Schneider Electric leverages Retrieval Augmented LLMs on SageMaker to ensure real-time updates in their CRM systems
Schneider Electric's account teams had to manually sort through new customers daily and link them to the correct parent entity in their CRM. Using LLMs alone was insufficient because their knowledge is limited by their training cutoff date, causing them to miss recent acquisitions, market news, and corporate restructurings.
The initial blanket prompt approach achieved only 55% accuracy and did not generalize well to education or healthcare domains, where the notion of 'parent company' is not meaningful.
Domain-specific prompt engineering boosted overall linking accuracy from 55% to 71%, reduced manual effort in linking workflows, and delivered faster data access to downstream analytics teams.
Frequently asked questions
What did this team achieve with this AI workflow?
Domain-specific prompt engineering boosted overall linking accuracy from 55% to 71%, reduced manual effort in linking workflows, and delivered faster data access to downstream analytics teams.
What tools did this team use?
Amazon SageMaker JumpStart, Flan-T5-XXL, LangChain, Serper, Amazon SageMaker Studio.
What results were reported?
Account linking accuracy after domain-specific prompts: 71%; Account linking accuracy before domain-specific prompts: 55%; Manual linking effort: reduce the manual effort; Downstream analytics data access speed: faster data access (source-reported, not independently verified).
What failed first in this deployment?
The initial blanket prompt approach achieved only 55% accuracy and did not generalize well to education or healthcare domains, where the notion of 'parent company' is not meaningful.
How is this back office ops AI workflow structured?
New account triggers linking need → Domain classification via RAG → Web search retrieval → LLM synthesizes parent company answer.