Which repetitive task to start with
A good starting point is a recurring action with clear inputs and an expected output. Examples include preparing a report from several sources, checking data completeness or transferring statuses. We clarify rules and exceptions first so the team can verify results.
- Data collection, normalization and validation.
- Report updates and exception notifications.
- API and webhook exchanges.
- Document processing and internal knowledge search.
When AI fits
AI can help extract information from documents, classify text or search a knowledge base. If reliable rules can describe the task, we start there. For AI features, we assess quality against agreed examples and define which outputs require human approval.
Data access and failure handling
We identify allowed sources, access rights and what may be sent to external services. A knowledge assistant needs source references and a clear response when an answer is unavailable. Automated workflows need execution logs, reprocessing and failure notifications.
Tools and deliverables
We use Python, n8n, SQL, REST APIs and webhooks to fit the task. LLM APIs and retrieval-augmented generation can support document search. Maintenance, data volume and access constraints inform the choice of tools.
- Inputs, rules and exception definitions.
- A working automation workflow or AI integration.
- Validation against agreed scenarios and data.
- Instructions for running, checking results and handling failures.
Can this be a small project?
Yes. Automating one report or a recurring spreadsheet operation can be a standalone project. To estimate the scope, we need the frequency, data sources and an example of the expected output. The initial description does not need confidential files.