AWS Partner Network (APN) Blog
From weeks to hours: How a UK energy retailer transformed residential pricebooks with Gorilla on AWS
By: Jack Rivers, Content Marketing Manager – Gorilla
By: John Gray, Sr. SAP Specialist Partner Solutions Architect – AWS
By: Sascha Janssen, Principal Solutions Architect – AWS
By: Simon Cunningham, Principal ERP Specialist Solutions Architect – AWS
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| Gorilla |
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In this post, you will learn how a major UK energy retailer modernized its residential pricebook process using the Gorilla Energy Margin Intelligence application, built on Amazon Web Services (AWS). Facing intense market volatility, the retailer transformed their error-prone, manual process into an automated, auditable workflow that completes in under 4 hours. We detail the industry challenge, the technical solution using Amazon Simple Storage Service (Amazon S3), AWS Glue, and Amazon Elastic Compute Cloud (Amazon EC2), and the major results, including a 95% reduction in pricebook creation time and full Market-wide Half Hourly Settlement (MHHS) compliance (Pricing Manager, UK Energy Retailer).
To stay competitive, you need to move now, not days later
As energy markets have shifted from a state of relative calm to near constant volatility, the pricing team has faced a slew of new challenges. We need to remain competitive across numerous brokers and other third-party intermediaries (TPIs) while verifying that each new deal is going to make money. A slight delay and we lose to a competitor; a mistake and we potentially lose even more.
For years our processes have been manual. An analyst would export billing data from SAP, rebuild the pricing model in Excel, wait 2–5 days for calculations to be complete, then route everything through finance and commercial sign-off before anyone could touch the SAP billing system. By the time we had a number we trusted, the market had moved again.
We weren’t slow because our people weren’t good. We were slow because the tools weren’t built for the speed the market now demands. That’s why we partnered with Gorilla, an AWS Partner and AWS Marketplace Seller, to rebuild this workflow from the ground up on AWS.
The industry-wide problem: Adjusting at pace to a volatile market
Energy retailers across Europe and North America face the same structural challenge. Reacting quickly enough to a chaotic market requires new capabilities. Changes in one or more of the following areas can trigger a wave of pricebook updates:
- Wholesale market volatility – Spot and forward price movements that can shift margins overnight
- Network and distribution charge updates – Typically annual but increasingly frequent
- Government levies and tax changes – Requiring rapid pass-through recalculation across products
In the UK, pricing has been further complicated by the introduction of Market-wide Half Hourly Settlement (MHHS), which mandates that energy usage (domestic and non-domestic) be settled at half-hourly granularity. Aside from the operational challenge, this has introduced a vast amount of new data into every calculation. Similar changes to settlement times have occurred in other energy markets, with 5-minute settlement in Australia and 15-minute settlement across the EU.
The traditional approach to updating a pricebook—with spreadsheets, manual SAP exports, and siloed teams—creates three compounding risks:
- Speed risk – Slow quotation means losing out or taking a risk with errors
- Accuracy risk – Manual processes introduce errors across millions of contract lines
- Capacity risk – Peak demand for pricing analysis reaches exactly when market volatility is highest
For our customer, a major UK retailer, this meant they could update their pricebooks only once a month, with five levels of business sign-off to bring a new product to market and no ability to model scenarios before committing to a price.
What good looks like: Rapid, automated, auditable pricebooks
The target state isn’t only faster spreadsheets. It’s a fundamentally different operating model:
- A pricing manager receives an alert that wholesale costs have shifted
- Within minutes, an automated workflow recalculates tariffs across products and customer segments
- Pricebook creation has gone from taking days to completing within 30–45 minutes
- The approved book is written back to SAP with a detailed audit trail
- The entire cycle, from market signal to updated billing system, is designed to be complete in one to two hours
This is what Gorilla’s Energy Margin Intelligence application, built on AWS, is designed to deliver.
The solution: Gorilla on AWS
Gorilla and the energy retailer began to work on an implementation that can cover their needs without disrupting business operations.
Architecture overview
The following diagram illustrates the end-to-end pricing-update pipeline, showing how SAP S/4HANA pricing data is extracted, transformed, and delivered to the Gorilla Pricing Engine, with results captured back into an analytics layer for reporting.
Figure 1: Solution architecture
The application connects three layers that previously operated in silos:
- Data foundation – SAP IS-U (Industry Solution for Utilities) and S/4HANA remain the system of record for billing and contract data. SAP Energy Data Management (EDM) provides consumption data. AWS Glue extracts this data into Amazon S3, which serves as the central data lake. There is no rip-and-replace of existing SAP infrastructure.
- Automated orchestration – AWS Step Functions coordinates the end-to-end workflow. AWS Lambda handles event-driven triggers and lightweight processing. Amazon API Gateway manages external integrations. Amazon DynamoDB stores workflow state and intermediate results.
- Calculation and analytics – Gorilla’s Python-based pricing engine runs on Amazon ECS, processing tariff recalculations in parallel across the full portfolio. Amazon Athena and Amazon Redshift power portfolio-level analysis. Amazon Quick delivers scenario dashboards to pricing managers and commercial directors and allows business users to query portfolio data in plain English with no SQL required.
The result is a closed-loop system: SAP data flows into AWS, Gorilla calculates, and results flow back to SAP through the AWS Glue SAP OData connector. The process is automatic, can scale, and is auditable.
Why we built on AWS – A note from Gorilla’s CTO Joris Van Genechten
When we evaluated cloud providers for our calculation engine, three things drove our decision toward AWS:
- Elastic compute at portfolio scale – Energy retailers range from 50,000 to 50 million contracts. Amazon ECS enables us to scale our parallel processing engine to match portfolio size without pre-provisioning infrastructure. A retailer with 500,000 contracts and one with 5 million contracts run the same code base and the application platform scales to fit.
- Native SAP integration – Most energy retailers run SAP. The AWS Glue SAP OData connector meant we can build a production-grade data pipeline without custom middleware. This dramatically reduced implementation time and ongoing maintenance burden for our customers.
- The analytics layer – Our customers don’t just need fast calculations: they need to make decisions from the results. Amazon Quick gave us a business-ready analytics and natural language interface we can embed directly into our solution without building it ourselves.
A day in the life: Before and after
To illustrate the operational impact of this solution, the following tables walk through a typical pricing-update cycle, first as it exists today using manual processes, and then as it runs with Gorilla on AWS.
Before Gorilla on AWS
Figure 2: A typical pricing-update cycle using manual processes
Before Gorilla on AWS, the process took a total of 10–14 business days. The process was lengthy, error prone, and required high manual effort. There was no scenario modeling.
After Gorilla on AWS
Figure 3: A typical pricing-update cycle with Gorilla on AWS
After Gorilla on AWS, the process took less than 4 hours. The new process is scenario modeled, auditable, and requires minimal manual intervention.
Results: What changed for the UK retailer
After Gorilla on AWS, the UK energy retailer saw the following improvements:
- Pricebook creation time – Reduced from 2–5 business days to under an hour, a 95% reduction
- Full-time equivalent (FTE) hours per cycle – From 3 analyst-days to 2 hours of review time
- MHHS compliance – Previously impossible; now fully MHHS ready
- Error rate – 75% reduction in pricing errors reaching SAP billing
- Market responsiveness – Ability to respond to intraday wholesale movements for the first time
“It’s been a huge gain to take any existing process and transform it into something automated and modern” – Pricing Manager, UK energy retailer
How this Fits the SAP + AWS joint go-to-market
For energy retailers running SAP IS-U or S/4HANA, this solution is additive and doesn’t require major changes in SAP. Gorilla’s solution extends SAP’s data without replacing it. AWS Glue handles extraction and enriched pricing data writes back to SAP through standard interfaces. There is no core system migration required.
This makes Gorilla on AWS a natural fit for SAP customers looking to modernize their analytics and pricing capabilities without a multi-year transformation program. AWS and SAP’s existing partnership means joint customers can use existing commercial agreements, certified integration patterns, and joint support structures.
For AWS Partners and SAP resellers, this represents a concrete, deployable solution for a pain point that exists in every energy retail SAP environment.
Getting started
Gorilla’s Energy Margin Intelligence solution is available on AWS Marketplace, supporting procurement through existing AWS commercial agreements. The solution is compatible with SAP IS-U and S/4HANA environments and can be deployed incrementally; starting with a single market or product line before scaling to the full portfolio.
To learn more or request a proof-of-concept scoping session, visit the Gorilla AWS Marketplace listing or contact your AWS Energy & Utilities Industry Specialist.
Conclusion
In this post, we showed you how a major UK energy retailer transformed its residential pricebook process from a 10–14-day manual workflow into an automated, auditable cycle that completes in under 4 hours. Using the Gorilla Energy Margin Intelligence application built on AWS, the retailer used AWS Glue for SAP data extraction, Amazon ECS for parallel tariff recalculation at portfolio scale, and Amazon Quick for scenario analysis and decision support. The results speak for themselves: a 95% reduction in pricebook creation time, a 75% decrease in pricing errors, full MHHS compliance, and the ability to respond to intraday market movements for the first time. For energy retailers running SAP IS-U or S/4HANA, this solution demonstrates that modernizing pricing operations doesn’t require replacing core systems, it means extending them with automation built for the cloud that delivers speed, accuracy, and auditability where it matters most. To explore how Gorilla on AWS can accelerate your pricing workflows, visit the Gorilla AWS Marketplace listing or contact your AWS Energy & Utilities Industry Specialist.
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Gorilla – AWS Partner Spotlight
Gorilla is an AWS Differentiated (Software) Partner and AWS Energy & Utilities Software Competency Partner that provides Energy Margin Intelligence, unifying pricing, forecasting, hedging, billing, and finance into a single source of margin truth so energy retailers can see, steer, protect, and grow their margins with confidence.