Pre-Sale AI Makeover: $180M Building Materials Distributor

Intro

If you're 6–12 months from a PE process, the goal isn't an "AI strategy deck." The goal is measurable operating lift that survives diligence: higher margins, less working capital drag, and KPIs a buyer can validate without a week of spreadsheet archaeology.

This anonymized case study shows what BuildClub would implement in a common distributor situation—and the results you should expect when it's executed with urgency and governance.

Profile (anonymized)

• ~$180M annual revenue
• Regional building-materials distributor (lumber, drywall, roofing, etc.)
• 18 branches
• EBITDA margin ~7%
• Exploring a sale to private equity within 12 months

The situation

This distributor would be a classic: strong relationships, messy execution.

Pricing

• Branch managers would be pricing from memory and old spreadsheets
• Discount discipline would vary 4–5 points across branches
• No real visibility into customer-level margin

Inventory

• Stockouts on fast-movers, deadstock on long-tail SKUs
• Working capital tied up in the wrong items

Quoting

• Rep-to-quote conversion ~25%
• Quote turnaround often 24–48 hours, losing deals to faster competitors

Analytics

• KPIs would be assembled monthly in a flurry of CSV exports
• No standard buyer-grade view of branch performance

BuildClub Solution

1) AI pricing engine

• Standardize price floors and recommended prices by SKU, customer segment, and branch

• Surface real-time guardrails to reps inside their quoting tool

• Flag low-margin or out-of-policy quotes for review

• Tie outcomes to a clear discount-discipline KPI Expected results (after ~6 months)

• Average margin lift: ~80–120 bps on covered SKUs

• Discount variance across branches cut roughly in half

• ~50% fewer below-floor quotes Expected financial impact

• ~+$1.4M EBITDA from margin lift on ~70% of revenue

• ~$300k saved from fewer below-floor concessions

• Total pricing EBITDA impact: ~$1.7M/year

2) Inventory and replenishment intelligence

• Cluster SKUs by velocity, seasonality, and branch context

• Recommend reorder points and safety stock per SKU/branch

• Highlight deadstock and slow movers for proactive markdown / transfer

• Make recommendations explainable, not a black box Expected results (after ~6 months)

• Stockouts on key items: down ~30–40%

• Deadstock dollars: down ~$3–4M (one-time release)

• Inventory turns improving by ~0.4–0.6 turns Expected financial impact

• One-time working-capital release: ~$3.5M

• Recurring carrying-cost savings: ~$250k/year

• Lost-sale recovery: ~$700k of margin/year

3) Quoting copilot for reps

• LLM-based assistant that drafts quotes from short prompts and context

• Pulls in customer history, prior pricing, and freight assumptions

• Generates clear quote PDFs with consistent terms

• Tracks quote-to-win conversion and time-to-quote Expected results (after ~5 months)

• Average time-to-quote: 24h+ → ~3h

• Quote-to-win conversion: 25% → ~32%

• Reps spend less time wrestling with paperwork Expected financial impact

• ~+5–7% incremental revenue on covered customer segments

• At ~25% incremental margin → ~$1.0–1.4M EBITDA contribution

• Equivalent of ~6 FTEs of admin time freed up

4) Buyer-grade analytics layer

• Standardized branch P&L, SKU performance, and customer concentration

• Same numbers used internally and for diligence

• Natural-language queries (e.g., "Top 20 declining customers in the SE region last 12 months") Expected results

• Diligence cycle compressed from weeks of ad hoc work to days

• Fewer surprises in the QofE

• A clearer growth narrative tied to actual data

Expected pre-transaction outcome (run-rate, ~12 months)

• Revenue: ~$180M → ~$190M
• EBITDA: ~$12.6M (7.0%) → ~$16.5M (~8.7%)

What typically drives the EBITDA delta

• Pricing engine: +$1.7M
• Inventory + lost-sale recovery: +$0.95M
• Quoting copilot: +$1.2M
• Misc operational wins (back office, RGA, freight): +$0.4M

Valuation impact (illustrative)

• Before: ~7.5x on ~$12.6M → ~$95M enterprise value
• After: ~9.0x on ~$16.5M → ~$148M enterprise value Fixing the operational and pricing plumbing is what unlocks the multiple, not a generic "AI story."

If you're heading into a process

If you're 6–12 months from a transaction, the highest-leverage moves are pricing discipline, inventory intelligence, and a buyer-grade data layer. Get those right and the AI narrative tells itself.

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