IA-02
Applied AI and automation
Automated workflow in production
Who it's for: a scoped use case, ready to go into production once validated.
The problem: you have identified a process to automate (document extraction, classification, notification, data enrichment). But between theory and execution lie the real questions: will it work on our data? How do we integrate it with our systems? What are the risks? What does it actually cost?
What I do
- Design — workflow architecture (triggers, steps, validations), integration with existing systems (APIs, connectors, databases)
- Implementation — building the workflow on n8n or Copilot Studio, a RAG pipeline where needed, AI models (Azure OpenAI, self-hosted LLMs), error handling and guardrails (validation, retry, alerting)
- Validation — testing on real data, measurement of the gain observed (time saved, errors reduced, volume processed)
Deliverables
- operational workflow: ready to process production data
- technical documentation: flows, parameters, integration points
- running cost: per month (APIs, infrastructure, support)
What changes
you are not deploying a proof of concept with nothing to show — you are deploying a workflow that has proven itself on your real data. The IT team knows how to maintain it. The business knows the return.
Technologies
- n8n
- Copilot Studio
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