Finance ops · Production

Copel drives $3.3 million in projected revenue and AI-powered customer solutions with Fivetran

The problem

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.

First attempt

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.

Workflow diagram · grounded in source

Oracle billing data generated

trigger

“Oracle billing databases produced enormous amounts of data each week — an immense asset but also a technical challenge”

Fivetran CDC replication to BigQuery

integration

“Fivetran's log-based change data capture (CDC) replicates more than 400 GB per hour of redo logs, updating BigQuery every 5 minutes with minimal impact on source systems”

Vertex AI payment prediction

ai_action

“AI-ready data feeds Vertex AI models that predict payment behavior and recommend tailored payment plans”

RAG applications for grid and customers

ai_action

“retrieval-augmented generation (RAG) applications for personalized payment plans and smarter grid operations”

Real-time Power BI dashboards

output

“Leadership has a real-time view of company performance through Power BI dashboards”

New subscription revenue offerings

output

“Product teams are designing new subscription offerings projected to generate $3.3 million USD (approximately R$18 million) in annual revenue starting in 2025”

Reported 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.

Reported metrics

Projected annual revenue from smart-meter subscriptions$3.3 million USD

Receivables data visibilityfrom days to minutes

Initial data pipeline setup timeunder 4 weeks

Redo log replication throughputmore than 400 GB per hour

Show all 5 reported metrics

projected annual revenue from smart-meter subscriptions$3.3 million USD

receivables data visibilityfrom days to minutes

initial data pipeline setup timeunder 4 weeks

redo log replication throughputmore than 400 GB per hour

BigQuery update latencyevery 5 minutes

Reported stack

FivetranOracleGoogle BigQueryPower BIGoogle Cloud

Source

https://www.fivetran.com/case-studies/copel-drives-3-3m-in-projected-revenue-and-ai-powered-customer-solutions

Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

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 application…

What tools did this team use?

Fivetran, Oracle, Google BigQuery, Power BI, Google Cloud.

What results were reported?

Projected annual revenue from smart-meter subscriptions: $3.3 million USD; Receivables data visibility: from days to minutes; Initial data pipeline setup time: under 4 weeks; Redo log replication throughput: more than 400 GB per hour (source-reported, not independently verified).

What failed first in this deployment?

How is this finance ops AI workflow structured?

Oracle billing data generated → Fivetran CDC replication to BigQuery → Vertex AI payment prediction → RAG applications for grid and customers → Real-time Power BI dashboards → New subscription revenue offerings.