Natural-Language Data Analyst for a PE Portfolio Company
Client:
PE-backed portfolio company
Problem
The PE sponsor needed to take 8–10% out of SG&A in 180 days while protecting revenue. But the portfolio company’s data lived in five silos (Postgres, Snowflake finance mart, Salesforce, WMS, and freight invoices).
Non-technical business leaders depended on a small BI team for every ad-hoc question, leading to:
- 4–7 day wait times for simple analyses (“Which SKUs are unprofitable after freight?”)
- Missed savings in freight, inventory, and pricing leakage due to slow discovery
- Low adoption of existing dashboards that couldn’t flex to new questions
Executives asked for “analyst-on-demand” capabilities without expanding headcount or risking data governance.
Solution
We deployed a Natural-Language Data Analyst Agent that connects directly (read-only) to the company’s databases and lets business users ask questions in plain English from Slack or the web.
What we built (4-week rollout):
Secure data connectors & governance
- Read-only roles to Snowflake/Postgres; row-level security honored; SSO/RBAC
- Automatic data catalog & semantic layer (business names for tables/fields)
Guardrailed query generation
- The agent translates questions → validated SQL plans with schema awareness
- Policy checks (cost/time limits, PII redaction, sample-then-full execution)
- Lineage & reproducibility: every answer links to the SQL and source tables
Business-ready answers
- Outputs include tables, charts, and narratives (“why this changed vs. last week”)
- One-click follow-ups (e.g., “by region,” “last 90 days,” “top 20 outliers”)
- Slack & email digests with subscriptions to recurring questions
Change management
- We started with three high-ROI domains: Freight, Inventory, Pricing
- 90-minute enablement sessions; embedded prompts for good questions to ask
- Lightweight approval workflow for sensitive queries (finance controller review)
Example natural-language questions users asked on day 1:
- “Show SKUs with negative contribution margin after freight, last 60 days.”
- “Which customers received expedited shipments twice in a week? Cost impact?”
- “Where do we have >90 days of inventory and falling demand?”
- “Which reps offered >3 price overrides this month? Margin change vs. list?”
- “What’s the cash conversion cycle trend by region? What moved the most week-over-week?
Results
Run-rate savings identified and captured within 90 days: $3.6M
(anonymized; documented via finance sign-off and SQL lineage)
- Freight optimization — $1.2M: Eliminated duplicate expedites, flagged carriers with >15% rate variance, and enforced mode-switch rules surfaced by the agent
- Inventory reduction — $1.1M: Right-sized reorder points on long-tail SKUs; 9-day reduction in days-on-hand across two DCs
- Pricing leakage — $900k: Detected override patterns and misaligned customer-specific discounts; raised realized margin by 120 bps in targeted segments
- Analytics productivity — $400k: Contractor spend down 63%; internal BI backlog cut 76%; median time-to-answer dropped from 4 days to 14 seconds
Adoption & accuracy:
- 120+ monthly active business users; 92% "useful" rating on first-attempt answers
- 100% auditable SQL and source data links on executive-level answers
Why it mattered to the PE sponsor
- Fast, measurable cost takeout without new FTEs
- Repeatable playbook now rolling out to two additional portfolio companies with the same agent, semantics, and governance patterns
- Lower risk: read-only connections, row-level security honored, and every executive slide links back to the exact query and timestamp
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