DataKnobs: Transforming Enterprises with AI-Powered Data Products for Humans and Machines

Discover how DataKnobs empowers enterprises to build innovative data products using human-created NLP and structured data, as well as IoT data from machines and robots. Learn about Kreate, Kontrols, and AI Twin for seamless data product development


DataKnobs: Empowering Enterprises to Build Cutting-Edge Data Products

DataKnobs is at the forefront of innovation, enabling enterprises to seamlessly build data products by harnessing the full potential of human-created and machine-generated data. Our approach is grounded in understanding the diverse languages of humans and machines, offering two broad categories of data-driven capabilities:

1. Human-Created Data

This category focuses on NLP (Natural Language Processing) and structured datasets crafted by human inputs. Human-created data encompasses text, tabular data, and structured information generated in business processes, research, and decision-making workflows. DataKnobs provides tailored solutions for this category through its products:

  • Kreate: A comprehensive platform to build, experiment with, and deploy data products that leverage NLP and structured datasets. Kreate allows enterprises to design AI-driven workflows, generate actionable insights, and build scalable data products for a wide range of use cases, such as sentiment analysis, personalized content, and predictive analytics.
  • Kontrols: A powerful tool for monitoring, managing, and optimizing the lifecycle of data products. Kontrols ensures data integrity, compliance, and performance metrics for AI and data-driven applications.

2. Machine/Robot-Generated IoT Data

This category deals with IoT (Internet of Things) data and streaming data generated by machines and robots. IoT data represents the language of machines, encompassing sensor readings, telemetry, and other real-time data streams. To address this unique data stream, DataKnobs introduces:

  • AI Twin: A robust platform designed to build data products using IoT and streaming data from both machines and human-machine interactions. AI Twin enables enterprises to create AI models that simulate and analyze machine behavior, optimize operations, and deliver predictive maintenance capabilities.

Unified Approach to Building Data Products

DataKnobs brings together the best of human and machine data to redefine how data products should be built for enterprises. By understanding the nuances of both human language and machine communication, DataKnobs bridges the gap between structured and unstructured data sources, creating a holistic framework for data product development.

This integrated approach ensures that enterprises can:
- Harness the power of diverse data sources, from human-generated text to machine telemetry.
- Experiment with and optimize data-driven workflows using advanced AI models and tools.
- Build scalable, enterprise-grade solutions that drive innovation and operational efficiency.

With DataKnobs' suite of tools—Kreate, Kontrols, and AI Twin—enterprises gain the ability to navigate the complexities of modern data ecosystems and unlock the true potential of their data. Whether it’s transforming NLP insights into actionable strategies or leveraging IoT data for operational excellence, DataKnobs is your partner in building the future of data products.


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Dataknobs Blog

10 Use Cases Built

10 Use Cases Built By Dataknobs

Dataknobs has developed a wide range of products and solutions powered by Generative AI (GenAI), Agent AI, and traditional AI to address diverse industry needs. These solutions span finance, healthcare, real estate, e-commerce, and more. Click on to see in-depth look at these use cases - Stocks Earning Call Analysis, Ecommerce Analysis with GenAI, Financial Planner AI Assistant, Kreatebots, Kreate Websites, Kreate CMS, Travel Agent Website, Real Estate Agent etc.

AI Agent for Business Analysis

Analyze reports, dashboard and determine To-do

DataKnobs has built an AI Agent for structured data analysis that extracts meaningful insights from diverse datasets such as e-commerce metrics, sales/revenue reports, and sports scorecards. The agent ingests structured data from sources like CSV files, SQL databases, and APIs, automatically detecting schemas and relationships while standardizing formats. Using statistical analysis, anomaly detection, and AI-driven forecasting, it identifies trends, correlations, and outliers, providing insights such as sales fluctuations, revenue leaks, and performance metrics.

AI Agent Tutorial

Agent AI Tutorial

Here are slides and AI Agent Tutorial. Agentic AI refers to AI systems that can autonomously perceive, reason, and take actions to achieve specific goals without constant human intervention. These AI agents use techniques like reinforcement learning, planning, and memory to adapt and make decisions in dynamic environments. They are commonly used in automation, robotics, virtual assistants, and decision-making systems.

Build Dataproducts

How Dataknobs help in building data products

Building data products using Generative AI (GenAI) and Agentic AI enhances automation, intelligence, and adaptability in data-driven applications. GenAI can generate structured and unstructured data, automate content creation, enrich datasets, and synthesize insights from large volumes of information. This helps in scenarios such as automated report generation, anomaly detection, and predictive modeling.

KreateHub

Create New knowledge with Prompt library

At its core, KreateHub is designed to enable creation of new data and the generation of insights from existing datasets. It acts as a bridge between raw data and meaningful outcomes, providing the tools necessary for organizations to experiment, analyze, and optimize their data processes.

Build Budget Plan for GenAI

CIO Guide to create GenAI Budget for 2025

CIOs and CTOs can apply GenAI in IT Systems. The guide here describe scenarios and solutions for IT system, tech stack, GenAI cost and how to allocate budget. Once CIO and CTO can apply this to IT system, it can be extended for business use cases across company.

RAG For Unstructred and Structred Data

RAG Use Cases and Implementation

Here are several value propositions for Retrieval-Augmented Generation (RAG) across different contexts: Unstructred Data, Structred Data, Guardrails.

Why knobs matter

Knobs are levers using which you manage output

See Drivetrain appproach for building data product, AI product. It has 4 steps and levers are key to success. Knobs are abstract mechanism on input that you can control.

Our Products

KreateBots

Setup chatbots in minutes
  • Pre built front end that you can configure
  • Pre built Admin App to manage chatbot
  • Prompt management UI
  • Personalization app
  • Built in chat history
  • Feedback Loop
  • Available on - GCP,Azure,AWS.
  • Add RAG with using few lines of Code.
  • Add FAQ generation to chatbot

    • KreateWebsites

    LLM and AI based Website generation
  • AI powered websites to domainte search
  • Premium Hosting - Azure, GCP,AWS
  • AI web designer
  • Agent to generate website
  • SEO powered by LLM
  • Content management system for GenAI
  • Buy as Saas Application or managed services
  • Available on Azure Marketplace too.

    • Kreate CMS

    CMS GenAI
  • CMS for GenAI
  • Lineage for GenAI and Human created content
  • Track GenAI and Human Edited content
  • Trace pages that use content
  • Ability to delete GenAI content

    • Generate Slides

    Effortless slides creation
  • Give prompt to generate slides
  • Convert slides into webpages
  • Add SEO to slides webpages

    • Content Compass

    Your story writing is automated
  • Generate articles
  • Generate images
  • Generate related articles and images
  • Get suggestion what to write next