AI Assistant for Dispersed Work Protocol Retrieval
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Solution Overview
Problem
Large engineering and manufacturing firms face inefficiencies in accessing and utilizing dispersed technical documentation, requiring manual aggregation and tacit knowledge, which leads to increased training time, design time, and manufacturing errors.
Innovation Solution
A computer-implemented system that stores technical documents in a storage device, trains large language models on this data, and commissions an artificially intelligent assistant to retrieve and provide contextual responses to user prompts, facilitating efficient access and execution of work protocols.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If manual processes are used to access and aggregate technical documentation from multiple sources, then engineers can obtain the required information, but the process requires significant time and tacit knowledge that most employees do not possess
Solution Approach 1:
An AI assistant is introduced as an intermediary between engineers and technical documentation. The AI assistant is trained on company-specific technical documents, policies, and procedures, enabling it to retrieve and synthesize information from multiple sources automatically. This mediator eliminates the need for engineers to manually navigate disparate documentation systems while preserving access to all required technical information.
Solution Approach 2:
The AI assistant undergoes preliminary training on company-specific technical documentation, policies, and procedures before deployment. This preliminary action embeds institutional knowledge into the AI system, enabling it to immediately provide accurate technical information without requiring engineers to invest time in learning internal systems or manually aggregating documentation.
2Reliability
If engineers manually aggregate documentation from multiple programs and sources, then comprehensive technical information can be obtained, but the process requires vast tacit and program knowledge that very few employees possess
Solution Approach 1:
The AI assistant performs self-service by automatically retrieving, aggregating, and synthesizing technical information from multiple documentation sources based on engineer queries. This eliminates the need for engineers to possess specialized knowledge of internal systems, as the AI assistant independently navigates and synthesizes information from all connected documentation repositories.
Solution Approach 2:
The AI assistant is designed with multi-functionality to access and synthesize information from diverse documentation types including technical specifications, policies, procedures, and program-specific documents. This universal access capability allows a single tool to replace multiple specialized knowledge bases, providing comprehensive technical information without requiring engineers to master multiple systems.
3Ease of operation
If manual documentation aggregation processes are used, then engineers can access technical information, but training time and design time are significantly increased
Solution Approach 1:
The manual mechanical process of documentation aggregation is replaced with an automated AI-based system. Instead of engineers physically navigating multiple documentation systems and manually compiling information, the AI assistant automatically retrieves and synthesizes technical information, dramatically reducing the time required for both training and design activities while maintaining ease of access.
4Productivity
If engineers manually access and review technical documentation, then work protocols can be executed, but manufacturing errors increase due to the complexity of the process
Solution Approach 1:
The AI assistant provides feedback by continuously monitoring and verifying technical information against the latest company documentation, policies, and procedures. This feedback mechanism ensures that engineers receive accurate, up-to-date information for work protocol execution, reducing errors caused by relying on outdated or incorrect manual documentation aggregation.
Data Source
AI summary
A computer-implemented method comprises storing technical documents in a storage device. The technical documents include at least work protocols and technical data files tagged with metadata. One or more large language models are trained on at least the work protocols, and text and metadata are extracted from the technical documents. An artificially intelligent assistant is commissioned for the large language models, the artificially intelligent assistant configured to at least retrieve text and metadata in response to receiving a prompt. A digital work environment including an interface for the artificially intelligent assistant is provided. At the artificially intelligent assistant, a prompt related to a first work protocol is received. Text and metadata related to the first work protocol are retrieved via the large language model based on the received prompt. A contextual response is provided via the digital work environment based on the retrieved text and metadata.