AI Model Dev

Creating AI models involves training computers to learn patterns from data to perform tasks like humans.

GenAI Use Cases to Drive Business Outcome

GenAI is a smart tool that uses technology to help businesses by making tasks easier, like recommending products or answering questions.

Data preparation

  • Structured data observability
  • Chunking and training AI on Unstructured data
  • Segment / Enrich / Transform data


  • Auto-generate dashboards & Reports
  • Recommend associated analysis
  • Summarize documents (PDF, Texts, emails, docx, Webpages)


  • Faster, easier sentiment analysis
  • Generating new models and supporting code, visuals

Natural Language

  • Natural Language Query Text to SQL Text to ML / Chart
  • Data narratives and data stories
  • Summary reports, customer support, claim support, accounting support

Code Generation

  • Generate initial code to accelerate development lifecycle
  • Convert legacy code bases into destination code bases, serving as a migration accelerator

Chat Bots and Virtual Agents

  • Chatbots for customer service, claims agents, data analysts, sales, Operations and Supply chain teams
  • “data-in” features for deploying cognitive search services and LLMs against custom datasets

EzInsightsAI SQL Agent

EzInsightsAI SQL Agent simplifies data management by automating tasks like organizing information and generating reports using SQL, making it easier for businesses.

Data preparation

  • Data silos and quality issues persist, with data analysts spending 80% of their time on data discovery, cleansing, and preparation
  • Generative AI models can alleviate labor-intensive tasks such as data classification, tagging, anonymization, segmentation, transformation and enrichment
  • Auxiliary products like data cataloging and management tools enhance data lineage and accessibility
  • Example: EzInsightsAI offers a data management platform for data consolidation, governance, and distribution across analytics models


  • Analytics use cases typically aim for discovering new insights
  • Traditional models may inherit biases and preconceived notions from creators, hindering unbiased analysis
  • Some users may struggle to ask the right questions or be open to alternative perspectives
  • Generative AI models can uncover new data dimensions and correlations autonomously
  • Generate summary content, mix-n-match data from websites, intranet portals, pdfs, documents, emails and create deeper analysis and insights


  • Generative AI models provide concise data summaries from larger reports or generate entire copies using provided data
  • These models excel at contextualizing findings and communicating them succinctly to others
  • They can generate data visualizations at significantly faster speeds compared to humans
  • Example: A Generative AI model can summarize a lengthy sales report into a brief executive summary, or generate charts and graphs based on provided data, saving time and effort in data analysis and presentation

Natural Language Processing (NLP) for Text Analytics

  • Generative models are proficient in understanding and producing human-like text, making them highly effective for Natural Language Processing (NLP) tasks
  • Generate real-time narratives on data to provide key insights
  • Businesses can extract deeper insights from textual data, facilitating informed decision-making processes

Code Generation

  • Core use case: Application of Large Language Models (LLMs) in generating initial code to accelerate development lifecycle
  • Generative AI not a full replacement for structured, meaningful code but enhances productivity through automatic generation of template code
  • Practical application: Convert legacy code bases into destination code bases, serving as a migration accelerator
  • Example: Conversion of Qlik Sense reporting to Power BI, involving refactoring proprietary Qlik syntax into DAX code
  • Generative AI facilitates conversion of basic expressions from Qlik syntax to DAX, expediting solution delivery

Chat Bots and Virtual Agents

  • LLMs enable easier implementation and roll-out of chatbots for customer service on websites
  • Chatbot integration enhances front-end analytics with contextual dimensions in reporting
  • Cloud platforms offer “data-in” features for deploying cognitive search services and LLMs against custom datasets
  • Lower barrier to entry for deploying chatbots due to cloud platforms’ offerings
  • Chatbots can be integrated into workflows via API integration endpoints or native app deployment
  • Open source frameworks like LangChain can be deployed for similar purposes

EzInsightsAI Document Extraction Framework

RAG Workflow Architecture to Train LLMs / AI