Why Use Dataknobs Philosophy




Data Officer(s) need to confront complexity of modern enterprise thru multiple data models. Knobs enable interpretation of data & build logical understanding. Most importantly knobs act as levers using which data leaders can control how information is applied in various experiment and AI processes.

Create Data Products

Enable intelligence: Build higher level concepts from raw datasets enabling users to make decisions.

Add Natural Interfaces

LLM based interfaces combined with traditional web apps interfaces enable users to interact with data product. Buld accessible, personalized interfaces and conversational AI agents

Dataknobs Approach

Combining generative AI, traditional AI and data engineering creates a powerful synergy and enable building data products that human can understand and business process and consume.

AI Twins

Build higher level concetps from IoT dataset. See future of assets and control the present. AI twin is AI driven asset management from ground up.

AI Assitants

Supports LLM-based, small model, and intent-based AI agents. Intuitive interface simplifies agent creation and deployment.

Dataknobs AI Capabilities

Identifying orthogonal knobs/levers is key for AI. Knobs let you experiment, build, govern, manage Data Products built using AI

Predictive AI

Machine Learning amplifies the whispers from raw data. Build data products guiding smarter operational and finacial decisions.Bring predictive analytics directly into producion proceses.

Generative AI

Our intuitive interface and command line capabilities simplifies agent creation and deployment of AI Assitants. Supports LLM-based, small model, and intent-based AI agents. Deploy on cloud or on premise. Protects user data and privacy with robust security measures

Dataknobs Product - Kreate

Data products generally require validation both of whether the algorithm works, and of whether users like it. As a result, builders of data products face an inherent tension between how much to invest in the R&D upfront and how quickly to get the application out to validate that it solves a core need.

Kreate Bots

Create chatbot and AI agents with ease. Focus on enriching knowledbease. Combine simple chatbot and AI agents according to complexity of tasks. Try using pre-built AI Agent templates with full code access.

Kreate Websites

Generate webpages,seo and E2E websites directly from google drive or github content. Create visualization and experience that win with users and win with search engine algorithms..

Dataknobs Product - ABExperiment

AB Experiment

Test and optimize your chatbot conversations for better results. A/B experiment your digital human to see which persona resonates best with your audience. Generate different agent response and experiment at one place

AB Testing

A/B test in natural and intutive manner. Use pre built modeules, API and platform to unlock the power of a/b testing. Generate web design and a/b test at same place.

Data Governance Features

Data products generally require validation both of whether the algorithm works, and of whether users like it. As a result, builders of data products face an inherent tension between how much to invest in the R&D upfront and how quickly to get the application out to validate that it solves a core need.

Data Quality

Good data is accurate, consistent, complete and validated. The data should be available at right time and accessible to user with right context.


Trace the data journey from raw data to refined dataproducts. Ensure transparency throughout the life cycle - data product creation, experimentation, usage in website and powering chatbots.

Build Datasets for AI

Create Datasets

Use knobs to define dataset. Efficiently construct dataset that represent real word. Learn optimal policy and generaization with compressed dataset.

Augment datasets

Add more data points, diversity, variability to build dataset that represent world. Generate new dataset to handle cold start problem or test model

Data Privacy, on your terms

Remove PII

Identify, mask or remove personally identifiable information from datasets. By obscuring PII information, use data for experiments and comply with regulations.

Advance anonymization

Anonymize data to protect against identify,membership and attribute disclosure. Protect the privacy as well as make data useful for getting insight.

E2E Use Cases

AI Assitant for Finance

Built AI assitants that help users in making investment decisions. Built AI assitants/bot to answer complex queries on stocks earnings call.

Predictive Maintenance

Integrate Iot datsets,built data products. Using A predict remaining useful life of equipment

Chatbot for Travel

Built chatbot that help user plan vacation according to wish list and packages provided by company. Chatbot seemlessly integrate with website and can derive the context from navigation across website.


AIASE use artificial intelligence (AI) to augment the capabilities of software engineers. AIASE aims to improve the efficiency, quality, and reliability of software development by automating repetitive tasks, providing insights into code, and helping engineers to make better decisions.

From the blog

Build Dataproducts

How Dataknobs help in building data products

Enterprises are most successful when they treat data like a product. It enable to use data in multiple use cases. However data product should be designed differently compared to software product.

Be Data Centric and well governed

Generative AI is one of approach to build data product

Generative AI has enabled many transformative scenarios. We combine generative AI, AI, automation, web scraping, ingesting dataset to build new data products. We have expertise in generative AI, but for business benefit we define our goal to build data product in data centric manner.

Well governed data

Data Lineage and Extensibility

To build a commercial data product, create a base data product. Then add extension to these data product by adding various types of transformation. However it lead to complexity as you have to manage Data Lineage. Use knobs for lineage and extensibility

Develop data products with KREATE and AB Experiment

Develop data products and check user response thru experiment

As per HBR " Data product require validation of both 1. whether algorithm work 2. whether user like it". Builders of data product need to balance between investing in data-building and experimenting. Our product KREATE focus on building dataset and apps , ABExperiment focus on ab testing. both are designed to meet data product development lifecycle

Innovate with experiments

Experiment faster and cheaper with knobs

In complex problems you have to run hundreds of experiments. Plurality of method require in machine learning is extremely high. With Dataknobs approach, you can experiment thru knobs.

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.


Generative AI slides

  • Learn generative AI - applications, LLM, architecture
  • See best practices for prompt engineering
  • Evaluate whether you should use out of box foundation model, fne tune or use in-context learning
  • Most important - be aware of concerns, issues, challenges, risk of genAI and LLM
  • See vendor comparison - Azure, OpenAI, GCP, Bard, Anthropic. Review framework for cost computation for LLM

    Our product KREATE can generate web design. Web design that are built to convert

    Using KREATE you can publish marketing blog with ease. See KREATE in action

    Fractional CTO for generative AI and Data Products

    Startup and enterprise who wish to build their own data prodct can hire expertise to build Data product using generative AI

  • Generative AI expertise
  • Machine Learning expertise
  • Data product building expertise
  • Cloud - AWS, GCP,Azure