Modelez2AI-Core classifies and connects the relationships among departments, tasks, documents, and owners across documents, work records, and system data. The standalone model can be embedded in your products, internal systems, or air-gapped networks.
Organize your terminology, data structure, and workflows so the model can understand them.
Check results against real work scenarios to reduce incorrect connections and omissions.
When needed, run the model on internal servers or in a closed network without external connections.
Need enterprise AI in your own product
Model
Deploy Model
Purpose
Build an enterprise AI product
Replacing the existing system is difficult
Model
Integrate Model
Purpose
Strengthen data analysis and classification
External cloud use is not allowed
Model
Build on internal servers
Purpose
Operate AI independently in a closed network
AI decisions need to be governed
Model
Use your own execution environment
Purpose
Manage decision criteria and validation history
Classify production, equipment, and quality data to spot anomalies faster.
Connect contract and transaction data to find what needs review.
Classify public requests and route them to the right team.
Process sensitive data safely in environments separated from external networks.
Organize clinical and research materials so teams can find what they need.
Review key clauses and risk factors in contracts and legal documents.
Connect partner, product, and supply-chain data to understand the flow.
Analyze old system data structures and identify ways to improve them.
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ez2AI-Core does not replace general-purpose AI such as ChatGPT. It focuses on accurately classifying and connecting your systems, documents, and work data. Combined with a public LLM, it can deliver both reliable enterprise data and natural conversations.
Accredited test report
100%
Recall
Not a single relationship that should have been found was missed.
98.53%
Precision
The share of proposed relationships that matched the answer key.
In this test ez2AI-Core missed nothing the answer key listed (100% recall), and on top of that it flagged one relationship the answer key did not contain. Under the test rules that single item counted as a false positive, which is why precision reads 98.53%.
Out in the field, though, there is no answer key. In a system 15 to 20 years old the foreign keys were never declared and the people who knew the relationships have long since left. What you need there is not the ability to confirm relationships someone wrote down — it is the ability to surface the ones nobody ever did.
Flagging something outside the answer key means the model did not memorise a fixed answer; it read the structure of the data itself. We consider that single item the most important part of this test, so we publish both figures rather than hiding one.
People decide. Relationships the model proposes come with their supporting evidence and are presented for review, and your team confirms them. In legacy analysis, surfacing more and reviewing is safer than missing something.
All sensitive enterprise data — customer information, work data, operational DBs and more — is completely excluded from AI neural network training and rigorously protected.
Even with the same AI model and execution environment, accuracy can vary greatly depending on data quality and structure. ez2AI-Core recorded a 34.6-point performance gap in an evaluation based on its proprietary dataset.
AI performance is not determined by the model alone. The quality of the result depends on how accurately field knowledge is accumulated and structured. Industrial and operational data like this cannot be secured quickly through capital and manpower alone.