Create AI applications using Data Products, GenAI, and Agentic Workflows.
Dataknobs enables businesses to build custom GenAI agents and data products from privacy compliance monitors and financial analytics copilots to personalized AI assistants for consumers.
AI Agents For Analysis
AI Agent for eCommerce and Finance automate sales, revenue, and customer behavior analysis to uncover insights and drive growth
Automate Compliance
Stay ahead of privacy risks use AI agents to audit your features and ensure compliance with evolving regulations
Personalized Assistant
Offer your consumers personalized AI assistants from financial planners to retirement advisors and diet coaches, tailored guidance at scale.
Solutions Showcase
Explore Dataknobs' proven solutions interactively in this section. Utilize the filters to browse use cases by industry or business function, and select any card to view an in-depth analysis of the business challenge, technical implementation, and delivered value.
Platform Deep Dive
Dataknobs' primary focus is on a cohesive and adaptable platform that allows for the quick creation of tailored, expandable, and regulated AI solutions. Explore each element in the system, starting from data intake to user interaction, by hovering over the components below.
KreateData
Data Engine
KreateBots
Conversational AI
KreateWebsites
Web Deployment
Kontrols
Governance Layer
ABExperiment
Optimization Engine
Hover over a platform component to see details.
Foundational Strengths
Dataknobs excels in foundational data science, giving it a major edge over competitors. This section delves into its 'data factory' capabilities, which guarantee the reliability and trustworthiness of each AI solution provided, tackling the issue of data scarcity in enterprise AI.
Dataknobs enhances AI model performance by utilizing an internal 'data factory' to generate and enhance datasets, including creating high-level features such as a server 'health index' from raw data. This speeds up model development and improves accuracy.
We utilize Weak Supervision to automatically label large datasets using programmatic rules and heuristics, replacing slow manual labeling. Active Learning is employed to have the AI pinpoint the most uncertain data points, ensuring human experts focus their time on the most informative examples and boosting efficiency significantly.
In order to enhance the quality of data, we utilize Optimal Transport, a robust mathematical framework. This enables us to strategically generate synthetic data in order to balance datasets, such as increasing instances of a rare equipment failure. This results in more resilient, unbiased models that demonstrate improved performance in practical scenarios.