AI-Driven Demand Forecasting Saves $1M for REPs

A Texas retail electric provider needed better energy forecasts to offer competitive prices. We built a machine learning model using historical load data.
Customer Story

Consero Global

Finance-as-a-Service

Consero logo
Industry
Finance-as-a-Service
Situation
Growing fragmented tech stack was affecting productivity and revenue as the business scaled.
Offering
PrimarythinkOne SecondarythinkAI
Key Result
40%
Revenue increase with no corresponding growth in overhead.
Client logo
Project details
Industry
situation
Offering
thinkOne
thinkAI
key result
2x

The problem

Why thinkbridge

Implementation & solution

The Result:

The problem

Every new client added another point tool to a stack that could not talk to itself: one system for reconciliation, another for reporting, another for client communication. Account managers absorbed the gap by hand, re-entering data, chasing exceptions, and stitching together updates one client at a time.

The labor hiding between those systems set a hard ceiling on how many accounts one person could run. Every added client added headcount and overhead before it added margin, and the firm needed a way to grow revenue without growing cost at the same rate.

Why thinkbridge

The realistic alternative was a sixth point tool bolted onto a stack that five tools had already failed to fix. The firm required one platform built around how it delivers the service: intake, reconciliation, exception review, and client reporting as a single flow.

That kind of system around a client's own operations is the thinkOne solution.

Implementation & solution

Production stage, thinkOne primary, thinkAI ancillary. thinkbridge and the firm's own team designed and built one delivery platform in place of the old tool stack. The platform automated the repetitive parts of the job: invoice processing, reconciliations, and routine reporting.

AI-driven exception review then routed staff attention to the anomalies that required a judgment call. Real-time dashboards gave the firm's internal team and its own clients a shared, current view of the numbers, replacing the periodic manual updates the old model ran on.

Metrics

The new platform closed the gap between growth and cost that the old stack had opened.

2x

Increase In Manager Span Of Control

75%

Faster Time To Monthly FInancials

100%

Improvement In Accuracy
wave

Results & metrics

The new platform closed the gap between growth and cost that the old stack had opened.

2x

Increase In Manager Span Of Control

75%

Faster Time To Monthly FInancials

100%

Improvement In Accuracy

Related Customer Stories

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CASE STUDY

AI-Driven Demand Forecasting Saves $1M for REPs

A Texas retail electric provider needed better energy forecasts to offer competitive prices. We built a machine learning model using historical load data.
AI-Driven Demand Forecasting Saves $1M for REPs
2x
Increase In Manager Span Of Control
75%
Faster Time To Monthly FInancials
100%
Improvement In Accuracy

The Challenge

Texas’ energy market is deregulated, meaning in most parts of the state consumers have the ability to choose their retail electric provider (REP) on an open market:

  • To set competitive market prices, REPs must estimate the amount of energy they need to provide their customers for any given hour on any given day.
  • Energy is traded in hourly segments on the Nymex, and REPs have traditionally relied on decades-old algorithms to determine the correct amount of power to buy.
  • These algorithms generally yield conservative estimates, leading REPs to purchase more energy per hour than required.
  • This over-purchasing results in tens of thousands of dollars in wasted overhead to the REP annually.

The Solution

thinkbridge is developing a far more accurate load prediction model to help REP’s avoid the lost revenue associated with the traditional method of purchasing energy. Based on REST/HTTP-based EDI, we pull 15-year historical energy load data and analyze it using our Machine Learning service for Analytics and Prediction (BigML).

Key components of the solution included:

  • Forecasting models based on our Cloud Services and Big Data competencies are then produced for the REPs.
  • Initial test runs have found our model to be a much more accurate forecasting tool for purchasing the correct amount of energy on the Nymex.
  • Our model provides REPs with a much greater ROI compared to the software traditionally used to estimate energy needs.
  • Additionally, the depth and accuracy of our data allows us to more precisely forecast metrics like consumer load patterns and revenue generated per customer.

Read more about how we work using our accelerators, context expertise, and global delivery.

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Result

  • Reduction in Overhead Costs: Achieved a 35% reduction in annual overhead costs for REPs by significantly lowering the excess energy purchased, resulting in substantial savings on surplus energy costs.
  • Improvement in Load Forecasting Accuracy: Increased forecasting accuracy by 40%, allowing REPs to make more precise energy purchases and reduce the frequency of surplus energy bought on the open market.
  • Increase in Revenue Per Customer: Boosted revenue per customer by 20% due to optimized purchasing and reduced wastage, improving overall profitability for REPs and enhancing customer satisfaction through competitive pricing.

Conclusion

By replacing outdated forecasting methods with a data-driven, machine learning approach, thinkbridge helped Texas REPs transform a costly challenge into a strategic advantage. The new load prediction model not only cut overhead and improved accuracy but also unlocked measurable gains in revenue and customer satisfaction. With smarter forecasting in place, REPs are now better positioned to thrive in a competitive, deregulated market—achieving stronger margins while delivering greater value to their customers.

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CASE STUDY

AI-Driven Demand Forecasting Saves $1M for REPs

A Texas retail electric provider needed better energy forecasts to offer competitive prices. We built a machine learning model using historical load data.

AI-Driven Demand Forecasting Saves $1M for REPs
2x
Increase In Manager Span Of Control
75%
Faster Time To Monthly FInancials
100%
Improvement In Accuracy