DataKlear

Your gateway to custom AI assistants with no-code simplicity

Contact for Pricing

Description

DataKlear specializes in developing advanced AI-powered virtual assistant solutions leveraging state-of-the-art large language models (LLM) and retrieval-augmented generation (RAG) technologies. The platform focuses on creating intelligent, conversational AI systems designed to enhance productivity, streamline workflows, and deliver highly personalized user experiences across B2B and B2C markets.

By combining LLMs with RAG, DataKlear's virtual assistants provide accurate, real-time responses drawn from vast data sources. The core offering is a low-code and no-code toolkit enabling users, regardless of technical expertise, to rapidly build and deploy custom AI applications like digital twins, AI agents, co-pilots, and educational neurotrainers. The platform emphasizes ease of use through drag-and-drop interfaces and pre-built templates, significantly reducing development time and costs while ensuring scalability and seamless integration with existing systems.

Key Features

Low-code/No-code Platform: Enables rapid development and deployment of AI virtual assistants without extensive coding.
RAG Technology Integration: Provides accurate, real-time responses by retrieving information from vast data sources.
Conversational AI Platform: Builds intelligent, interactive AI systems.
Digital Twin Creation: Allows building virtual replicas of assets and processes.
AI Agent Deployment: Creates agents for customer support, workflow automation, and insights.
Co-pilot Development: Assists users in real-time with recommendations and task automation.
Drag-and-Drop Interfaces: Simplifies the design and deployment process.
Pre-built Templates: Accelerates development for common AI applications.
Customizable Workflows: Allows personalization of applications to specific needs.
Scalability and Flexibility: Platform adapts to growing business needs.
Seamless Integration: Connects with existing systems and workflows.
Adaptive Learning: Assistants improve responses based on user interactions.
Context-Aware Responses: Remembers past interactions for relevant replies.