Applitools

Automated Testing for Applications (Mobile, Web and Native)

Development, Enterprise, Image Processing
Computer Vision Deep Learning Machine Learning

Business Overview

Applitools is on a mission to help Test Automation Engineers, DevOps Teams, Frontend Developers, and Digital Transformation Executives release, test and monitor flawless mobile, web, and native apps in a fully automated way that enables Continuous Integration and Continuous Delivery (CI-CD). Founded in 2013, Applitools uses sophisticated AI powered image processing technology to ensure that an application appears correctly and functions properly on all mobile devices, browsers, operating systems and screen sizes. Applitools has more than 300 customers from a range of verticals, including Fortune 100 companies in software, banking, online retail, insurance, pharmaceuticals, and more. Applitools is headquartered in San Mateo, California, with an R&D center in Ramat Gan, Israel.

Operating Status
Acquired
Founded
January 2013
Employees
74
Business model
B2B
Offering type
Software
Funding stage
Series C
Business stage
Launched
Total funding
$41.5 Million
Sectors
Development, Enterprise, Image Processing, Software Testing

Founders

Applitools
Adam Carmi
CTO
Applitools
Gil Sever
CEO
Serial Entrepreneur
Applitools
Moshe Milman
COO

Funding Rounds

Calculated as the average ratio between the current funding round amount raised and the previous funding round amount raised. Note that 'plus' rounds are summed together. i.e. Series A = Series A and Series A+.
Funding Growth Multiple
3.5x

New wpDataTable

Date Announced

Funding Round

Amount Raised

Investors

Exit Events and Acquisitions

Date Announced

Transaction

Consideration

Target

Acquirer

March 2021
Acquisition
$250 M
Applitools
Thoma Bravo

Revenues

Date

Revenue Type

Amount

2020
Annual
$25M

AI Technology Stack

AI Description

Our task is to understand if a user interface renders correctly and appears properly across all digital screens and engagement points. AI is core to our technology, as it helps us understand if the UI is “correct” in various modes — we are taking this fuzzy concept, and bringing accuracy into it, as our system learns and evolves to generate more accurate results over time.

AI employees
3
AI application
Horizontal AI
AI types
Computer Vision, Deep Learning, Machine Learning, Natural Language Processing
ML types
Supervised Learning
AI algorithms
Decision Trees, Neural Networks
AI tools
Keras, Scikit-Learn, TensorFlow
Coding
Python
AI hardware
CPU, GPU

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