The promise of AI in industry is real. 

But why aren’t manufacturing plants producing more, with less – and doing so more safely? What is preventing the benefits of “digital transformation” from translating to shareholder value? 

AI in industry remains stuck in the hype phase – not because the technology is flawed, but because the data foundation is missing. 

You cannot build AI if you do not know how your assets connect

Trust us, we tried. 

cerebre was founded in 2019 to bring AI to life at scale across complex continuous manufacturing environments. We built over 400 advanced models across refining, chemical, and utility plants. The models worked and were technically impressive. But each came with a hidden cost: manually collected context of the plant configuration – what equipment is where, how equipment is connected, and how data correlates to that physical context.

For example, you cannot model the failure of a pump, compressor, or valve in isolation. You need to understand how equipment interacts within the context of all other equipment and production itself. Where equipment is within the system, what is upstream and downstream, and how failures propagate throughout production. “Digital twins” miss this context.

Imagine analyzing traffic without Google Maps. This is the reality for industrial facilities today: siloed systems and isolated data, disconnected from the physical reality of the plant.

Manufacturing companies have endless amounts of data yet lack a sustainable, structured data model that governs what equipment exists, where it is located, and how it connects to everything else.

Without this data foundation, AI in industry will remain bespoke, reactive, and underwhelming. 

So how do we create and sustain the missing data foundation? 

The source is hiding in plain sight – your engineering drawings

Not as images, but as the missing context of equipment relationships. P&IDs, isometrics, and single-line diagrams contain the plant’s DNA. The opportunity to enable AI lies in extracting this DNA and transforming it into usable data. 

That’s where a PlantGraph comes in. 

A PlantGraph captures the physical relationships, system boundaries, and flow paths of a plant – at scale. 

With a PlantGraph, your equipment and data become searchable through the lens of the plant, just like you expect from Google Maps. 

You can ask:

“Show me all relief valves downstream of this vessel.” 
“Which systems are impacted if this pump fails?” 
“Where has this failure mode occurred before?” 
“Show me a double block and bleed configuration upstream of this exchanger.” 
“What equipment is the root cause of this alarm or process deviation?” 

Beyond search and visualization, a PlantGraph enables you to codify tribal knowledge, standardize key performance indicators, automate operating procedures, and power AI with real-world context. 

This is the foundation for intelligent operations – intelligence built on a trusted understanding of your plant’s configuration. 

Once this infrastructure is in place, you can reimagine how your organization leverages data to make smarter decisions across your value chain. 

A new way to scale digital

Consider this:

Regardless of your organization’s size or the strength of your vendor relationships, you are likely not fully capitalizing on your investments. Why?

Each new model, platform, tool, etc., creates more derivative, unsustainable data – and more silos.

A PlantGraph solves this. It provides the structure for continuous enrichment and consumption of your enterprise knowledge. With this foundation, you can ensure the value of your data compounds to power your AI.

Avoid the noise. Start here.

1. Generate data that reflects your plant. Convert your CAD drawings into high-fidelity, structured data. Connect your data to your MOC process to maintain your source of truth. 

2. Build and connect your PlantGraph. Use dynamic hierarchies to create the relevant view of your equipment. Manage your ontology at scale to connect your data and procedures. Ensure open operability across your digital ecosystem. 

3. Centralize consumption and enrichment. Use your PlantGraph to drive decisions across different tools, analytics, and users. Feed insights back into your data model. Make this knowledge accessible to future models, applications, and people. Repeat. 

4. Avoid lock-in. Stay modular. Stay flexible. Get your data to where it needs to go. 

5. Own your data and your intelligence. Control the keys to your AI future. 

cerebre helps industrial companies unlock trapped facility data, build PlantGraphs, and scale AI with real context.