Introduction:
Since the release of the SAP HANA Cloud vector engine, numerous internal and external stakeholders have been using it productively to achieve their AI business goals. In this blog, I would like to share the details of one such business use case developed by Wipro and certified as a Validated Partner Use Case by SAP. Wipro worked closely with the teams from SAP Hack2Build and SAP BTP Gen AI Lighthouse Program streams to bring their vision to action, specifically in the domain of energy procurement. These innovation-boosting programs from SAP highlight modern use cases leveraging SAP’s technology stack, including the SAP HANA Cloud vector engine and SAP generative AI hub within the SAP Business Technology Platform (SAP BTP).
Use Case: Smart Energy Procurement Advisor (SEPA)
The Problem Statement:
The business scenario centers around the challenges faced by energy procurement executives due to sudden surges in energy demand, that require quick identification of optimized procurement strategies. The complexity is also increased by the multitude of procurement options, including renewable energy, long-term contracts, spot markets, and demand-side response (DSR), as well as unstructured contract terms and fluctuating market prices. These factors make the procurement process tedious and complex, with a high dependency on experienced executives. Additionally, there is a lack of visibility and clarity in the rationale behind procurement decisions, making it difficult for the broader organization to understand and participate in the process.
Additionally, the early stages of implementing AI solutions at Wipro presented several challenges, particularly in maintaining security and data integrity. Ensuring that business processes and proprietary practices remained protected while integrating advanced technologies was crucial. Addressing these security challenges was essential for Wipro to gain customer trust and drive the adoption of AI-driven solutions.
Solution:
Wipro’s Smart Energy Procurement Assist (SEPA) is a Gen AI-powered platform specifically designed to address the complexities and challenges faced by energy procurement executives. By leveraging Natural Language Query (NLQ) capabilities and the Retrieval Augmented Generation (RAG) model, SEPA efficiently navigates the vast array of procurement options. The platform employs multi-dimensional spatial distance ranking to deliver the most relevant historical results, supports the simulation of custom scenarios, and facilitates stakeholder approvals with comprehensive track and trace features. Additionally, SEPA includes a dashboard for event management and detailed closure tracking. With future integration plans for Joule, SEPA enhances the procurement process by reducing costs, improving demand fulfillment, ensuring compliance, decreasing reliance on experienced executives, and strengthening tracking, governance, and auditability.
Building on the capabilities of SEPA, the introduction of the SAP Generative AI Hub and the enhancement of the SAP HANA Cloud database with the vector engine within the SAP BTP landscape provided a robust and secure foundation for Wipro’s AI initiatives. This setup allowed Wipro to access Large Language Models (LLMs) like GPT-3.5 and GPT-4.0 within a controlled and secure environment, ensuring that all data processing remained within the BTP suite, thereby mitigating the risks of data leakage or unauthorized access. The integration of the SAP HANA Cloud vector engine further enabled the efficient handling and storage of vector data, crucial for developing industry-grade AI solutions. Together, these tools empowered Wipro to leverage advanced AI capabilities while maintaining strict security protocols, offering a secure, efficient, and scalable platform for their AI-driven innovations.
Use Case Architecture
Value Add of SAP HANA Cloud vector engine in realizing Wipro’s use case to full potential
Wipro has been building AI solutions for long and more recently with GenAI. With flexible access to LLMs through the SAP Generative AI Hub, building powerful solutions has become relatively easy. Wipro’s SEPA leverages frameworks like Langchain, Text-ada-002 for embedding, and the SAP AI core (SAP Generative AI Hub) for accessing LLMs. The result is a robust Retrieval Augmented Generation (RAG) based solution, seamlessly powered by the SAP HANA Cloud vector engine.
As you might see from the solution architecture, SEPA simplifies data extraction from the S/4 stack into the SAP BTP landscape, where it becomes consumable by the Gen AI workflow. This allows for inclusion of S/4 data using natural language queries. Additionally, developers utilized SAP Build Code, which supports Joule for GenAI assisted coding.
In essence, Wipro’s SEPA solution is a great example of the value added by the SAP HANA Cloud vector engine. The key advantages for this use case includes:
Fast Vector Search: The vector engine enables quick retrieval of similar vectors, enhancing the efficiency of data processing and response generation.Inherent scalability, high performance, and efficient storage – The usual strong points of SAP HANA Cloud are bolstered now with vector support.Cost Efficiency Upgrading to SAP HANA Cloud incorporates vector support without extra charges, making it a cost-effective solution for enterprises.Integrated Data Management: Integrated with structured cross-sectional data, it seamlessly combines vector fields with traditional fields, supporting similar structures and enabling faster development.
Stay tuned for more Partner Use Case Highlights
The successful implementation of the SEPA solution showcases the potential of the SAP Hack2Build & SAP BTP Gen AI Lighthouse program in driving innovation and solving complex industry challenges. Please make sure to check this solution out on our SAP Business Accelerator Hub. Stay tuned for more blogs explaining additional use cases that have benefited from SAP’s tech stack. These upcoming posts will provide deeper insights into various AI-driven solutions, their development processes, and their transformative impacts on different sectors. Join us on this journey as we continue to push the boundaries of what’s possible with AI and SAP’s advanced technologies.
Note: This blog is a collaborative effort between SAP HANA Cloud Product Management and Wipro, highlighting the innovative use case enabled by the SAP HANA Cloud vector engine. For those interested in implementation details of this solution, Prasanna Mahalingam (prasanna.mahalingam1@wipro.com) from Wipro is available as the primary contact.
References:
Embedding Business Context with the SAP HANA Cloud, Vector EngineGenAI Reference Solution Architecture on SAP Business Technology PlatformSAP HANA Cloud Vector Engine: Quick FAQ ReferenceTry for yourself:Harnessing Generative AI Capabilities with SAP HANA Cloud Vector EngineExplore SAP HANA Cloud Vector Engine via Basic Trial
Introduction: Since the release of the SAP HANA Cloud vector engine, numerous internal and external stakeholders have been using it productively to achieve their AI business goals. In this blog, I would like to share the details of one such business use case developed by Wipro and certified as a Validated Partner Use Case by SAP. Wipro worked closely with the teams from SAP Hack2Build and SAP BTP Gen AI Lighthouse Program streams to bring their vision to action, specifically in the domain of energy procurement. These innovation-boosting programs from SAP highlight modern use cases leveraging SAP’s technology stack, including the SAP HANA Cloud vector engine and SAP generative AI hub within the SAP Business Technology Platform (SAP BTP).Use Case: Smart Energy Procurement Advisor (SEPA)The Problem Statement:The business scenario centers around the challenges faced by energy procurement executives due to sudden surges in energy demand, that require quick identification of optimized procurement strategies. The complexity is also increased by the multitude of procurement options, including renewable energy, long-term contracts, spot markets, and demand-side response (DSR), as well as unstructured contract terms and fluctuating market prices. These factors make the procurement process tedious and complex, with a high dependency on experienced executives. Additionally, there is a lack of visibility and clarity in the rationale behind procurement decisions, making it difficult for the broader organization to understand and participate in the process.Additionally, the early stages of implementing AI solutions at Wipro presented several challenges, particularly in maintaining security and data integrity. Ensuring that business processes and proprietary practices remained protected while integrating advanced technologies was crucial. Addressing these security challenges was essential for Wipro to gain customer trust and drive the adoption of AI-driven solutions.Solution:Wipro’s Smart Energy Procurement Assist (SEPA) is a Gen AI-powered platform specifically designed to address the complexities and challenges faced by energy procurement executives. By leveraging Natural Language Query (NLQ) capabilities and the Retrieval Augmented Generation (RAG) model, SEPA efficiently navigates the vast array of procurement options. The platform employs multi-dimensional spatial distance ranking to deliver the most relevant historical results, supports the simulation of custom scenarios, and facilitates stakeholder approvals with comprehensive track and trace features. Additionally, SEPA includes a dashboard for event management and detailed closure tracking. With future integration plans for Joule, SEPA enhances the procurement process by reducing costs, improving demand fulfillment, ensuring compliance, decreasing reliance on experienced executives, and strengthening tracking, governance, and auditability.Building on the capabilities of SEPA, the introduction of the SAP Generative AI Hub and the enhancement of the SAP HANA Cloud database with the vector engine within the SAP BTP landscape provided a robust and secure foundation for Wipro’s AI initiatives. This setup allowed Wipro to access Large Language Models (LLMs) like GPT-3.5 and GPT-4.0 within a controlled and secure environment, ensuring that all data processing remained within the BTP suite, thereby mitigating the risks of data leakage or unauthorized access. The integration of the SAP HANA Cloud vector engine further enabled the efficient handling and storage of vector data, crucial for developing industry-grade AI solutions. Together, these tools empowered Wipro to leverage advanced AI capabilities while maintaining strict security protocols, offering a secure, efficient, and scalable platform for their AI-driven innovations.Use Case Architecture Value Add of SAP HANA Cloud vector engine in realizing Wipro’s use case to full potentialWipro has been building AI solutions for long and more recently with GenAI. With flexible access to LLMs through the SAP Generative AI Hub, building powerful solutions has become relatively easy. Wipro’s SEPA leverages frameworks like Langchain, Text-ada-002 for embedding, and the SAP AI core (SAP Generative AI Hub) for accessing LLMs. The result is a robust Retrieval Augmented Generation (RAG) based solution, seamlessly powered by the SAP HANA Cloud vector engine.As you might see from the solution architecture, SEPA simplifies data extraction from the S/4 stack into the SAP BTP landscape, where it becomes consumable by the Gen AI workflow. This allows for inclusion of S/4 data using natural language queries. Additionally, developers utilized SAP Build Code, which supports Joule for GenAI assisted coding.In essence, Wipro’s SEPA solution is a great example of the value added by the SAP HANA Cloud vector engine. The key advantages for this use case includes:Fast Vector Search: The vector engine enables quick retrieval of similar vectors, enhancing the efficiency of data processing and response generation.Inherent scalability, high performance, and efficient storage – The usual strong points of SAP HANA Cloud are bolstered now with vector support.Cost Efficiency Upgrading to SAP HANA Cloud incorporates vector support without extra charges, making it a cost-effective solution for enterprises.Integrated Data Management: Integrated with structured cross-sectional data, it seamlessly combines vector fields with traditional fields, supporting similar structures and enabling faster development.Stay tuned for more Partner Use Case HighlightsThe successful implementation of the SEPA solution showcases the potential of the SAP Hack2Build & SAP BTP Gen AI Lighthouse program in driving innovation and solving complex industry challenges. Please make sure to check this solution out on our SAP Business Accelerator Hub. Stay tuned for more blogs explaining additional use cases that have benefited from SAP’s tech stack. These upcoming posts will provide deeper insights into various AI-driven solutions, their development processes, and their transformative impacts on different sectors. Join us on this journey as we continue to push the boundaries of what’s possible with AI and SAP’s advanced technologies.Note: This blog is a collaborative effort between SAP HANA Cloud Product Management and Wipro, highlighting the innovative use case enabled by the SAP HANA Cloud vector engine. For those interested in implementation details of this solution, Prasanna Mahalingam (prasanna.mahalingam1@wipro.com) from Wipro is available as the primary contact.References:Embedding Business Context with the SAP HANA Cloud, Vector EngineGenAI Reference Solution Architecture on SAP Business Technology PlatformSAP HANA Cloud Vector Engine: Quick FAQ ReferenceTry for yourself:Harnessing Generative AI Capabilities with SAP HANA Cloud Vector EngineExplore SAP HANA Cloud Vector Engine via Basic Trial Read More Technology Blogs by SAP articles
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