The buyer was looking at a ~15-year-old, California-based managed service provider — recurring IT contracts inside one institutional vertical, long-tenured relationships, sticky logos, ~300 active agreements. On paper, an attractive recurring-revenue business: roughly $7.35M in the most recent year (~$21M across the three-year window), a healthy ~90% net retention, and an owner ready to transition.
But the blended gross margin was only ~32%, and net profit was thin. A high-cost-of-labor California base only sharpened the question the buyer’s own investors kept asking: why is profitability this thin on $7M of recurring-heavy revenue? The seller’s P&L couldn’t answer it — it could give a single blended margin, but it could not say which customers made money, which lost it, or where the real margin lived.
Why is EBITDA so low on $7M of recurring-heavy revenue — and which customers actually carry the business?
What the Buyer Needed Answered
- Why is EBITDA so low on $7M of recurring-heavy revenue?
- What is the revenue per client — and which clients carry the business?
- What is the revenue per contract, and per hour worked?
- What is the true margin by service line — recurring vs. resale vs. project vs. coaching?
- Which customers make money, and which lose it?
What Kautilya Delivered
Kautilya rebuilt the target’s economics from primary operational data — not management summaries — and handed the buyer a view of the business no P&L could provide: revenue, fully-loaded cost, and gross profit for every one of ~300 agreements, every service line, and every hour worked. The deliverable set spanned a reconstructed agreement-level workbook, a labor-cost model across 51,063 time entries, a three-year revenue rebuild with base/best/worst forecasts, a normalized EBITDA bridge with every adjustment independently toggleable, and a full cohort and churn review.
Five Businesses Inside One P&L
What the buyer was actually buying: underneath the single revenue figure sit five distinct lines, each with its own margin profile and its own multiple. Buying the blend means overpaying for the weak lines and underpaying for the strong one. Rebuilt over three years, the mix looked like this:
- Recurring MSP — $10.1M revenue (3-yr), 44.5% gross margin
- Resale — $8.7M revenue (3-yr), 18.3% gross margin
- Contracted Services — $1.3M revenue (3-yr), 14.5% gross margin
- Coaching — $0.9M revenue (3-yr), 46.2% gross margin
- Project (one-time) — $0.15M revenue (3-yr), 83.6% gross margin
~48% of revenue is the high-margin recurring book; ~47% is low-margin resale and contracted install. Day-to-day operations ran in ConnectWise, the PSA system of record for tickets, agreements, time and invoices. Custom queries in Metabase pulled that data into structured dumps, and the books sat in Intuit/QuickBooks. None of the three agreed at face value — so the first job was to make them agree, and the second was to read agreements nested inside one another, with recurring and project work keyed differently in the schema.
Three Systems, Reconciled to One Truth
Decision-grade economics start with data you can trust. Operational data in ConnectWise was extracted through Metabase queries, then validated back against ConnectWise to hold the sanity of every dump — and the result was further reconciled to the Intuit books. Nothing in the model rests on a number that wasn’t checked against its source.
Five Workstreams, Run in Parallel
Five workstreams ran in parallel and reconciled against one another. The hardest was joining labor to revenue — because the system didn’t make it easy.
Rebuilt revenue on the invoice as the unit of record, and sorted every line into five buckets — so the buyer sees revenue by what it actually is.
Turned 51,063 time entries into true, fully-loaded cost-to-serve per agreement — separating billable engineering from absorbed indirect time.
ConnectWise validated through Metabase and tied to the Intuit books, across accrual and cash — so the rebuild stands up to a lender.
Per-agreement margins, revenue-per-hour and per-contract, top-10 concentration, and a bridge the buyer can run scenario by scenario.
ARR by customer by month — where retention holds, where revenue leaks, and which customers to build the growth plan around.
Digital agreements nest child agreements tabbed within a parent, with nested billing rolling to a sibling. Recurring work is keyed on a record ID and joins directly to time entries. Project work is keyed on a separate order number — but its labor hours carried no matching key.
Our fix: where no key tied hours to a project order, labor was rolled up by company account and attributed back to project revenue. No off-the-shelf join would have caught this.
Finding I · It Isn’t One Business. It’s Two.
A single margin figure averages a high-margin service operation with a low-margin resale operation — two businesses with different economics and different multiples. By billing structure, agreement-billed (recurring) work runs ~47% margin; sales-order/project work runs ~14.5%. The blend (~32%) describes neither. The buyer was about to pay a blended price for a blended fiction. The rebuild let them value the recurring book on its own economics and price the low-margin resale for exactly what it is — passthrough volume, not hidden profit. We didn’t estimate it — we modeled every one of the 303 agreements: revenue from the invoices, labor from the re-costed hours, unit cost from the product records, down to a gross profit and margin per contract.
Finding II · A 45% Recurring Margin, Built Down to Prove It
A naive allocation flattered the recurring book to 62% — too generous, and not how the cost is actually incurred. Kautilya refused that number and built it down to something a lender could underwrite: charging the recurring book its fair share of non-billable labor spent winning and supporting it (−10 pts), then loading wages for benefits, PTO and payroll taxes the time records don’t show (−7 pts). The result — a defensible ~45% blended recurring margin — independently landed within ~1 point of the seller’s own internal analysis (43.8%). A recurring margin built the conservative way, validated against the seller’s own books, survives a lender’s scrutiny and doesn’t unwind after close.
Finding III · $3.2M of Labor That Never Touches a Customer
The investors’ question — why is profit so thin on $7M of recurring-heavy revenue? — had a concrete answer once the labor was fully reconstructed. Across three years, ~$3.2M of labor sits outside customer delivery: internal admin, sales and management time (19,140 hours on a single internal agreement alone), plus legacy internal tracking. The organization is built and staffed for a larger revenue base than it currently carries — a profitable core obscured by overhead, not a broken model. This is the bulk of the EBITDA gap and the clearest normalization lever in the deal — it turns “why is profit low?” from an unanswered risk into a quantified, fixable line in the bridge, and an integration thesis for a buyer who can grow into the cost base.
The Result
For the first time, the buyer could see the business the way an operator does: by customer, by service line, by hour. That ~32% blend resolved into its parts: a low-margin resale segment (~47% of revenue) pulling the average down, a coaching line in decline, and a recurring service book running near 45% once labor was fully loaded and overhead was set aside. The buyer stopped underwriting a 32% blend — and started underwriting the recurring book that actually drives the value.
- Value the recurring book on its true ~45% margin and price the low-margin resale and declining coaching line for what they are
- Answer the investors’ question with an evidence-backed story: an organization built for a larger revenue base, not a broken one
- Build a growth plan with a margin plan attached, using the cohort work to pinpoint the ideal customer profile
- Move with conviction — every number traces to a source the buyer and their lender can re-check
Deliverables
- Reconstructed workbook with a full source trail to ConnectWise, Metabase and Intuit
- Agreement-level P&L across all 303 agreements and five service-line buckets
- Labor cost model — 51,063 time entries loaded and allocated by agreement and role
- Revenue rebuild and forecast — FY2023–2025 actuals plus base/best/worst scenarios
- Three-system reconciliation across ConnectWise, Metabase and the Intuit ledger
- MRR, cohort and churn review — ARR by customer by month across 36 months
- Normalized EBITDA bridge, one page, every adjustment independently toggleable
- Per-pillar context memos — methodology, assumptions, caveats, and the findings that matter
Why This Engagement Matters
Most advisory firms won’t give a sub-$10M deal this much attention — the model doesn’t allow it. Kautilya put ~300 analyst-hours into this engagement over roughly a month, in near-daily working sessions, tracing individual records through the systems until each number was real. That intensity is the same five-phase methodology behind every Kautilya mandate — it’s how a buyer gets institutional-grade certainty on a deal a larger firm would have triaged with a sample and a template.
All figures in this case study are anonymized. Target identity, customer names and individual staff are withheld — the numbers reflect a live engagement and illustrate method, not investment advice. If you’re under LOI or staring at a data room on a similar deal, Kautilya can run the same rebuild on yours.