THE PROBLEM

Your systems are too old for AI.
Or so you have been told.

Every AI vendor takes one look at your infrastructure and runs. Your data is scattered across legacy systems, spreadsheets, and tools that should have been replaced a decade ago. You have been told you need to modernize everything before AI is even possible. That is not true.

SOUND FAMILIAR?

The usual suspects.

You are not alone. Most companies look like this.

The Ancient ERP

Still running on-prem from 2008

Critical data locked in a system nobody knows how to modify. The one person who understood it retired.

The Spreadsheet Empire

Mission-critical data lives in Excel

No API, no automation, no audit trail. But it works, so nobody touches it.

The Franken-Stack

Six systems that sort of talk to each other

Years of acquisitions and quick fixes. Integration is duct tape and prayers.

The Custom Build

That thing engineering built in 2015

Great at the time. Now it is a liability nobody wants to own.

The Data Swamp

Duplicates, orphans, and contradictions

Same customer, five different records. Nobody knows which is correct.

The SaaS Sprawl

47 cloud tools, zero integration

Everyone bought their own solution. Nothing connects to anything.

THE REALITY

Everyone has this problem.

89%
Companies with "too messy" data for AI
80%
Time spent on data prep vs. AI work
3-6 months
AI projects delayed by data issues
$12,900
Data quality cost per employee/year

The companies winning with AI are not the ones with perfect data. They are the ones who figured out how to work with messy data. Perfect is the enemy of deployed.

You do not need to replace your ERP. You do not need a data lake. You need strategic plumbing that connects what matters and ignores what does not.

OUR APPROACH

Data plumbing first. AI second.

01

Data Archeology

We map your actual data landscape. Not what the architecture diagram says, but what really exists. Every source, every format, every connection.

02

Minimum Viable Plumbing

We identify the smallest set of connections needed for AI to work. Not a complete data overhaul. Just enough infrastructure to get value.

03

Strategic Migration

We move data incrementally, starting with highest-value use cases. The old systems keep running while new infrastructure proves itself.

04

AI Layer on Top

Once data flows cleanly, AI becomes possible. We build the intelligence layer on a foundation that will not collapse.

"Three vendors told us we needed an 18-month data transformation before AI was possible. These folks had us running predictions on our legacy data in 8 weeks. Same messy data. Different approach."

CFO, Manufacturing Company
THE TRUTH

You do not need perfect data.

The big consulting firms will tell you to spend two years cleaning up your data estate before thinking about AI. That is great for their billable hours, but it is not what companies that actually deploy AI do.

Real AI deployments work with real data. They handle missing fields, inconsistent formats, and legacy systems. They build around constraints instead of waiting for a perfect future that never arrives.

We have deployed AI on systems older than some of our clients' employees. It is not about the age of your tech. It is about the quality of the plumbing.

Your tech is not too old.
You just need better plumbing.

The free AI Readiness Audit maps your data landscape and identifies the minimum viable infrastructure for AI. No 18-month transformation required.