Application Of AI In Fintech Insurance Management
January 12, 2024Generative AI in Recruitment Automation
February 15, 2024Modernize your Data Infrastructure and reap the benefits of GenAI 🚀
Generative AI applications can readily be built against the documents, emails, meeting transcripts, and other content that knowledge workers produce as a matter of course.
- CIO.com
🤔 Challenges of ever growing data
- Majority of companies estimate that about 75% of their data is unstructured [1].
- They see data volume grow by an average of almost 65% every month in their companies [2].
- Growing data presents the challenge of ingesting and utilizing the data, and over 60% of companies are limited by the volume of data and technology to harness their data fully [3].
❌ Missed opportunities due to lack of Data Infrastructure
Let’s take an example of a manufacturing wing of a large company. This company creates lots of textual documents related to R&D, sale and purchase of equipment, regulatory documents, financial statements, employee safety guidelines, etc. in the scale of thousands which amount to GBs of unstructured data. The manufacturing wing could also be creating more data in terms of educational videos, equipment monitoring logs either in video or log files, etc. which are much larger in the scale of TBs and are not manageable via traditional data infrastructure.
📉 Limitations of out-of-the-box LLM like GPT
While GPT products like ChatGPT show unlimited potential when it comes to generating answers to general queries, its training data is static, it lacks organization specific data and it cannot explain its results. This is where we can leverage RAG, a technology which improves upon GPT like Large Language Models by utilizing specific data and context. You can simply understand RAG by breaking it down into Retrieval And Generation.
📊 Understanding Retrieval Augmented Generation
Retrieval Augmented Generation uses the embeddings created by a Large Language Model to embed the context of company specific documents into a language that an LLM can understand. This becomes a context for your company specific queries and the LLM can now answer with appropriate knowledge and show where it retrieved the answer from. Take an example of a Data Analyst at a company, sorting through millions of documents to find past decisions and business metrics which is inefficient with traditional approaches. But with a RAG workflow, they could directly ask client application their questions, and it would sort through the millions of document via a Vector Database and find relevant, truthful and verified answers.
🔍 Executive Summary
- Companies lack the data infrastructure to handle ever growing data.
- Companies can invest in GenAI and utilize the troves of data to generate delivery productivity enhancement by 30% [4] and improve knowledge worker productivity by upto 70% [5]
- Arocom IT Solutions Pvt. Ltd. helps your company build state-of-the-art, functional and easy to use GenAI workflows to realize the full potential of GenAI at your company. To discuss how we can enable you to achieve similar data transformations, reachus@arocomsolutions.com
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Author
Sairam Pillai
Sairam leads the technology group at Arocom. He brings a solid industry experience and expertise in deep learning, advanced data modeling, and the design of sophisticated data pipelines. He is architect of automated, scalable, and cutting-edge data & machine learning pipelines on the cloud. Follow him on
