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AI workflow automation

Production AI workflows for repeated operational steps, with structured inputs, review states, monitoring, and fallbacks.

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Engagement
Estimated after discovery
Timeline
3-6 weeks
Recent project: Rautenberg Pitch EngineRead the case study

When this is the right step

Your team is drowning in repetitive work

Your team spends hours every week on data entry, report formatting, and copying information between systems. Repetitive admin work usually means the system is missing.

You're sitting on data you can't use

Your knowledge base, documents, and internal data are scattered across tools. AI-powered retrieval makes that knowledge instantly accessible to your entire team.

You tried no-code automation and hit the wall

Zapier got you started, but now you need conditional logic, error handling, and integrations that don't exist as pre-built connectors. Custom automation picks up where no-code tools stop.

The Approach

The workflow that wastes the most hours per week gets a focused AI automation built around it. Every automation is tailored to your existing tools and processes, with guardrails that keep humans in the loop where it matters.

The Outcome

Measurable time savings from day one. Your team stops doing repetitive work and starts focusing on decisions that actually need a human. The automation runs reliably in production with monitoring and fallbacks built in.

Rautenberg Pitch Engine: A Claude Code Research Studio for a Documentary ProducerRautenberg Pitch Engine: A Claude Code Research Studio for a Documentary ProducerRautenberg Pitch EngineRead the case study

What you get

Build focus

  • Custom AI agents and assistants
  • Workflow automation
  • Knowledge retrieval systems (RAG)
  • Third-party API integrations
  • Data pipeline automation
  • Monitoring and alerting

Included

  • Automation workflows running on your real data and tools, configured end to end
  • A custom AI agent or assistant assigned to the task it owns
  • Integration documentation covering every system the automation touches
  • A monitoring dashboard that shows what ran, what failed, and what needs review
  • A maintenance playbook so the automation survives staff and tool changes

The service deck as a PDF

The compact PDF for this service: starting point, approach, scope, and next step on three pages.

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Frequently Asked Questions

What kind of AI features can you build?
Document processing pipelines, conversational interfaces, classification systems, content generation tools, and custom AI agents. The work is planned around the workflow, data sources, review steps, and controls your team needs in production.
How do you handle data privacy with AI?
The privacy model is chosen during planning. Some builds use API-based models with no training on your data and strict access controls; sensitive workflows can use private or on-premise deployment when required.
What's the typical ROI timeline for AI automation?
ROI depends on task volume, error cost, review steps, and run cost. It is estimated during planning, then validated against actual time saved once the first production workflow launches.
What's the first thing you typically automate for a client?
Whatever the team complains about most. Usually it's document processing, report generation, or pulling data from one system into another. The first build targets the task that wastes the most hours per week, then expands from there. One working automation builds more internal buy-in than any strategy deck.