Most mid-market companies are stuck between AI hype and operational reality.
Consultants hand you a plan and leave. Software vendors fix one narrow problem. A full-time AI leader is hard to justify at your size.
01
Nobody owns it.
Scattered ChatGPT use and half-finished experiments, with no one accountable for what AI actually changes.
02
The numbers live in five places.
The ERP, accounting, the CRM, and the spreadsheets that really run things. None of them talk to each other.
03
Your best people do busywork.
Re-keying orders, chasing status, and building reports by hand, instead of the work only they can do.
04
Leadership can’t ask the real questions.
Margin, capacity, risk, missed revenue, the answers exist, but cross-system, so nobody pulls them.
95%
of organizations are getting zero return from generative AI.
2×
Companies that bring in outside specialists get AI into production about twice as often as those that build it alone.
Source: MIT NANDA, The GenAI Divide: State of AI in Business 2025
How we work
A new model of engineering, product management, and consulting, built with AI from the ground up.
Operators who’ve run companies and engineers who build with AI every day, on one small team.
Strategy01
Where AI pays off in your business.
We surface the problems worth solving with AI — the ones where your data and institutional knowledge give you an advantage nobody can copy.
How the business actually runs, mapped
Opportunities ranked by money and effort
What to build, what to buy, what to leave alone
A plan leadership agrees on
Build02
One source of truth, then agents.
We don’t replace your systems. We connect them, then put AI to work on top.
ERP, accounting, and CRM connected
Reporting leadership can trust
AI that works inside approval rules
Internal tools and dashboards
Ownership03
Systems your team owns.
If we keep working together, it should be because there’s another problem worth solving, not because you can’t manage without us.
Business rules written down
Everything documented and handed over
Your team trained on what we built
Kept running after launch
Built on our own AI delivery system. It lets us understand your business, shape solutions, and build faster, and it makes the process repeatable across projects.
Where we’ve done the work
Work across private equity, medical device distribution, food production, and healthcare.
Our engagements are confidential, so we describe the work, not the client.
Medical device distributionPrivate equity-backed
Rebuilt the data foundation, then put AI to work on it.
Problem
Sales data scattered across the ERP and spreadsheets. Every report built by hand.
Foundation
Mapped the business end to end. Rebuilt sales reporting from source. Every number ties to the books.
AI layer
An AI order monitor so no order slips through, and rep-by-rep sales views for leadership.
Private equityHealthcare investment firm, built with the deal team
Turned hundreds of calls into a live deal pipeline.
Problem
Deal flow buried in recorded calls and inboxes.
Foundation
Every call transcribed. Every person and firm resolved into one registry.
AI layer
Scores each call, pulls out the deals, and tracks every one from the inbox.
Food productionMulti-location operator
Turned handwritten kitchen logs into food-cost data.
Problem
Production and waste tracked on paper. Food cost visible only in aggregate.
Foundation
Paper logs converted to item-level data in the company’s data platform.
AI layer
AI reads the handwriting. A person checks anything it isn’t sure of.
HealthcareVenture-backed
An AI operating model for a fast-growing company.
Problem
AI adoption stuck with a few power users.
Diagnosis
Mapped where AI was working, where it stalled, and why.
Plan
Focused agents that share context safely, clear owners, and the first one to build.
Client references are available once there’s a fit.
About
Our mission is to bring the benefits of AI to the businesses the tech industry overlooks.
Island Range is a founder-led implementation practice. We work directly with leaders and operators to find the valuable workflow, design the right intervention, and build it into the way the business actually runs.
One continuous line of responsibility
Business strategy
Workflow discovery
Product design
AI-native implementation
Jordan grew up in Montana, spent childhood summers working his family’s farm in Iowa, and spent a decade building software for agriculture. The businesses the economy runs on operate well outside the technology bubble, and they remain underserved by it.
Jordan Taylor
Product operator and builder
Founder, Island Range
Cofounder & Chief Product Officer, Seso
Scaled the company from zero to $20M+ ARR, leading a 31-person product, engineering, and design organization. More than a third of the 100 largest U.S. agricultural employers became customers.
Director of Product, Farmers Business Network
Joined at 35 employees and led product as the company grew to more than 700. Designed and shipped the agriculture inputs ecommerce platform that grew to more than $100 million in annual gross purchases.
Product manager, Dropbox
Developed products used inside the everyday work of organizations.
Fixed Income Research Associate, RBC Capital Markets
Worked with proprietary trading desks and wrote fixed income strategy research cited by Reuters and The Wall Street Journal.
Current implementation experience
Engagements span investment firms, healthcare, and food supply, with a focus on document-heavy, judgment-intensive operations.
This transformation isn’t inevitable. It takes deliberate action.
Tell us what you’d fix first: a leak in the margin, a process that eats your team’s week, a number you can’t get without three spreadsheets. We’ll tell you straight whether AI is the answer and what it would take.