AI Makeover Before a Sale — $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
• ~20–25% of line items would land below target margin

Inventory

• ~$32M of inventory would be on the books
• Slow-moving overstock would coexist with stock-outs on top-selling SKUs

Sales ops

• Reps would spend ~30–40% of their time hunting for prices and availability
• No structured win/loss or quote analytics would exist

Reporting

• Basic questions like “Which customers are margin-dilutive?” would take days of spreadsheet work

Advisors would be clear: the story is attractive, but buyers would push hard on thin margins and bloated working capital.

BuildClub Solution

1) AI-assisted pricing

• Ingest ~24 months of transactions (~14M line items), vendor costs, freight tables, and basic customer/branch segmentation
• Build a hybrid rules + LLM pricing engine that would:
  • Recommend target margins at the SKU × segment × branch level
  • Flag recurring margin leaks (habitual discounts, stale costs, freight errors)
  • Produce weekly, branch-specific price books
• Embed guidance into quoting so reps would see:
  • Green zone = recommended price range
  • Red zone = below-threshold margin
  • Reps would keep final control

Expected results

• Average gross margin lift of ~180 bps (e.g., 21.2% → 23.0%)
• On $180M revenue: ~+$3.24M gross profit
• With ~70% drop-through: ~+$2.3M incremental EBITDA

2) Inventory & replenishment optimization

• Deploy an AI reorder suggestion engine combining demand patterns, seasonality, lead times, and branch-transfer logic
• Add an LLM “inventory health” assistant so buyers could ask:
  • “Which SKUs should we burn down this quarter?”
  • “Where are we tying up the most cash for the lowest turns?”
  • “Which branches should transfer inventory this week to prevent stock-outs?”

Expected results

• Inventory reduced from ~$32M → ~$27M while maintaining service levels
• ~$5M in working capital released
• Carry cost reduction (8% cost of capital): ~+$400k/year
• Obsolescence reduction: ~+$250k/year
• EBITDA-equivalent benefit: ~+$650k/year

3) Sales & quoting co-pilot

• Embed a local LLM co-pilot inside the quoting workflow that would:
  • Generate draft quotes from rough scopes or takeoffs
  • Suggest alternates based on real-time availability
  • Surface price history and recommended margins at the moment of decision

Expected results

• Quote prep time cut by ~40–50%
• Reps freed up ~1.5 hours/day to sell instead of chase data
• Revenue uplift of ~3% on targeted segments from faster responsiveness and better alternates
• ≈ +$1.5M incremental gross profit
• ≈ +$1.05M incremental EBITDA (at ~70% drop-through)

4) Deal-ready analytics

• Build buyer-facing dashboards for:
  • SKU-level margins
  • Inventory turns and stock-out rates
  • Price realization by branch and rep
• Document data sources and logic so a buyer’s analyst could validate quickly

Expected results

• Faster diligence
• Fewer margin debates
• A platform story a buyer can actually underwrite

Expected pre-transaction outcome (run-rate, ~9–10 months)

• Revenue: ~$185M
• EBITDA margin: ~7.0% → ~10.9%
• EBITDA: ~$12.6M → ~$20.2M
• Incremental EBITDA: ~+$7.6M, with ~$4–4.5M clearly attributable to the AI initiatives

Valuation impact (illustrative)

• Before: ~8x EBITDA → ~$100M enterprise value
• After: ~9.5x on higher EBITDA → ~$190M+ enterprise value

Even with conservative attribution, this typically represents tens of millions in incremental value created or protected in under a year.

If you’re heading into a process

If you’re 6–12 months from a sale, the window is now. The goal is not a science project. It’s a measurable operating lift that shows up in run-rate EBITDA and a diligence-proof story.

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