The PE sponsor required an 8 to 10 percent cut in SG&A within 180 days while holding revenue.
Data lived in five isolated systems (Postgres, a Snowflake finance mart, Salesforce, a WMS, and freight invoices), so ad hoc questions took four to seven days, savings opportunities were missed, dashboard adoption was low, and a small BI team created a bottleneck.
Eliza built a governed natural language data agent for a PE portfolio company, letting finance and operations query Snowflake and Postgres in plain English through Slack or the web, with a semantic layer, guardrailed SQL, and full source lineage.
$3.6M in run rate savings in 90 days, verified by SQL lineage and finance sign off (freight $1.2M, inventory $1.1M, pricing $900K, analytics $400K)
Median time to answer a business question fell from about four days to 14 seconds
120+ monthly active users at a 92 percent useful rating on first attempt answers
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