CHALLENGE

Fragmented Data and a Lack of Strategic Insight

“We had treasure troves of data spread across disconnected systems. We knew insight was there, but we couldn’t access it.”

Sydney McDowell, Senior Manager, Quality Enablement, Angi

Angi’s leadership needed clarity on two critical drivers: revenue potential in sales conversations and early indicators of compliance or reputational risk. But with manual review covering just one conversation per agent each month, they lacked the visibility required to examine either with confidence.

Operational data lived across disconnected systems—an internal CRM and Snowflake—leaving teams without the context needed to target the right conversations or generate trustworthy signals.

Data Science was already working to model revenue and risk in Snowflake, but without conversational insight feeding those efforts, they couldn’t deliver the accuracy or business impact leaders expected.

SOLUTION

A Unified AI-Powered Intelligence Layer for the Business

Angi implemented MaestroQA, unifying conversation data with operational data from their CRM and Snowflake. Custom LLM prompts are calibrated for accuracy, giving teams clear, explainable insights they can trust.

LLM-powered analysis now turns every interaction into actionable signals that flow back into Snowflake to strengthen revenue and risk models.

Instead of manual sampling or disconnected systems, Angi now operates with a scalable model for uncovering buying intent, identifying risk, and informing high-stakes decisions across the business.

“Maestro lets Data Science and QA work in one place, align quickly, and ship better models. Without that shared layer, we’d be slower, more misaligned, and delivering weaker insights.”

Won Seo, Senior Data Scientist, Angi