Wiliot

At Wiliot, we bring connectivity and intelligence to everyday products and packaging; things previously disconnect from the Internet of Things. The Wiliot Platform combines cloud services and IoT Pixels, computing elements that can power themselves by harvesting radio frequency energy. This revolutionary mixture of cloud and semiconductor technology is being used by some of the world’s largest brands, food, and pharmaceutical companies to change how we make, distribute, sell, use and recycle products. Our investors include Amazon, Verizon, NTT DoCoMo, Qualcomm, and PepsiCo.
Active
January 2017
165
Hardware, Software
B2B
Launched

people
Alon Yehezkely
CTO
people
Tal Tamir
CEO
people
Yaron Elboim
VP R&D

people
Dov Moran
Managing Partner …
people
Dror Nahumi
General Partner a…
people
Yoram Snir
Partner and Board…

$270,000,000

Series C

July 27, 2021
Series C
$200,000,000

Date Announced

Round Stage

Round Size

Lead Investors

February 19, 2020
Series B+
$20,000,000

Date Announced

Round Stage

Round Size

Lead Investors

January 16, 2019
Series B
$30,000,000

Date Announced

Round Stage

Round Size

Lead Investors

83north-logo
83North
Venture Capital
2006
$1,850,000,000

Investor

Investor Type

Founded

Funds Raised

aws.png
Amazon Web Services
Company
2006

Investor

Investor Type

Founded

Funds Raised

avery-dennison.jpg
Avery Dennison
Company
1990

Investor

Investor Type

Founded

Funds Raised

Wiliot’s Cloud transforms raw sensory data into powerful insights and endless opportunities for application development, using machine learning-based algorithms that learn and adapt over time, so you can transform your supply chain one product at a time. While the Wiliot IoT Pixels produce the low-level, raw sensory data, the actual sensing and analysis are performed in the Wiliot Cloud. The Wiliot cloud-based machine learning algorithm translates raw sensory information into the corresponding physical domains; for example, temperature change, and pick-up/movement. These programs are continually learning and improving over time to recognize trends, events and exceptions.
Artificial Intelligence, Machine Learning
5
LSTM, Neural Networks, Recurrent Neural Network (RNN), Time Series Analysis, Transformer
Kafka, Keras, MXNet, Pytorch, Spark, TensorFlow
Python, Scala

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