Enterprise AI at Startup Speeds: How a Leading Customer Engagement Platform Reliably Made AI Personalization Available to 50,000 Companies in Weeks

CHALLENGE

A leading customer engagement platform stands as a giant in cloud communications, powering personalized customer experiences for more than 50,000 companies and two million developers worldwide. As artificial intelligence reshapes the industry, the team recognized an opportunity to revolutionize both their internal operations and customer-facing solutions through generative AI. However, as a trusted B2B2C platform, they face a crucial balancing act: maintaining their reputation for reliability while innovating at the speed demanded by the market.

The organization’s AI Team understands the non-deterministic nature of generative AI. While speed is crucial, equally important is the assurance of quality and reliability in their solutions. To strike the right balance between these competing priorities, they needs a robust technology stack that provides comprehensive visibility and interpretability of how system outputs are generated. Without addressing these critical capabilities, the entire organization risks stagnation and falling behind the wave of innovation led by generative AI startups.This has delayed plans for the organization to launch an agent-builder for its clients to build the next generation of personalization into their client communications.

SOLUTION

The company’s AI team turned to Galileo to gain end-to-end visibility into the performance of their large language model (LLM) applications. With Galileo’s Observe module, the company’s Innovation Team implemented 24/7 monitoring of agentic workflows used internally as well as through its customer-facing applications through an intuitive dashboard. This setup ensured complete transparency into every LLM interaction, allowing for rapid optimization and debugging whenever issues arose.

Like many agent-based LLM applications, the team’s solution integrates multiple layers of tooling with a separate model layer (LLMs), orchestration layer powered by Langchain, and Pinecone serving as a vector database for RAG capabilities. These components work together to ensure the quality of inputs and outputs across the entire LLM system. When a user calls on the AI system, this chain of tools and reasoning are what finally produce a LLM response. With Galileo, the team is able to inspect every node in the chain to identify the step at which either a tooling or data error led to a failed or incorrect response.

When system issues arise, Galileo’s platform immediately alerts users, allowing for rapid root cause analysis and inspection down to the individual node or trace-level in just a few clicks. This allows resolution times and system improvements measured in minutes rather than hours or days. As their AI team notes, more often than not things go wrong at an intermediate step in the middle of a chain, therefore this level of visibility into every node is crucial

Impact

With Galileo serving as the observability and evaluation backbone for both internal and external LLM applications, the large organization has accelerated its AI innovation while maintaining enterprise-grade reliability. The comprehensive visibility and continuous monitoring provided by Galileo enabled its AI team to launch its agent-based application builder to its community in just weeks, smoothly transitioning from developer preview to public alpha with minimal user experience risks.

As one of the leading B2B2C platforms, the organization now moves at startup speed in deploying AI capabilities, while ensuring exceptional experiences and rapid issue resolution for its workforce of 5,000+ employees and community of over 50,000 customers. These customers are now using the company’s agent-building framework to power the next era of personalization for their respective users.