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Composable Enterprise

Coforge Forge-X™: AI-Driven Software Engineering and Delivery

AI that reasons about your business, not just your data.

The Problem

 

“We've invested heavily in AI, but the models don't understand how our business works; the same customer, product, or claim is described differently across systems. Data governance programs haven't solved it because they try to standardize the data. Forge-X works differently: it maps the meaning, not the format.”

 

 

 

What Forge-X Does

Semantic Structures
Maps the meaning behind your data, not just its format, creating a shared vocabulary across domains and systems without requiring a master data migration first.
Knowledge and Ontology Layer
A durable enterprise knowledge graph that grounds AI reasoning in your actual business context, policies, products, entities, and their relationships.
AI Decisioning Engine
Connects model outputs to business logic, so AI recommendations are traceable to the data and rules that produced them, not opaque black-box outputs.
Cross-System Integration
Joins siloed systems into a coherent reasoning substrate, incrementally, without a full data rearchitecture as a prerequisite.

How Forge-X Makes an Impact

 

AI investments that previously produced generic outputs start producing decisions grounded in how your business actually works, reducing hallucination, increasing trust, and making AI recommendations actionable rather than merely interesting.

Why Forge-X Is Different

 

Prior data governance programs tried to standardize source data, a multi-year effort. Forge-X layers semantic understanding on top of existing systems, delivering AI context without waiting for data unification to complete.

 

 

 

How To Tell if Forge-X is the Right Solution

AI outputs lack business context
Models produce technically correct outputs that operations teams can't act on because the domain context is missing from the reasoning.
Prior MDM or data governance stalled
Data unification programs have failed or are years away, but AI needs to work now, on existing systems.
Low AI adoption post-pilot
Pilots look promising in demos but stall in production because real data complexity breaks the model's reasoning.