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 on standard orders, 48–72 hours on multi-product packages — 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

• ~+$1.35M EBITDA from margin lift on ~70% of revenue

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

• Total pricing EBITDA impact: ~$1.65M/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 on recurring-customer orders: 24h+ → under 2h for ~80% of quotes; same-day for complex multi-product packages

• 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, ~9 months in the seat)

• Revenue: ~$180M → ~$190M (roughly half from quote-velocity-driven wins, half from recovered lost sales)
• EBITDA: ~$12.6M (7.0%) → ~$16.5M (~8.7%) — a 168 bps margin lift
• Discount variance across branches: roughly halved

EBITDA bridge

• Pricing engine (margin lift + fewer below-floor quotes): +$1.65M
• Inventory intelligence (lost-sale recovery + carrying-cost reduction): +$0.95M
• Quoting copilot (conversion lift + freed-up rep time): +$1.20M
• Procurement & freight optimization: +$0.10M
• Total: +$3.9M (matches the $12.6M → $16.5M lift)

Valuation impact (illustrative)

• Before: ~7.5x on ~$12.6M → ~$95M enterprise value
• After: ~9.0x on ~$16.5M → ~$149M enterprise value

Decomposition of the ~$54M EV lift

• EBITDA growth at the original 7.5x multiple: ~$29M
• Multiple expansion (1.5 turns) on the new EBITDA base: ~$25M The multiple expansion is the part that doesn't happen without the operational plumbing being already documented and trended. The buyer's IC underwrote it because every KPI was real and verifiable, not because of a slide deck. 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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