Startup Korl’s platform works across multiple systems to help create highly customized communications. The multi-agent, multimodal tool uses a mix of models from OpenAI, Gemini, and Anthropic to source and contextualize data. Korl’s AI agents aggregate information from across different systems — such as engineering documentation from Jira, outlines from Google Docs, designs from Figma, and project data from Salesforce — to build a multi-source view. The platform then automatically generates personalized quarterly business reviews (QBRs), renewal pitches, tailored presentations and other materials for use in important customer milestones. The company’s core differentiator is its ability to deliver “polished, customer-ready materials” such as slides, narratives and emails, “rather than merely analytics or raw insights.” Korl orchestrates an “ensemble of models” across OpenAI, Gemini and Anthropic, selecting the best model for the job at the time based on speed, accuracy and cost. The company has implemented “sophisticated fallback mechanisms” to mitigate failures. “Rather than just semantic or field-name matching, our approach evaluates additional factors like data sparsity to score and predict field matches,” said Berit Hoffmann, CEO and co-founder of Korl. To speed the process, Korl combines low-latency, high-throughput models (such as GPT-4o for rapid, context-building responses) with deeper analytical models (Claude 3.7 for more complex, customer-facing communications). “This ensures that we optimize for the best end user experience, making context-driven tradeoffs between immediacy and accuracy,” Hoffmann explained. Early indications suggest Korl can unlock at least a 1-point improvement in net revenue retention (NRR) for mid-market software companies because it uncovers previously unrealized product value and makes it easy to communicate that to customers before they churn or make renewal or expansion decisions. The platform also improves efficiency, reducing deck preparation time for each customer call from “multiple hours to minutes,” according to Hoffman.