Responsible data collection & crawling
For teams that want to automate a real workflow without losing human validation.
Limited collection, clear purpose, data minimization and respect for source constraints.
Services
Collect and structure business data responsibly
Collect, structure and enrich publicly accessible data with a controlled and responsible approach.
Problems solved
- recurring manual time
- scattered data
- poorly documented exceptions
- limited impact tracking
Use cases
- request qualification and routing
- tool synchronization
- operational reporting
- supervised follow-ups and alerts
Method
- map the process
- define rules and exceptions
- prototype a limited workflow
- test with users
- document and roll out progressively
Deliverables
- functional specification
- connected prototype
- team documentation
- guardrails and logs
- tracking dashboard
Guardrails
- human validation for sensitive decisions
- limited access to useful data only
- documented manual fallback
- no automatic result promise
Related articles
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A practical guide to connect AI to existing tools: use cases, method, risks to avoid and criteria for launching useful AI automation.
Frequently asked questions
Do we need to replace existing tools?
No. We prefer connecting tools already used by the team when it is reliable.
How do we avoid opaque automation?
Rules, exceptions and validation points are documented before deployment.