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Guided Artificial Intelligence

(Subset of Agentic AI)

A class of Artificial Intelligence that fuses system capabilities with human skills

Artificial Intelligence includes digital systems for performing tasks that require human intelligence. It spans broad human cognitive functionalities and core concepts, e.g., learning, recognition, reasoning, problem solving, perception, and adaptability.

Categorizing types of AI helps understanding its value. For instance, Generative AI creates contents, and Agentic AI takes actions. They offer distinctive and discrete value propositions, and that allows recognizing their contributions deeply and separately.

An important and highly-valued subset is Guided AI (gAI), which in fact can be viewed as nonautonomous Agentic AI. It establishes human and AI collaboration methodically and productively, and delivers a paradigm that is uniquely practical with consequential results.

Certain class of challenging problems requisites advanced analytical solutions. These are highly-sophisticated, well-thought through analytical models that apply complex algorithms. That’s the core of gAI.

The gAI process

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1.

Inputs are identified, and the user provides that from various enterprise systems.

2.

Data is submitted to the gAI agent, that ensures the integrity, accuracy, and completeness of all required data. This step fuses both human-in-the-loop and appropriate degree of automation, leading to input verification.

3.

aAI agent is then executed. While human initiates this step, gAI runs autonomously, and the output is produced.

4.

Human then conducts the output review, ensuring fullness, correctness, and accuracy of conclusions.

5.

Results (outputs) are then submitted to the user.

Significant advantages of gAI

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1.

Ease of deployment process for the user, as the gAI provider does the heavy-lifting of this novel framework.

2.

The user can focus on required inputs that are typically available, but may reside disjointedly on various enterprise systems in different places.

3.

The user avoids investing significantly on AI systems (hardware and software) that are not only costly, but require continued upgrade, integration, support, and maintenance.

4.

Staff training, both initial and periodic, that can significantly add to expenses. Staff turnover also creates challenges for the organization, and adds to the risk of incorporating AI in business processes.

5.

The business model is simple: pay per use. This creates significant opportunity for the user to experience the benefits of AI while not spending a fortune in bringing in AI.

gAI eliminates much of the headache in introducing and operating AI systems for cases that highly-advanced analytical techniques are required for solving exceptionally-difficult problems.

Please contact us for further information regarding our gAI practice.

Our AEMPS and M24R technologies offer gAI structure,
hence lowering costs of using this AI tool while providing
the exceptional benefits that they provides.

Advanced Engine Maintenance

Planning Systems (AEMPS)

Materials Management for

Repairable Parts (M24R)

	1.	Inputs are identified, and the user provides that from various enterprise systems.

	2.	Data is submitted to the gAI agent, that ensures the integrity, accuracy, and completeness of all required data. This step fuses both human-in-the-loop and appropriate degree of automation, leading to input verification.

	3.	aAI agent is then executed. While human initiates this step, gAI runs autonomously, and the output is produced.

	4.	Human then conducts the output review, ensuring fullness, correctness, and accuracy of conclusions.

	5.	Results (outputs) are then submitted to the user.

	1.	Ease of deployment process for the user, as the gAI provider does the heavy-lifting of this novel framework.

	2.	The user can focus on required inputs that are typically available, but may reside disjointedly on various enterprise systems in different places.

	3.	The user avoids investing significantly on AI systems (hardware and software) that are not only costly, but require continued upgrade, integration, support, and maintenance.

	4.	Staff training, both initial and periodic, that can significantly add to expenses. Staff turnover also creates challenges for the organization, and adds to the risk of incorporating AI in business processes.

	5.	The business model is simple: pay per use. This creates significant opportunity for the user to experience the benefits of AI while not spending a fortune in bringing in AI.

gAI eliminates much of the headache in introducing and operating AI systems for cases that highly-advanced analytical techniques are required for solving exceptionally-difficult problems.

Please contact us for further information regarding our gAI practice.