A custom AI assistant gave a Fortune 500 CPG company a faster, conversational way to explore NPS, stakeholder feedback, and performance data across 150+ supply chain technology products.
The company tracked NPS, CAPEX, OPEX, ROI, product reviews, deployment data, and stakeholder feedback across more than 150 supply chain technology products.
The data existed across sophisticated Power BI dashboards, but answering specific business questions still required complex queries and manual analysis. Multilingual feedback made exploration even harder, slowing the path from a question to a usable answer.
The company's internal AI team had also explored a similar solution but struggled to achieve the accuracy required for enterprise use.
150+ supply chain technology products across a global organization
NPS and stakeholder feedback spanning multiple languages
Complex analysis required across Power BI dashboards and operational data
Existing internal AI efforts struggled to deliver sufficiently accurate answers
EES designed and built a custom AI assistant combining enterprise data retrieval, a fine-tuned language model, specialized AI agents, and natural-language processing to make complex operational information accessible through conversation.
Structured NPS, product feedback, and supporting performance metrics from multiple sources into a reliable foundation for AI-powered exploration.
Combined a fine-tuned OpenAI language model, retrieval-augmented generation, and specialized AI agents to generate accurate, context-aware responses grounded in company data.
Enabled stakeholders to ask questions conversationally across languages, making complex analysis accessible without manually navigating dashboards or constructing queries.
Query response times were reduced by 95%, dramatically shortening the path from business question to informed decision.
Multilingual AI analysis improved the company's understanding of stakeholder sentiment across products, regions, and user groups.
Earlier identification of negative feedback helped teams respond faster and contributed to stronger vendor relationships.
The speed and accuracy of the solution led the company to explore centralizing additional data sources and expanding AI-driven decision support into other areas of the business. The results also led its internal AI team to engage EES for additional consulting work.
