The 9-month AI Makeover. Lift EBITDA before the buyer prices the risk.

BuildClub deploys AI inside the operational systems of mid-market businesses preparing for a sale, a strategic transaction, or a PE exit. We work directly with founder-CEOs, PE-backed operators in years 3–5 of a hold, and the sell-side investment bankers and M&A advisors who represent them. The engagement is short, sharp, and tied to one number: the deal value.

Two anonymized cases. Same playbook. Different deal economics.

Both engagements ran approximately 9 months. Both EBITDA lifts held under buyer diligence. Both moved the multiple. The full write-ups live in the journal — the headlines are below.

Case 1 · $180M building-materials distributor, 12 months from a PE process

Before: 18 branches across the Mid-Atlantic, ~7% EBITDA margin, pricing from memory and three-year-old spreadsheets, discount discipline varying 4–5 points across branches, stockouts on fast-movers running 8–11% while $3–4M sat in long-tail deadstock, quote turnaround 24–48 hours on standard orders and 48–72 hours on multi-product packages, KPIs assembled monthly from a flurry of CSV exports.

What we built in nine months:

• AI pricing engine with real-time guardrails inside the existing quoting tool, branch-by-branch discount discipline scoring, and customer-level margin visibility

• Inventory and replenishment intelligence with explainable reorder points, SKU velocity clustering, and proactive deadstock surfacing for markdown or branch transfer

• LLM-based quoting copilot pulling customer history, prior pricing, freight assumptions, and the new pricing-engine guardrails into a single draft a rep can edit in 2–3 minutes

• Buyer-grade analytics layer with standardized branch P&L, customer concentration views, and natural-language query against a single warehouse Outcome the buyer saw (run-rate at month 9):

• Revenue ~$180M → ~$190M (5.6% growth, of which roughly half is quote-velocity-driven and half is recovered lost sales)

• EBITDA ~$12.6M (7.0%) → ~$16.5M (~8.7% — a 168 bps margin lift)

• Quote turnaround 24–48 hours → under 2 hours for 80% of recurring-customer quotes, same-day for complex multi-product packages

• 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 and freight optimization: +$0.10M

• Total: +$3.9M (matches the headline $12.6M → $16.5M lift) Valuation impact: ~7.5x on $12.6M ($95M EV) → ~9.0x on $16.5M ($149M EV). Roughly $29M of the $54M lift came from EBITDA growth at the original multiple; the other $25M came from multiple expansion the buyer's IC was willing to underwrite because every operational KPI was already documented and trended.

Read the full $180M case study →

Case 2 · $95M field-services business, 9 months from a strategic sale

Before: 11 branches across 5 Midwest states, ~9% EBITDA margin, dispatch run from whiteboards and phone calls with techs zigzagging across territories, first-time fix stuck at ~63%, invoices going out 10–20 days after job completion, change orders routinely missed or under-billed, no unified asset history across the customer base.

What we built in nine months:

• AI dispatch and routing layer trained on three years of work orders (~220k jobs), technician skills and certifications, and SLA constraints — with first-time-fix and tech utilization as the primary KPIs the deal team would defend

• LLM job-to-invoice assistant turning field notes and photos into structured line items, properly captured change orders, and customer-ready work summaries

• Customer and asset intelligence layer with natural-language query across service history, SLA performance, and emergency-call patterns by site Outcome the buyer saw (run-rate at month 9):

• Revenue ~$95M → ~$99M (4.2% growth, mostly from change orders that previously slipped through the cracks and a small lift in attach-rate on recurring contracts)

• EBITDA ~$8.6M (9.1%) → ~$10.7M (~10.8% — a 176 bps margin lift)

• First-time fix 63% → 76%

• Time from job completion to invoice 11 days → 3 days

• Tech utilization (billable vs. paid hours) 62% → 71% EBITDA bridge:

• Dispatch and routing (utilization, fuel, first-time-fix): +$1.25M

• Quote-to-cash automation (faster billing, recovered change orders, fewer write-offs): +$0.70M

• Overhead and small wins (back-office automation, lower dispatch headcount cost): +$0.30M

• Pricing-discipline give-back (some small-account churn from tighter terms): −$0.15M

• Total: +$2.1M (matches the headline $8.6M → $10.7M lift) Valuation impact: ~8.5x on $8.6M ($73M EV) → ~9.5x on $10.7M ($102M EV). Of the ~$29M lift, roughly $18M came from EBITDA growth at the original multiple and $11M from a one-turn multiple expansion the buyer's IC validated against operational KPIs rather than narrative. We deliberately did not push for a 10x multiple in the model — the buyer paid for what they could verify, not what we claimed.

Read the full $95M case study →

For investment bankers & M&A advisors

This is the part that doesn't usually get said out loud: the difference between a Q1 close at the asking price and a Q3 close 15% below it is almost always operational, not strategic. The buyer didn't change their mind about the sector. The diligence team found something they didn't trust. We work directly with sell-side advisors to make sure that doesn't happen.

Referral pitch — why this is your asset, not ours. Send us your best clients 6–9 months before the data room opens. We compress the operational plumbing into something a QofE team can validate without theatrics. You close at multiple expansion, not multiple compression. We work entirely under your engagement letter or alongside it — the client is yours, the deal is yours, the credit is yours. We just want the work to be there when you need it.

Working alongside a live process. If a client is already in market, we can still run a compressed 4–6 month sprint focused on the three highest-impact buyer concerns the banker has surfaced from preliminary diligence calls. Smaller scope, faster turnaround, designed to neutralize a specific objection rather than reshape the whole operating story.

What the advisor sees. Monthly check-ins with the deal team. A clean diligence binder ready when the buyer asks. A KPI baseline narrative that matches what the CFO is presenting. No surprises at the data room.

Who we work best with. Boutique and middle-market M&A advisors. Sell-side teams at investment banks running deals in the $50M–$1B EV range. PE operating partners 12–18 months from an exit. Independent sponsors approaching a refi or recap. Special-situations advisors carving out a non-core line.

If you'd like to white-label the offering inside an advisor relationship, we'll structure that too.

What good looks like at transaction close

• EBITDA margin lifted 150–300 bps versus the Sprint 1 baseline, validated by 6+ months of run-rate before the data room opens
• Multiple expansion of 1.0–2.0 turns versus comparable-company benchmarks, supported by buyer-validated operational KPIs rather than narrative
• Diligence cycle compressed by 30–50% because the data room ships pre-built with control narratives, KPI lineage, and a clean QofE narrative
• Buyer-side surprises eliminated — by the time the QofE team gets to the asset, every operational improvement is already documented and reproducible
• A defensible AI story for the buyer's investment committee that doesn't depend on a slide deck — it's in the numbers, in the systems, and in the people running them These ranges come out of the diagnostic, calibrated to your sector, deal scale, and starting operational maturity. They are the modeled case, not the best case.