Manufacturing
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What do you get?
Generative AI working for your organization innovation
What are we going to do for you?
Enhance your manufacturing with our tailored generative AI solutions. Boost revenue, optimize supply chains, and enhance customer engagement effortlessly.
Challenge
To remain competitive, the manufacturing sector navigates multifaceted challenges spanning product design, supply chain optimization, process efficiency, maintenance predictability, quality assurance, customization demands, energy management, and human-robot collaboration, each demanding innovative solutions for sustained success.
Solution
OmniData's bespoke Generative AI distinguishes itself by not only analyzing existing data but also by actively generating new data and solutions based on learned patterns. This unique capability enables you to optimize product designs, forecast demand, and enhance manufacturing processes in ways that traditional analytics cannot match.
Knowledge Discovery:
Explore or capture the essence of cumulative years of experience documented in unstructured repositories with an easy-to-use web interface.
Prescriptive Actions
Use OmniData's Generative AI capabilities to prescribe actions and suggested improvement opportunities based on client specific processes.
Automated Analysis & Business Processes
Generate automated analyses and integration to current business processes.
Business Problem
A wealth of information can be found in company document repositories written by subject matter experts over time. Having years of experience captured in unstructured text in such a way has many benefits if it can be unlocked.
The challenge lies in making sense of this disparate data scattered across various platforms, such as Sharepoint repositories, and providing employees with a user-friendly interface to access and extract valuable insights. The inability to efficiently navigate and utilize this wealth of information hampers productivity, decision-making, and innovation within the organization, hindering competitiveness and growth potential in an increasingly data-driven marketplace.
Solution Approach
Generative AI offers solutions to some of these challenges. The advent of Generative AI has amongst other things enabled users to interact with their data using natural language. Technology advances in cloud solutions such as Azure OpenAI have given companies access to the most powerful LLMs available today.
These models liberated the data captured in repositories by SMEs over time, no matter the strcuture of these sources. Added to this is the abilty to combine these sources with other documents in a way that provides relvant content to the user. Cateloging the information to be retrievable based on indexed criteria provides fast feedback. Key word extraction and document understanding provides context while questioning and answering is facilitated with a web based interface. Content summarization is seamless and semantic search by understanding the meaning of the query provides superior responses.
Value Impact
By enabling employees to easily access and interact with vast amounts of unstructured data, Generative AI transforms information retrieval from a cumbersome and time-consuming process into a seamless and intuitive experience.
Providing employees with an AI assistant on their documents also created efficiencies in upskilling staff, and reduced employee turnover by relieving them of mundane tasks.
Technical Implementation: Data Sources
The data sources for this use case comprised of many different types of document files. Website content and internal document repositories were extracted into HTML files, while others still came in as PDFs. All of these files were however analyzed and converted to semi-structured files such as JSON for further analysis.
Technical Implementation: Solution Features
- Convert unstructured documents into semi-structured documents by extracting information into key-value pairs
- Catelog the different documents and content into indexes for faster retreival of information
- OmniData applied the LLMs available for use in Azure to build an interface with the data that is user-friendly and which can be adjusted to provide better answers
Conclusion
These results enabled the client to interact with their document repositories in ways not available before. It provided a solution to analysing massive amounts of unstructured data in a structured and natural way. The soltution also provided the client with option to further enhance current customer relationship management processes through automating certain tasks.