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
1
Oracle billing data generated
Trigger
Oracle billing databases produce enormous amounts of data each week as the source for all downstream analytics and AI workloads.
▾ source quote
“Oracle billing databases produced enormous amounts of data each week — an immense asset but also a technical challenge”
2
Fivetran CDC replication to BigQuery
Integration
Fivetran's log-based change data capture replicates more than 400 GB per hour of redo logs, updating BigQuery every 5 minutes with minimal impact on source systems.
▾ source quote
“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”
3
Vertex AI payment prediction
Ai action
AI-ready data feeds Vertex AI models that predict payment behavior and recommend tailored payment plans.
▾ source quote
“AI-ready data feeds Vertex AI models that predict payment behavior and recommend tailored payment plans”
4
RAG applications for grid and customers
Ai action
Retrieval-augmented generation (RAG) applications support personalized payment plans and smarter grid operations.
▾ source quote
“retrieval-augmented generation (RAG) applications for personalized payment plans and smarter grid operations”
5
Real-time Power BI dashboards
Output
Leadership has a real-time view of company performance through Power BI dashboards.
▾ source quote
“Leadership has a real-time view of company performance through Power BI dashboards”
6
New subscription revenue offerings
Output
Product teams design new subscription offerings projected to generate $3.3 million USD in annual revenue starting in 2025.
▾ source quote
“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
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Source
https://www.fivetran.com/case-studies/copel-drives-3-3m-in-projected-revenue-and-ai-powered-customer-solutions
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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?

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 rec…

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.

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