Firstshift

Domain-specific Generative AI for Democratizing Supply Chain Insights

We believe that if deployed thoughtfully, Generative AI can play a significant role in the Supply Chain domain as well - in democratizing Supply Chain Insights to a broader set of stakeholders within an enterprise. However, accuracy and recall of these insights are of primary importance as decisions driven by these insights have significant business impact.

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About the Customer

Generative AI has shown strong potential in enterprise areas such as marketing and customer support. Most applications in these domains focus on unstructured data, using it to answer queries or generate content. A common approach is Retrieval-Augmented Generation (RAG), which builds vector embeddings from the data and provides a query interface to generate responses, such as marketing emails or support replies.

Specialized data models

Supply chain data uses expert-defined models that RAG and text-to-SQL overlook. SCOGEN trains small models to stay within these structured data constraints.

Domain-specific compiler.

Supply chain vocabulary, though different from schema names, is vital for planners. SCOGEN's small models use this domain knowledge to translate natural language into accurate data operations.

Database-native runtime

Once SCOGEN interprets natural language, it compiles it into optimized operator trees and runs them directly on existing databases. This ensures efficient, secure execution within tenant boundaries, crucial for multi-tenant environments.

Performance and cost optimizer

Supply chain queries are complex and costly. SCOGEN's optimizer reuses shared computations to cut LLM and processing overhead, reducing latency from 30s to under 2s and flattening costs.