Copel drives $3.3 million in projected revenue and AI-powered customer solutions with Fivetran
Copel's Oracle billing databases generated enormous data volumes that traditional replication tools couldn't handle at scale, leaving finance teams relying on week-old snapshots and blocking product teams from launching AI-powered features or new revenue-generating offerings.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Oracle billing data generated
Oracle billing databases produce enormous amounts of data each week as the source for all downstream analytics and AI workloads.
Tools used
FivetranOracleGoogle BigQueryPower BIGoogle Cloud
Outcome
Copel unlocked $3.3 million USD in projected annual revenue from smart-meter subscription services, reduced receivables data visibility from days to minutes, and enabled AI-driven predictive models and RAG applications for personalized payment plans and smarter grid operations.
What failed first
Traditional data replication tools failed to keep pace with Copel's Oracle data volumes, requiring engineering-intensive workarounds, struggling to maintain transactional consistency, and lacking automatic failure recovery.