You Have the Data, All of It
The job number, the machine log, the inspection result, the maintenance note, the five versions of the same spreadsheet.
On‑premise predictive AI · nothing leaves your building
You Already Own the Answer, You Just Can't Ask the Question
Watch: why your data can't answer a simple question - and the 3 steps that fix it. 8 min.
Find Out What Your Data Can Predict
Twenty years of your operational data is sitting in exports, machine logs, spreadsheets, and a filing cabinet - and none of it connects. Cogentic builds the foundation that joins it, then runs predictions on top. On your hardware. Run by your people. With nothing ever leaving your network.
Tell us one thing you'd want predicted - a machine failure, a no‑show, a stockout - and we'll tell you straight whether your data can answer it. Free. No call required to start.
Prefer to talk to an engineer first? Book a 20‑minute call. No sales layer - you'll speak to someone who builds the product.
In production at a Fortune 100 manufacturer, on an air‑gapped network◆
Founding engineers, Georgia Tech, CS and Machine Learning◆
Your data never leaves your building◆
- In production at a Fortune 100 manufacturer, on an air‑gapped network
- Founding engineers, Georgia Tech, CS and Machine Learning
- Your data never leaves your building
If You've Ever Said "Our Data Can't Leave the Building," Every Tool on the Market Was Built for Someone Else
You Can't Correlate Anything With Anything
But ask it a normal question
Do we scrap more on this alloy, on this machine, on second shift, right after a tool change?
And you can't get an answer. The shift is in one file, the tool change in another, the scrap in a third, and nothing connects them. Which is why you can't predict anything either.
They Sell the Last Mile and Skip the First Ten
And every AI platform, every no‑code tool, every chatbot assumes you're already past this - that your data showed up clean, centralized, and joined. It never does.
If you don't run a shop:
- the ERPis your practice management system or your case system,
- the scrapis the no‑show or the write‑off,
- and the veteranis the person whose knowledge walks out the door when they retire.
Same structure, different building.
We Do the Part Everyone Else Skips, Then the Part Everyone Else Sells
Michelangelo said the statue was already inside the marble; his job was to remove everything that wasn't it. Your prediction is already inside your history. Ours is to clear away everything that isn't, and connect what's left. It happens in three parts.
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01
We Make the Data Exist
Exports out of your ERP, logging set up at the source, paper records digitized. Whatever format it's in, wherever it lives - we go and get it, and turn it into one thing.
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02
We Build the Foundation Underneath It
Our engineers build the ETL pipelines and design a data model around your operation - your naming, your process, your edge cases - to the industry standard called medallion architecture:
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Bronze
Everything centralized in one place, exactly as it is today.
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Silver
Cleaned, deduplicated, and validated, with every fix versioned so an auditor can trace it.
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Gold
Structured tables built around the questions you actually ask.
Running AutoML on scattered data is a Ferrari with no road. Everyone else sells the car. We build the road first.
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Bronze
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03
The Software Runs on Top, on Your Hardware
Cogentic runs on a machine inside your building. Offline. Air‑gap capable. Your own people run it - no code, no data scientist - in four steps: upload, profile, train, predict. It trains competing models, promotes the winner automatically, and hands back a ranked answer with a confidence score. Every model traces to a hashed, re‑runnable plan you can hand to an auditor.
Proven Where It's Hardest
Nothing In, Nothing Out
Cogentic is in production at a Fortune 100 manufacturer, on a fully air‑gapped network - no internet, nothing in, nothing out. Software can't fake its way through that environment: one outbound call and the deployment physically fails. When we say your data never leaves the building, a Fortune 100 security team has already verified it the hard way.
First Spreadsheet to a Validated Model in Three Weeks
At that deployment we went from the client's first spreadsheet to a validated model in three weeks. Their own engineer re‑runs it weekly now - without us.
Who Builds It
Both founders are engineers - Georgia Tech, CS and Machine Learning - deploying with a bench of five more engineers out of safety‑critical manufacturing. This buyer treats who built it as the evidence. Every account has a direct line to the engineers who build the product - no support‑ticket wall.
A Capability, Not a Report
A consultant leaves a report and a bill. A data scientist leaves in eighteen months and takes the capability with them. We leave a working function - for a fraction of the cost of one data‑science salary.
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01
Yours, Permanently
The pipelines, the data model, the structured data, and the models are yours, permanently.
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02
Without Us
Re‑run them next week, next year, on new data - without us.
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03
On Your Hardware
If we vanished tomorrow, your system keeps running. It's on your hardware.
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04
In the Institution
The knowledge stops living in one veteran's head and starts living in the institution.
The Four Things People Say Before They Start
Questions We Get Before the First Call
No. On‑premise by default, offline and air‑gap capable. That's structural, not a policy - the Fortune 100 deployment runs with zero outbound connections.
No. Your own operators run it in four steps - upload, profile, train, predict. No code. We train your team in the handover.
Any tabular data - CSV, Excel, Parquet, multi‑GB files. ERP exports, machine logs, inspection records, appointment histories, invoices. If you can put it in rows and columns, it's a candidate.
Whatever your operation leaks on: what will fail, what to reorder before it runs out or expires, which output will be defective, who won't show up, which quote will close, what to run first on which machine.
Engagements start with an exploratory phase at a fixed price, quoted after a scoping call and sized to your operation, credited in full toward any plan if you go forward. Ongoing plans scale from there by number of sites and data sources. The exploratory phase ends with a fixed implementation quote based on your actual data - not an estimate.
The exploratory phase runs 30–45 days and ends with one working model on your real data. For reference, the Fortune 100 build went from first spreadsheet to a validated model in three weeks.
You keep the blueprint. The audit, the readiness score, the data‑model blueprint, the roadmap - all yours, whether you proceed or not.
Stop Guessing, You Already Own the Answer
Tell us one thing you'd want predicted. We'll look at your data and tell you straight whether it can answer it - free, before you commit to anything.
Find Out What Your Data Can Predict
Takes a few minutes. No call required to start. If your data can answer the question, we'll show you how.
Ready to talk instead? Book a 20‑minute call with an engineer - not a salesperson.
We cap implementations per quarter to protect delivery. First come, first scheduled - when a quarter fills, the next start is months out.