Why Standard AI Models Can't Solve Industrial Knowledge Preservation
The technical challenges requiring specialized approaches
The Technical Gap in AI for Industrial Expertise
The standard LLM always give you an answer, whether it is right or wrong. In the industrial setting, there is no room for experiment; the answer must be correct.
Frequently asked questions
Computational and Technical Challenges
To create a 24/7 deep expert system, the computing resources need to be effectively distributed. The expert should be able to use their model on their laptop and generate it in a short timeframe by tapping into cloud computing
Scale
Processing industrial knowledge requires significant computing power
Validation
Resource-intensive verification processes and multi-model multi AI validation
Knowledge Integration
Combining text, visuals, voice and procedural knowledge
Real-time Expertise
Balancing depth with response time, creating truly conversational models that are proactive, not reactive
Enterprise-Grade Identity and Security Requirements
It is critical to verify that the expert is indeed an expert and confirm their degree and certifications. It is also important that the knowledge encoded into models remains the property of the experts or company; the delineation of access to answers is paramount.
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Stringent access control for industrial knowledge
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Enterprise identity integration requirements
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Multi-level permissions reflecting organizational structure
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Certification verification
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Secure knowledge exchange protocols
The Opportunity
Imagine talking to someone who has run LNG for 30 years and knows every possible failure mode, from personnel to equipment. If you are implementing a digital system and are not aware of these, you end up with a system that doesn’t work. This knowledge is increasingly hard to find, especially in regions like Asia and the Middle East. Our mission is to preserve this expertise using MaveriX platform so that this knowledge is available to guide the successful projects.
