Closing the Accuracy Gap in GenAI with Progressive Graphs: A Case Study

While retrieval-augmented generation (RAG) has helped organizations ground AI responses in enterprise data, many implementations still struggle with incomplete, inaccurate or hallucinated outputs—especially in domain-specific and highly regulated environments.

This whitepaper explores Progressive Graphs, the ProgressÒ approach to trusted AI. By combining semantic Graph RAG with continuous, human-validated feedback, Progressive Graphs help AI systems learn, adapt and improve over time. In a real-world case study, this approach increased AI response accuracy from 48% to 90%.

Download this whitepaper to learn:

• Why traditional vector-only RAG approaches often fall short
• How Progressive Graphs continuously improve AI accuracy through human validation
• How semantic knowledge graphs help create more trustworthy AI
• How hybrid retrieval strategies strengthen enterprise AI performance
• What drove a real-world increase in AI response accuracy from 48% to 90%

Whether you're building AI applications for financial services, healthcare, government or other data-intensive industries, this whitepaper provides practical insights for delivering more accurate, transparent and production-ready GenAI solutions.

Download the whitepaper to discover how Progressive Graphs can help your organization close the AI accuracy gap and build more trusted enterprise AI.