Your AI project didn't fail because AI doesn't work.
It failed because your team never learned to fight for data. We spent years in environments far more hostile than your factory floor. We can make AI work with what you have.
AI isn't broken. Your data was.
You tried AI. Or you watched someone else try. It flopped. Now the verdict is in: "AI doesn't work for us."
But that's not what happened.
What happened is you hired a team trained on clean data. They learned in labs where datasets behaved. They'd never worked outside a clean dataset in their lives.
Then they hit your world. Twelve systems that don't talk. An ERP held together with spreadsheets and prayers. Legacy machines with no documentation because the vendor went bankrupt years ago.
They didn't know what to do.
So they offered two options: rip everything out and start over, or bolt AI onto the mess and hope. You picked one. It failed. And AI took the blame.
Every month your systems don't talk, you're paying a tax you can't see. Manual workarounds. Duplicate entry. Decisions made on yesterday's numbers. For most manufacturers, it's six figures a year. Yours might be higher.
AI wasn't the problem. Your data was.
We learned to capture data that doesn't want to be captured.
We've worked with manufacturers for years. Growth and marketing. Operations and analytics. That's where we built the muscle.
Tracking performance across platforms that refuse to share. Stitching journeys when every system blocks visibility. Pulling signal from environments designed to resist measurement.
When a cash register company went bankrupt, it left thousands of restaurant and manufacturing locations with orphaned hardware. No documentation. No support. Proprietary protocol nobody could read.
The "right" answer was years of hardware replacement. We didn't accept that.
We reverse-engineered the network protocol and pulled the data out ourselves. Then we built a data warehouse that unified everything: thousands of QSR locations plus the manufacturing facility feeding them.
Three analysts. Full visibility. For the first time, leadership could see the whole picture.
They used it to offload an underperforming brand, acquire two with better economics, and take the company public.
Your factory floor has messy data. We've seen worse. And we know what happens when you finally connect it.
Data doesn't just report. It decides.
Which brand to keep. Which to cut. When to expand. When to hold.
These aren't spreadsheet questions. They're strategic questions that only get answered when you can finally see the whole picture.
Most manufacturers are flying blind. Not because they lack data. Because their data is trapped in systems that don't talk to each other.
We connect them. Then you can see. Then you can move.
Why we succeed where AI teams stall.
AI teams optimize models. That's their training. That's their hammer. So everything looks like a nail.
When your data is fragmented, they either ignore the problem or ask you to spend eighteen months replacing everything. They don't know how to stitch hostile systems together because they've never had to.
We do the work they skip. We connect your systems. We clean the data. We build the foundation that makes AI actually function.
No 18-month ERP replacement. No "turn everything off and pray." Your systems keep running while we wire them together.
Once the foundation is solid, AI stops being a science project. It becomes a tool that works.
Simple. Clear. No surprises.
Discovery
We map your data landscape. What you have, where it lives, what's broken, what's bleeding money. You get a clear picture and a prioritized fix list.
Implementation
We fix the foundation. Integration, cleanup, pipelines, dashboards. Phased so you see progress, not just invoices.
Ongoing Support
Optional retainer for optimization, new integrations, and AI enablement as you grow.
See where you actually stand.
AI Readiness Scorecard
Think you're ready for AI? Find out. 10 questions. 5 minutes. A clear picture of whether your data can support AI — or whether you're about to waste six figures learning the hard way.
Take the ScorecardLegacy Cost Calculator
What is your data mess actually costing you? Manual workarounds. Duplicate entry. Decisions made on stale numbers. Most manufacturers don't know the real number. This calculator shows you.
Calculate Your CostWho we are.
Five people. Combined 50+ years in data systems. No account managers. No handoffs. When you work with us, you work with us.
We've spent years with manufacturers on both sides of the house. Growth and marketing. Operations and analytics. We've seen the data chaos from every angle.
Both cofounders are qualified engineers with deep project management experience. One spent five years on factory floors, wrestling legacy systems into submission. The other spent 25 years building data infrastructure for enterprises. We started this company because we kept watching AI projects fail for the same reason: teams that had never learned to capture difficult data. We have.
Wynne
New Zealand
Cofounder. PMP-certified. Masters in Chemical, Materials, and Process Engineering. Five years in manufacturing operations. Spent years making sense of data that lived in fifteen different places. Now helps others do the same.
Alex
Toronto / Mexico
Cofounder. Professional Engineer with 25+ years building data systems for major enterprises. Once reverse-engineered a proprietary protocol to rescue data from thousands of locations when the vendor disappeared. When there's no playbook, he writes one.
Sunni
California
Coder, analyst, project driver. Makes sure things actually get done.
Saurabh
India
Senior ML and data engineer. 10+ years turning messy data into working systems.
Shalaka
India
ML engineer, data analyst. Builds the pipelines that make AI actually function.
Let's see if we can help.
Fifteen minutes. No pitch.
We'll ask about your systems, your data situation, and what you've already tried. You'll know within ten minutes if we can help. If we can't, we'll point you toward someone who can.