Glages - Experience Capitalization

Experience Capitalization

Glages

Glages Model Factory Project

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Glages Model Factory Project

Glages is a formal-model production technology for creating reusable, machine-checkable models from accumulated knowledge and source material. Glages develops a different class of computational model: explicit formal models whose stable meaning can be inspected, verified, reused, composed, and specialized independently of the probabilistic model that may help produce

Glages: Scientific and Practical Foundations

Glages: Scientific and Practical Foundations

Glages begins with a strong proposition: reliable computational activity should not depend on a probabilistic system reconstructing stable operational meaning during every execution. That proposition requires evidence. It would be weak to justify Glages only by saying that current agents sometimes make mistakes, or that formal models appear more orderly

Glages: A Different Kind of AI Model

Glages: A Different Kind of AI Model

Artificial intelligence is currently discussed as though the word model has one obvious meaning. A model is assumed to be a large learned neural network. It is trained on enormous quantities of data, stores statistical structure in its parameters, receives an input, and produces a prediction, continuation, plan, answer, or

Glages: What the Factory Is and Is Not

Glages: What the Factory Is and Is Not

New technologies are often described through familiar software categories. Glages may be compared with agent frameworks, workflow engines, rules systems, ontologies, knowledge graphs, retrieval products, and evaluation tools because each of them touches part of the problem. The comparison is useful only after the main category is clear. Glages is

Glages: Why the Model Factory Is Difficult to Replicate

Glages: Why the Model Factory Is Difficult to Replicate

The basic idea behind Glages is easy to understand: turn accumulated knowledge and source material into reusable formal, machine-checkable models. That idea by itself is not the competitive advantage. The advantage comes from building a production system that can create, verify, compose, specialize, transfer, correct, and reuse those models

Glages: Models for Autonomous and Robotic Systems

Glages: Models for Autonomous and Robotic Systems

Autonomous systems face a different problem from ordinary software. A conventional application can stop and ask a user for more information. A robotic system, drone, mobile machine, or autonomous device may have to act inside a changing physical environment with limited time, limited computation, incomplete observations, and real consequences. The

Glages: Models for Edge and Constrained Systems

Glages: Models for Edge and Constrained Systems

Many AI systems assume that intelligence can be called whenever it is needed. That assumption breaks down at the edge. A device may have a weak processor, limited memory, intermittent connectivity, strict latency requirements, high inference cost, or no permitted connection to a frontier model at all. The standard answer

Glages: Models for Formal Verification and Certification

Glages: Models for Formal Verification and Certification

A formal model does not have to execute anything in order to be valuable. It can serve as the thing against which another system is checked. This creates a very different application of Glages from AI automation, robotics, or runtime execution. Glages is a formal-model production technology for creating

Glages: What Organizations Can Build on the Models

Glages: What Organizations Can Build on the Models

A Glages model is not a finished application for one market. It is a reusable software foundation that organizations can use to build several kinds of software systems and automation services. The organization remains responsible for its market, customers, interface, integrations, deployment, and support. Glages supplies the formal process or

Glages: Business Results Are Part of the Model

Glages: Business Results Are Part of the Model

When software does not perform the requested action, people often describe the event as a failure. That word is too broad for controlled automation. A payment decline, blocked refund, missing evidence, or unsupported request may be a valid business result of a correctly executed process. The model must distinguish these

Glages: The Agent Is a Replaceable Mechanism

Glages: The Agent Is a Replaceable Mechanism

AI products are often designed around the current capabilities of a particular model. The prompts, tools, memory, and orchestration become the center of the architecture. Glages places the formal business model at the center instead. The agent becomes one execution mechanism among several. The Activity Must Outlast the Agent A

Glages: Models Become a Growing Capital Asset

Glages: Models Become a Growing Capital Asset

Most project knowledge loses value after delivery. It remains inside implementation code, scattered documents, local system behavior, and the memories of the people who built the system. Glages is designed so that completed formalization work can become a reusable production asset rather than disappearing into one implementation. The asset is