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Technology Due Diligence for Private Equity

Technology diligence that shapes better deals and drives real value creation.

We evaluate technology in businesses where work happens in the field—whether you're acquiring the software company or the operating company that runs on it. We've built both.

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Due diligence professional
$135B
PE Capital into Industrial & Construction (2024)
1,800+
Industrial PE Deals Closed in 2024
33%
YoY Growth in Construction M&A
1
Dedicated Diligence Offering from Industry-Native Practitioners
"North Labs was an invaluable partner during our side-sell diligence. Their team didn't just provide reports; they collaborated deeply with our staff, bringing a level of expertise that gave us total confidence in the process." — Nate Alvar, VP of Engineering, FMG
The Problem

Three gaps we fill.

The Generalist Gap

They have the same playbook for construction as they do FinTech

Generalist providers can tell you if the code is well-written. They can't tell you if the product works in the field—if the app runs without cell service, if estimating handles regional labor rates, or if the payment flow matches how trades businesses get paid.

The Industry Gap

Know the market, but can't evaluate the technology

Industry advisors understand buyers, structure, and growth. But when the target claims their AI "predicts project delays," these firms take the pitch deck at face value. Nobody on their team can open the hood.

The Operating Company Gap

Who validates the technology thesis?

When you acquire a $300M mechanical contractor, the value creation plan says technology will drive efficiency. But who evaluates whether they can actually adopt new systems—what it will cost and how long it will take?

How We Help

Two kinds of deals. One team that understands both.

Whether the target builds the technology or runs on it, we validate if the technology thesis holds up.

Engagement Type 01

Software & Platform Targets

Technology assessment of vertical SaaS, AI, and platform companies serving construction, trades, and industrial markets—evaluated by people who understand the end users.

"We're acquiring a field service management platform. Is the technology defensible? Is the AI real? What's the technical debt exposure?"
Architecture & code quality deep-dive AI/ML capability validation Competitive moat & defensibility analysis EBITDA-quantified value creation roadmap
Engagement Type 02

Operating Company Targets

Technology posture assessment for PE firms acquiring contractors, manufacturers, and industrial services companies. We evaluate current state, size the transformation opportunity, and validate the thesis in your deal model.

"We're acquiring a $200M mechanical contractor. The deal model assumes $4M in technology-driven margin improvement. Can they actually get there?"
Current technology stack & maturity assessment Technology transformation opportunity sizing Implementation roadmap & risk analysis 100-day plan with projected EBITDA impact
Service Structure

Prioritizing the customer's deal timeline.

Fixed-fee. Defined scope. No surprises. Built for deal speed.

01
Scoped Within 48 Hours

Fixed fee, defined deliverables, clear timeline. No discovery phase. No meter running.

02
IC-Ready Deliverables

Reports built for the investment committee. Quantified risk, EBITDA-impact estimates, and clear recommendations.

03
We Stay Through Close

Post-report support through negotiation and close. The same team builds the 100-day roadmap and executes it.

Why North Labs

We are experts in building the technology customers are evaluating.

Practitioners from the trades who build production software today—not consultants who read about it.

01

Operators First

Founded by a veteran who started in the trades. We know the operational reality technology has to survive.

02

Builders, Not Observers

We operate production intelligence platforms for contractors today. When we identify an opportunity, we're the team that can build it.

03

AI Depth That's Earned

Not a checkbox on "uses AI." We validate whether models deliver measurable value in field conditions—messy data, edge cases, and all.

04

Value Creation, Not Just Risk

Beyond identifying problems—you get EBITDA-quantified opportunities, transformation roadmaps, and actionable implementation plans.

05

Beyond the Report

The same team builds your 100-day roadmap and manages post-close transformation. One relationship from diligence through value creation.

06

Deal-Speed Delivery

Fixed-fee, clear scope, IC-ready deliverables—not academic exercises. Senior practitioners on every engagement.

Engagement Outcomes

What this looks like in practice.

Three engagements across three verticals—each uncovering findings that generalist providers missed.

Regulated Vertical SaaS

Validating the technology thesis behind a vertical SaaS platform in regulated financial services

A PE firm needed to know whether a platform serving 100,000+ financial professionals had genuine technology moats—or just captured share ahead of competitors.

Leading vertical SaaS platform. $16M+ ARR, 100K+ users, 3 acquisitions consolidated onto unified architecture.
$87K
Avg. Eng Salary vs. $130K Industry
121%
Enterprise Net Revenue Retention
41.5%
Support Volume Reduction via AI

Capital-efficient R&D engine — 2x output at 60% of industry cost with stable defect rates across three concurrent migrations

Measurable AI ROI: shipped 4 months post-GPT-3.5, industry leader recognition, 2x content output with headcount reduction

Proven M&A integration: 94-101% ARR retention across 3 migrations, $1M+ recurring savings from consolidation

Critical database dependency — unmaintained open-source DB requiring 12-18 month migration, informing deal structuring

What a generalist may have missed

Whether the AI generated compliance-grade output versus demo-ware. Whether R&D cost advantage was structural or just underpayment. How integrations preserved—or destroyed—the revenue that made each deal worth doing.

Trades & Field Service

Rewriting the deal model for a field service platform by finding what generalist diligence missed

A prior generalist DD provider rated this platform favorably on standard metrics. We found five domain-specific issues that determined whether the deal thesis held.

Field service platform for HVAC, plumbing, and electrical contractors. ~$45M ARR, 6,800+ customers, 185K monthly field technicians.
$8-14M
Incremental Annual Payment Revenue
5-6 pts
Churn Recharacterized as Seasonal
104-108%
Actual NRR (Stable Base)

Flat-rate pricing engine was the real moat — 48K+ line items, regional labor rates, 18-22% ticket increase, 3-4 year replication barrier

Offline sync was fragile: last-write-wins works for single techs, breaks for multi-crew commercial expansion the deal model assumed

Embedded fintech undermonetized 40-60% — 1.1% take rate vs. 1.8-2.5% for peers. Processor renegotiation = largest value creation opportunity

AI scheduling was pre-production — 23% adoption. Optimized for drive time but ignored skill match, equipment, and cascading delays

What a generalist may have missed

14% gross churn was actually 8-9% competitive plus seasonal patterns. Offline architecture couldn't survive commercial expansion. AI scheduling would fail because it didn't understand what dispatchers actually manage.

Industrial & Manufacturing

Reframing a $200M+ industrial AI acquisition from "buy the models" to "protect the data"

A prior provider gave this a clean bill of health. We found three deal-level risks they missed—and a data monetization opportunity not in the deal model.

Predictive maintenance platform for heavy manufacturing and industrial facilities. ~$38M ARR, 1.2M sensors, 14B daily data points across 340+ deployments.
$6-12M
Data Monetization Opportunity Found
34%
ML False Positive Rate in Production
3
Deal-Level Risks Prior DD Missed

Sensor data pipeline was the real asset — 17 industrial protocols, 18-24 months ahead. Reframed acquisition from "AI company" to "data infrastructure company"

ML accuracy overstated: 92% on curated data, 34% false positive rate in the field. Vibration models applied to equipment where vibration is irrelevant

OT security exposure missed by prior IT-focused assessment — SCADA/DCS adjacency in 38% of deployments, zero IEC 62443 alignment

$6-12M data monetization opportunity unsized in the deal model — OEM benchmarking, insurance data licensing, energy analytics

What a generalist may have missed

Vibration-trained models produce noise on electrical switchgear. The OT boundary was the real security risk—not the cloud. The data pipeline was the real asset, not the AI.

100%

Every PE firm that's engaged us has used us for more due diligence, expanded to engineering execution, or referred us to another firm.

Marian Pailden
"Our people have the scarce combination of deep technical experience, expert domain understanding, and proven advisory experience."
— Marian Pailden, Manager - Operations

Evaluating a deal?

Start a Conversation Or reach out directly to our Managing Partner: collin@northlabs.io