Top 10 Machine Learning Start-Ups to Watch in 2023

Whether you are new to machine learning or are looking forward to coming up with a startup in the same domain, the best way to get inspired is by looking at successful startups. Machine learning has not left any industry untouched. Be it healthcare, defense, B2B services, or agriculture, machine learning has catered in numerous ways. In this article, we will throw light on the top 10 machine learning startups to watch in 2023. Read on!

Savvie

Savvie, a Scandinavian startup employs machine learning as a tool in order to mitigate food waste. Knowing the enormous quantity of food waste that piles up with every passing day, Savvie surely turns out to be a saviour. With this startup, the bakeries and cafes now have an access to an easy-to-use, digital resource to streamline operations and reduce waste using actionable insights.

Delta AI

Organizations rely heavily on customer's choices and taste by knowing how many people are buying a product is valuable data for a company. Taking this into account, Delta AI leverages the power of artificial intelligence and machine learning to compile data from videos on social media. The objective is clear – to provide impactful, contextual insights for product developers and companies on how their products are being used.

Luminance

Using the power of machine learning, Luminance develops technology to improve and streamline reading and understanding of legal documents. Well, what is worth a mention is the fact that it does all this in every language and jurisdiction. In simple terms, this Cambridge startup helps users at every stage of the process, from drafting legal documents to contract negotiation.

Particle

IoT is a trend that doesn't seem to disappoint you. With this idea, Particle, a machine learning startup based out of San Francisco, offers users both hardware and software to enable them to prototype, scale, and manage Internet of Things (IoT) products all on one edge-to-cloud platform that replaces over 20 IoT vendors and services.

LeadGenius

In no time, LeadGenius has emerged to become an impactful tool for sales teams to generate, qualify, deliver, and convert leads resulting in an automated revenue pipeline and streamlined growth — all on one convenient platform. No wonder why this California based startup makes it to the list of top 10 machine learning startups to watch in 2023.

Alation

Alation has widely gained recognition for bringing data catalogs to market. The goal is to provide users with trustworthy, simple data collection to help predict trends and make impactful decisions, thereby enabling them to improve their business strategy. The startup is widely accepted – so much so that it has two major and notable clients, Pfizer and Cisco.

ClosedLoop.ai

ClosedLoop.ai is a data science company built to support the healthcare industry by making care more effective and accessible. Healthcare organizations are immensely benefitted as a result of this startup as they are now able to leverage data and corresponding insights to improve their services and overcome challenges in the field.

Anodot

Customer service is probably the most important parameter that organization pay heed to. On the same lines, Anodot, a leading machine learning tool for companies has significantly improved customer experience. In addition to this, this machine learning startup also protects revenue, and secure partner ecosystems. It has a good number of notable clients including Pandora, Vimeo, and Credit Karma.

RideVision

The name gives a slight idea as to what the startup could be about. Well, you guessed it right! It relates to motorcycles. RideVision has come up with innovative autonomous driving software that provides any motorcycle with predictive Vision and enhanced visibility and safety features. Well, thanks to machine learning!

Reekon

Yet another interesting machine learning startup is Reekon which provides a semi-automated approach to the customer service experience through email, live chat, and phone calls to reduce the number of inquiries addressed by actual team members without sacrificing the quality of service.