PE Portfolio Company Gives Teams a Natural-Language Data Analyst

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 natural language agent that lets teams ask questions in plain English through Slack or a web interface. Read only connectors to Postgres and Snowflake enforce SSO and role based access, and an automatic semantic layer maps business terms to the right tables and fields. SQL is generated behind guardrails (cost limits, PII redaction, sample before full run), and answers come back as tables, charts, narratives, and one click follow ups, with Slack and email subscriptions for recurring questions.

Read only database roles with row level security kept access safe, a lightweight approval workflow covered sensitive queries, and 90 minute enablement sessions with embedded prompt guidance drove adoption. First value came in three to four weeks, focused on freight, inventory, and pricing.

Finance and operations leaders at PE backed companies who need self serve, auditable answers fast, especially where data is spread across warehouses, CRMs, and operational systems.