Suite·60 days

5 to 10 connected workflows with shared infrastructure. Fits internal-ops or content-production engines.

Retainer

Ongoing workflow operations, new workflow builds, optimization, monitoring.

Process

01.

Workflow audit

Identify the 3 highest-leverage workflows from your team's manual processes.

02.

Productize

Design each workflow with clear input, LLM reasoning step, and structured output artifact.

03.

Build + eval

Ship each workflow with its own eval harness so output quality is measurable.

04.

Operate

Optional monthly retainer for monitoring, optimization, and new workflow builds.

Proof

Real builds. Named clients. Architecture detail.

FAQ

Zapier and Make are great for simple wiring (CRM updates, notification routing, sheet syncs). They struggle once you need real LLM reasoning, retries with eval gates, custom data transformations, or PDF/Excel artifact generation. I build the parts that need engineering depth, and use n8n or Make for the wiring where they fit.

Want this for your team?

Book 30 min. We will talk through your specific situation and I will tell you whether this is the right fit or not.