Natural-Language Data Analyst for a PE Portfolio Company

Client:

PE-backed portfolio company

Time to First Value:

3 weeks

Users Enabled:

Finance + Operations teams

Our Problem

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.

Our Solution

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?

Our Results

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