Industrial Intelligence

Digital infrastructure
for the industrial
economy

ProEdge Operations builds bespoke software and intelligence platforms that bring operational precision to industrial verticals. Purpose built. Operator led. Engineered for the field.

Assess Your Readiness Our Thesis
2
Active Platforms
$2T+
Addressable Markets
DFW
Headquarters
Investment Thesis

Industrial categories are underbuilt in software

The industrial economy generates trillions in annual revenue yet operates on decades old workflows, disconnected systems, and paper based processes. The gap between how these businesses operate and what modern technology makes possible is enormous.

ProEdge Operations exists to close that gap. We identify industrial verticals where fragmentation creates compounding inefficiency, then build category defining platforms that become the operating system for an entire trade.

01

Operator Designed

Built by people who understand how industrial businesses actually run, not by technologists projecting assumptions onto the field.
02

Intelligence Native

Every platform embeds AI and predictive analytics at the infrastructure level, not as a feature bolted on after the fact.
03

Network Effects

Designed to connect fragmented ecosystems into multi sided data marketplaces that compound in value as adoption grows.

Currently in active design partnership with HVAC operators in the DFW metroplex. Our platforms are built alongside the operators who will use them, not in isolation.

Portfolio

Active solutions

HVAC Field Service Management
ProEdge Ops
A full stack field service management platform built for independent HVAC contractors running 1 to 5 trucks. Dispatch, quoting, inventory intelligence, CRM, and technician management in a single system priced for the independent operator.
Dispatch Virtual Supply House AI Diagnostics Technician Portal CRM
proedgeops.com
In Development
Aviation Operations
ProEdge Flight Systems
An aviation operations and intelligence platform planned for Part 135 and Part 145 operators. The thesis: flight operations, maintenance tracking, and regulatory compliance are fragmented across the same disconnected workflows that plague field service trades. The same architectural approach that powers ProEdge Ops applies to aviation MRO with equal force.
In Research
Diagnostic

Digital Readiness Scorecard

Five dimensions. Five questions. An honest look at where your operation actually stands on the path to AI readiness. Most industrial businesses overestimate their digital maturity by two full tiers.

Data
Process
Decisions
Workforce
AI Potential
Profile
Dimension 1: Data Infrastructure
Can you answer basic operating questions from a system right now?
Not tomorrow. Not after pulling a report. Right now, from your phone, without calling anyone.
No. Most of what I need lives in people's heads or filing cabinets.
Job history, customer records, equipment details are scattered or paper based.
Some of it. I have spreadsheets and basic software but nothing talks to each other.
Accounting is digital but operations, inventory, or field activity is fragmented.
Mostly. I have a platform that captures the core data but it has gaps.
CRM, scheduling, or job management is digital but not everything flows through.
Yes. Revenue, jobs, inventory, customer history are all in connected systems.
Integrated platform or well connected tools with clean, queryable data.
Dimension 2: Process Digitization
How many of your core workflows run digitally from start to finish?
Think about the journey from a customer request to cash collected. Where does the chain break into phone calls, paper, or manual handoffs?
Almost everything involves a phone call, text message, or piece of paper somewhere.
Dispatch is verbal. Invoices are handwritten or manually entered after the fact.
I have digital islands. Some steps are in software but they do not connect.
QuickBooks for accounting, a separate tool for scheduling, email for everything else.
Most workflows are digital but I still have 2 or 3 manual handoff points.
Parts ordering, certain approvals, or field documentation still break the chain.
Lead to cash runs end to end in connected systems with minimal manual steps.
Booking to dispatch to job completion to invoice to payment is one flow.
Dimension 3: Decision Architecture
How does your operation actually make its most important recurring decisions?
Pricing a job. Routing a crew. Deciding whether to stock a part. Choosing when to follow up. What drives those calls?
Gut feel and experience. The senior people just know.
Pricing is intuition. Scheduling is whoever's closest. Inventory is "we'll order when we run out."
We look at historical data sometimes but mostly react to what is in front of us.
End of month reviews, occasional P&L analysis, but daily decisions are still intuitive.
We have dashboards and reports that inform decisions, though not in real time.
Weekly reporting, KPI tracking, data informs strategy but not minute to minute operations.
Systems surface recommendations and flag anomalies. Data drives daily operations.
Automated alerts, predictive insights, or rules engines are part of how we operate.
Dimension 4: Workforce Readiness
What happens when you introduce a new tool or process to your team?
Not what you hope happens. What actually happened the last time you tried to change how something was done.
High resistance. The last tool rollout was mostly ignored within a month.
Field team reverts to old habits. "I've been doing this 20 years, I don't need an app."
Mixed. A few people adopt quickly. Most need heavy hand holding or mandates.
Younger crew members get it. Veterans push back. Partial adoption is the norm.
Generally willing. The team will use new tools if they clearly make the job easier.
Adoption takes a few weeks with training. Once value is obvious, people stick with it.
The team actively asks for better tools. They flag inefficiencies before I do.
Culture of continuous improvement. People want to work smarter, not just harder.
Dimension 5: AI Leverage Potential
How much of your team's time is spent on work that follows a repeatable pattern?
Generating quotes. Writing follow up emails. Looking up equipment specs. Entering the same data twice. Scheduling the same routes. These are the hours AI reclaims first.
A huge amount. Most of our admin and coordination work is repetitive patterns.
Data entry, status updates, quote generation, scheduling follow ups, document prep.
A fair amount. Back office is repetitive. Field work is more variable.
Invoicing, customer comms, and reporting are patterns. Diagnostics and repair are judgment calls.
Some, but most of what we do requires skilled judgment that is hard to standardize.
Custom engineering, complex diagnostics, highly variable scope.
Very little. Every job is different and requires significant expertise.
Bespoke work, highly specialized, limited repetition.
Your Profile
See your diagnostic results
We will map your scores to a maturity tier and identify the highest leverage opportunities for your operation.

Your full diagnostic is on the way.

A member of the ProEdge team will follow up within 24 hours with a detailed readout and a conversation about what a purpose built digital operating system could look like for your operation.

How We Build

Operator conviction,
institutional execution

We do not build software for categories we do not understand. Every ProEdge platform starts with deep domain immersion and a contrarian thesis on why existing solutions have failed to capture the market.

01

Identify the Structural Gap

Map the value chain, find the point of maximum fragmentation, and size the inefficiency in dollars and days lost.
02

Build with the Operator

Co develop with actual practitioners in the field. Every workflow, every screen, every data model reflects how the work actually gets done.
03

Embed Intelligence from Day One

Architect the data layer first. Predictive analytics, demand forecasting, and pattern recognition are infrastructure decisions, not feature additions.
04

Scale the Network

Connect fragmented participants into a marketplace that creates value for every node as it grows. Turn data into a compounding asset.
Intelligence

Industrial AI in practice

Analysis of how large cap industrials and high growth operators are deploying AI to redesign workflows, not just augment them. Published by the ProEdge Operations research practice.

White Paper
The 80/20 Rule of Industrial AI: Why Technology Delivers Only 20% of the Value
PwC's 2026 AI predictions confirm what McKinsey has observed across hundreds of engagements: technology delivers roughly 20% of an AI initiative's value. The other 80% comes from redesigning work so agents handle routine execution and people focus on what actually drives impact. Most industrial companies invert this ratio, spending 80% of their budget on tools and 20% on workflow redesign. This paper maps the inversion pattern across field service, manufacturing, and distribution, and provides a framework for correcting it before the investment stalls.
88%
of organizations using AI in at least one function
~33%
have begun scaling AI beyond pilots
5.8x
average ROI within 14 months of production deployment
Read the analysis
White Paper
From Copilot to Autopilot: How Siemens, IBM, and Honeywell Are Building Industrial AI Operating Systems
Siemens and NVIDIA are building what they call the Industrial AI Operating System, starting with the first fully AI driven adaptive manufacturing site in Erlangen, Germany. IBM analyzed 1,400 standard operating procedures at a single client and identified over 1,000 workflow improvement opportunities projected to cut operating costs by 25% in 18 months. Honeywell is splitting into three entities with the core remaining company built entirely around an Automation to Autonomy thesis. This paper examines what these moves mean for mid market industrial operators and what the playbook looks like when it scales down from the Fortune 500 to a 15 truck HVAC operation.
40%
of enterprise apps will embed AI agents by end of 2026
25%
operating cost reduction projected from agentic workflow redesign
$301B
global AI spending in 2026
Read the analysis
White Paper
The $9.7 Billion Blind Spot: Why Field Service Management Is the Next AI Battleground
The global field service management market is projected to reach $9.68 billion by 2030, growing at 11.5% CAGR. But 94% of FSM software users are small businesses with 1 to 50 employees, and the dominant platforms were architected for enterprise buyers. McKinsey estimates AI can slash field service content creation costs by 80%, boost operational efficiency by 30%, and automate a quarter of all customer interactions. Two thirds of aftermarket summit attendees are already investing in agentic AI for dispatch, troubleshooting, and parts scoping. This paper maps where the value concentrates for independent operators and why the next category defining platform will be built for the 3 truck contractor, not the 300 truck enterprise.
94%
of FSM users are businesses with 1 to 50 employees
30%
operational efficiency gain from AI in field service
93%
of service orgs have implemented AI in some form
Read the analysis

Building the future of
industrial operations

If you operate in an industrial vertical and believe your category deserves better software, we want to hear from you.

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