AI Automation — replace the workflows that shouldn't need a human.
Production-ready AI automation for your most repetitive business workflows. Not a no-code Zapier hack — a proper system with error handling, monitoring, and documentation.
What you get
Scoped automation system with defined inputs, outputs, and business logic
Production deployment with error handling, retry logic, and failure alerting
Integration with your existing tools (CRM, analytics, email, Slack, etc.)
Monitoring setup — you know when it's working and when it's not
Written documentation: what it does, how it works, how to modify it
30 days post-delivery support for bug fixes and minor adjustments
How it works
Workflow scoping
Map the current manual workflow end-to-end: inputs, decision points, outputs, exceptions. Define the automation boundary — what gets automated, what stays human.
Architecture + approval
Propose the automation architecture: which AI models, which integrations, how exceptions are handled, what monitoring looks like. Written and approved before build starts.
Build
Implementation against the approved spec. Weekly progress updates. You see work in progress through a shared staging environment, not just the final deliverable.
Testing + QA
Testing against real data and edge cases. Failure modes documented. Exception handling verified. Performance benchmarked against the manual baseline.
Deploy + handover
Production deployment. Walkthrough session. Full documentation delivered. 30-day support window begins.
Tech stack
FAQs
What kinds of workflows can you automate?
Lead scoring and routing, content generation and review workflows, data extraction and transformation, report generation, customer communication sequences, internal approval workflows, and any structured process with clear inputs and outputs. If a human follows a decision tree to do it, AI can likely do it better.
What's the minimum viable scope for a €5,000 automation?
A single well-defined workflow end-to-end. For example: automated lead qualification from form submission to CRM with personalized outreach, or an automated weekly report pipeline from data sources to formatted output. The scope is fixed at kickoff.
How do you handle errors and failures?
Every automation I build has explicit error handling: what happens when an API is down, when input data is malformed, when the AI returns unexpected output. Failures are logged and alerted — you're never flying blind.
Who maintains the automation after delivery?
You do — with the documentation I provide. The 30-day support window covers bug fixes and unexpected behavior. Beyond that, maintenance is yours. I build for maintainability, not dependency.