White Papers
Evals in Production
Drawing on agentic AI deployments across financial services, life sciences, investment management, and technology sectors.
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In an era driven by digital transformation, the reliability and performance of software applications are paramount. However, unexpected issues can arise, leading to downtime, user frustration, and business losses. This white paper explores the potential of predictive modeling to anticipate and mitigate application issues before they escalate. By leveraging advanced data analytics and machine learning techniques, organizations can proactively identify trends, patterns, and anomalies, enabling timely intervention and optimization.
This paper outlines the framework for developing and implementing a predictive model tailored to forecasting application issues, emphasizing its potential benefits and best practices for implementation.
Drawing on agentic AI deployments across financial services, life sciences, investment management, and technology sectors.
Building a Trusted, Conversational Data Layer for Financial Services Financial services companies rely on data to operate. Things like credit ratings are based on data and market intelligence is built using data. The systems that support these things need to be easy to see, secure and well managed. But the information needed to manage all […]
AI Practitioner’s Guide to Building Production-Ready LLM Applicationss Most enterprise LLM projects stall between pilot and production. The difference is rarely the model, it is the architecture. This guide synthesizes what we have learned across 35+ enterprise implementations, organized into seven application patterns we have repeatedly delivered. The focus is on the decisions that determined […]
