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.
Why Use Dataknobs Philosophy
CIO
CDO
CPO
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.
Lineage
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
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
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.
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 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
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.
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.
Spotlight
Generative AI slides
KREATE
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