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.
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.
Dataknobs capablities - KREATE, KONTROLS and KNOBS. KREATE focus on creatibvity and generation KONTROLS provide guardrails, lineage, compliance, privacy and security. KNOBS enable experimentation and diagnosis
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. (quote:HBR)
AI powered creation of data, web design, web site and AI Assistant. Create new data with complete lineage. Generate website from content and configure to build AI assistant & Spark innovation. Build things beyond imagination at fast speed.
Deployed on customer tenant. Create Text, Images, Proposals, Slides to add new content. Generate internet or intranet portal, generate SEO. Create AI Assistant for enterprise.
Identifying orthogonal knobs/levers is key for AI. Knobs let you experiment, build, govern, manage Data Products built using AI
We use traditional machine learning methods for use cases like Predictive Maintenance, Risk Modeling, Sales Forecasting, Fraud detection kind of scenarios. Traditional Machine learning works best when there are historical data patterns to learn from and the problem requires predicting specific outcomes, like classification, regression, or anomaly detection.
We use GenAI when the goal is to create new content (text, images, audio) or understand context in a more human-like way, especially in unstructured environments such as using text or images in making decision or analysis. Example use cases - Stocks Earning Call.
Use engineering when the problem is well-defined, deterministic, and can be solved using explicit algorithms or rules, such as building software applications, databases, or infrastructure. It's ideal for tasks with clear requirements and predictable outcomes.
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.
Dataknobs Kreate empowers you to build comprehensive data products with ease. Create new datasets, design engaging visualizations, develop interactive presentations, and construct user-friendly web portals to serve your data.
Plus, leverage intelligent chatbots to enable natural language queries and access insights directly.
Kreate leverages advanced AI, generative AI, and engineering principles to create new, valuable data from raw sources. By analyzing and understanding the underlying patterns and structures within existing data, KreateData can generate summaries, extract meaningful data signals, and even create entirely new datasets tailored to specific needs.
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..
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.
Through experimentation, discover novel patterns, identify hidden correlations, and create signals, higher level concentp such as health of equipment, remaining life of machine.
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.
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
Built AI assitants that help users in making investment decisions. Built AI assitants/bot to answer complex queries on stocks earnings call.
Integrate Iot datsets,built data products. Using A predict remaining useful life of equipment
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.
Provide KREATE with your key points, target audience, and choose template style. Its AI engine then generates a well-structured presentation complete with compelling slides, branding, and clear messaging. you can regeerate slides at regular interval.
Stop wasting time manually crafting FAQs. KREATE leverages the power of GenAI LLM and vector DB to automatically generate comprehensive and informative FAQs for your content.
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.
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. Our Product KREATE enable creation of data, user interface, AI assistant. Click to see it in action.
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
KREATE empowers you to create things - Dataset, Articles, Presentations, Proposals, Web design, Websites and AI Assistants Kreate is a platform inclide set of tools that ignite your creatviity and revolutionize the way you work. KReatePro is enterprise version.
KONTROLS enable adding guardrails, lineage, audit trails and governance. KOntrols recogizes that different use cases for Gen AI and AI have varying levels of control requirements. Kontrols provide structure to select right controls.
Well defined tunable paramters for LLM API, LLM fine tuning , Vector DB. These parameters enable faster experimentation and diagosis for every state of GenAI development - chunking, embedding, upsert into vector DB, retrievel, generation and creating responses for AI Asistant.
Create articles for Blogs, Websites, Social Media posts. Write set of articles together such as chapters of book, or complete book by giving list of topics and Kreate will generate all articles.
Design impactful presentation by giving prmpt. Convert your text and image content into presentations to win customers. Search in your knowledbe base of presentations and create presentations or different industry. Publish these presentation with one click. Generate SEO for public presentations to index and get traffic.
AI powered website generation engine. It empower user to refresh website daily. Kreate Website AI agent does work of reading conent, website builder, SEO, create light weight images, create meta data, publish website, submit to search engine, generate sitemap and test websites.
Set up AI Assistant that give personized responss to your customers in minutes. Add RAG to AI assistant with minimal code- implement vector DB, create chunks to get contextual answer from your knowlebase. Build quality dataset with us for fine tuning and training a cusom LLM.
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
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.
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.
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