Dataknobs: Build Data Products Right from the Start with AI and Knobs

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DATAKNOBS 1
        


Unlocking the Power of Data Products with Dataknobs

In today's data-driven world, organizations face the challenge of efficiently building, managing, and scaling their data products. Dataknobs offers an innovative platform to address this need, empowering enterprises to create, control, and experiment with data products effectively. Our mission is to help businesses build data products right from the start.

The Four Pillars of Dataknobs

The Dataknobs platform revolves around four key pillars: Kreate, Knobs, Kontrols, and AB Experiment. Each pillar is designed to address a specific aspect of the data product lifecycle.

1. Kreate

This pillar focuses on the creation of data products, enabling organizations to build robust solutions, including:
- Assistants and Agents: Develop AI-powered assistants and agents to enhance customer engagement and operational efficiency.
- Websites and Portals: Leverage automation and AI to create dynamic, SEO-optimized websites and portals.
- Knowledge: Consolidate and present knowledge effectively to empower decision-making.
- Signals and Data: Generate actionable insights from raw data to inform strategies.

2. Kontrols

Kontrols ensures that your data products are secure, compliant, and aligned with organizational policies. Key features include:
- Integration with Enterprise and Third-Party Controls: Seamlessly integrate with existing systems and frameworks.
- Prompt Management and Controls: Govern prompt-based workflows for consistent outcomes.
- Guardrails and Policy Enforcement: Enforce rules to mitigate risks and ensure compliance.
- Data Lineage: Track and manage the lifecycle of data across systems.

3. AB Experiment

This pillar emphasizes experimentation, enabling teams to test and validate ideas quickly:
- Automated Experimentation on Data: Leverage automation to streamline the experimentation process.
- A/B Testing of Websites: Optimize websites by running controlled experiments.
- Prompt-Based Experimentation: Experiment with AI prompts to refine assistant and agent responses.
- Repository of Experiments: Maintain a comprehensive repository for all experiments, including those related to data, websites, and AI assistants.

4. Knobs

The Knobs pillar is where customization and flexibility come into play. It provides tools and levers to fine-tune data workflows and applications:
- Data Knobs: Configure data processing pipelines to meet unique requirements.
- AI Workflow Levers: Optimize AI workflows for efficiency and accuracy.
- Experimentation and Diagnosis: Experiment with datasets, algorithms, and visualizations to improve outcomes.
- Business and Domain Knobs: Customize data products for specific industries or business domains.

Why Dataknobs?

Dataknobs offers a unified platform that simplifies the creation and management of data products. Whether you're building a website, designing an AI assistant, or experimenting with new datasets, Dataknobs equips you with the tools and controls needed to succeed.

By leveraging Dataknobs, organizations can unlock the full potential of their data, drive innovation, and maintain a competitive edge in their industry. With its comprehensive suite of features, Dataknobs is the go-to solution for building data products right from the start.




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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

  • 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

  • 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 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

  • Give prompt to generate slides
  • Convert slides into webpages
  • Add SEO to slides webpages
  • Content Compass

  • Generate articles
  • Generate images
  • Generate related articles and images
  • Get suggestion what to write next