Case studies

Lead generation automation platform

Automated data pipeline for outbound scaling.

Business value: More qualified leads at lower operational cost and with higher data quality.

Outcome: Repeatable lead acquisition and handling process, ready for campaign volume scaling.

Tech stack: Python, Django, PostgreSQL, automation workflows, integrations, scheduling, monitoring

DeduplicationData Quality
100% AutoPipeline
ActiveMonitoring
PythonDjangoPostgreSQLautomation workflowsintegrationsschedulingmonitoring

Business problem

Lead data came from multiple sources, had inconsistent quality, and required manual handling before campaigns could start.

Approach and solution

I designed an automation platform connecting data collection, normalization, validation, and campaign launch through explicit business rules.

Delivery scope

  • Multi-source scraping with quality controls and deduplication.
  • Central PostgreSQL data model with normalization rules.
  • Campaign scheduler with step-level operational logging.
  • Monitoring and recovery procedures for critical workflow failures.

Business impact

  • Significantly shorter time from data acquisition to campaign launch.
  • Higher lead quality and less manual data cleanup work.
  • Scalable process without linear operational cost growth.
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