Kebotix

About Kebotix

Kebotix utilizes a self-driving lab that integrates cloud technologies, machine learning, and physical modeling to accelerate materials discovery and production. This approach addresses the lengthy and inefficient R&D processes in material innovation, enabling faster market entry for new products.

```xml <problem> Traditional materials science research and development is a slow, capital and labor intensive process, often taking many years to bring new materials to market. The trial-and-error nature of conventional lab work and reliance on human intuition limits the speed and efficiency of materials discovery. </problem> <solution> Kebotix offers an AI-powered "self-driving lab" that accelerates the discovery and development of novel materials. By integrating cloud computing, machine learning, physical modeling, and robotic automation, Kebotix creates a closed-loop system where each iteration of material design, production, and testing informs the next. This approach enables faster exploration of the chemical space, optimized material properties, and reduced time-to-market for new products. The platform provides enterprise AI solutions customized for specific materials discovery needs. </solution> <features> - Cloud-based platform integrating AI, physical modeling, and automation for materials R&D - Closed-loop design paradigm enabling automated learning from each predict-produce-prove cycle - Machine learning algorithms for predicting material properties and optimizing experimental design - Robotic automation for high-throughput synthesis and characterization of materials - Enterprise AI solutions customized for specific materials discovery applications </features> <target_audience> The primary customers are companies in the materials science industry, including chemical, pharmaceutical, and manufacturing companies, seeking to accelerate their R&D processes and discover novel materials with improved properties. </target_audience> ```

What does Kebotix do?

Kebotix utilizes a self-driving lab that integrates cloud technologies, machine learning, and physical modeling to accelerate materials discovery and production. This approach addresses the lengthy and inefficient R&D processes in material innovation, enabling faster market entry for new products.

Where is Kebotix located?

Kebotix is based in Cambridge, United Kingdom.

When was Kebotix founded?

Kebotix was founded in 2017.

How much funding has Kebotix raised?

Kebotix has raised 23650000.

Location
Cambridge, United Kingdom
Founded
2017
Funding
23650000
Employees
9 employees

Find Investable Startups and Competitors

Search thousands of startups using natural language

Kebotix

⚠️ AI-generated overview based on web search data – may contain errors, please verify information yourself! You can claim this account with your email domain to make edits.

Executive Summary

Kebotix utilizes a self-driving lab that integrates cloud technologies, machine learning, and physical modeling to accelerate materials discovery and production. This approach addresses the lengthy and inefficient R&D processes in material innovation, enabling faster market entry for new products.

kebotix.com2K+
cb
Crunchbase
Founded 2017Cambridge, United Kingdom

Funding

$

Estimated Funding

$20M+

Team (5+)

No team information available.

Company Description

Problem

Traditional materials science research and development is a slow, capital and labor intensive process, often taking many years to bring new materials to market. The trial-and-error nature of conventional lab work and reliance on human intuition limits the speed and efficiency of materials discovery.

Solution

Kebotix offers an AI-powered "self-driving lab" that accelerates the discovery and development of novel materials. By integrating cloud computing, machine learning, physical modeling, and robotic automation, Kebotix creates a closed-loop system where each iteration of material design, production, and testing informs the next. This approach enables faster exploration of the chemical space, optimized material properties, and reduced time-to-market for new products. The platform provides enterprise AI solutions customized for specific materials discovery needs.

Features

Cloud-based platform integrating AI, physical modeling, and automation for materials R&D

Closed-loop design paradigm enabling automated learning from each predict-produce-prove cycle

Machine learning algorithms for predicting material properties and optimizing experimental design

Robotic automation for high-throughput synthesis and characterization of materials

Enterprise AI solutions customized for specific materials discovery applications

Target Audience

The primary customers are companies in the materials science industry, including chemical, pharmaceutical, and manufacturing companies, seeking to accelerate their R&D processes and discover novel materials with improved properties.

Want to add first party data to your startup here or get your entry removed? You can edit it yourself by logging in with your company domain.