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30% REDUCTION
in yield losses

In complex manufacturing environments, yield losses due to defect or damaged products are a serious concern and can significantly decrease the production profitability. Because the production of semiconductor chips is a multistep process that takes weeks to months and include several intermediate quality-testing processes testing costs and yield losses can account for a major part of the production costs. With AI, you can reduce yield losses up to 30% and thereby substantially cut production costs*. This is accomplished among others through AI empowered simulation tests, chip design that is less prone for production faults and real-time production surveillance.

WHERE AI BOOSTS THE SEMICONDUCTOR BUSINESS

AI-powered Simulation Tests

Use AI to decrease electronic components testing and R&D time

Enhanced Design

Let AI support your engineers with chip design suggestions and optimizations

Production Surveillance

Detect manufacturing abnormalities and prevent production failures by analyzing real-time manufacturing data

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EXEMPLARY CLIENT CASE

SOLA

LEADING GERMAN
SEMICONDUCTOR MANUFACTURER

Challenge

Within the development of new semiconductor chips, the design must undergo thousands of individual tests in order to verify their proper functioning. A leading German semiconductor manufacturer approached us to reduce the number of tests needed in order to speed up R&D time.

Solution

We trained active-learning and constrained optimization algorithms that identified redundancies in the test sets and suggest an optimal testing process.

Impact

With our machine learning algorithm, we helped our client to significantly increase the efficiency of the testing process leading to reduced R&D costs and faster development cycles.

*McKinsey, Smartening up with AI: What's in it for Germany and its industrial sector?, 2017



$1 BILLION
in yearly savings at Netflix

Technology has led to a non-linearity of modern media giving users more choice and flexibility for media consumption than ever before. Especially the internet offers abundant and ubiquitous media content. In 2016, Netflix offered more than 4,335 movies and 1,197 shows in the U.S. Media players, therefore not only need to focus on the quality of the content but also make sure users find the content they like best. AI substantially helps you to process large amounts of media content, whether detecting inappropriate content or making recommendations. In a recent study, VP of product innovation Carlos Uribe-Gomez at Netflix estimates that Netflix is saving $1 billion per year by applying AI algorithms.

WHERE AI BOOSTS THE MEDIA BUSINESS

Content Recommendations

Help your users discover the most interesting content for them by letting AI make personalized recommendations

Detect Inappropriate Content

Help employees detect or label inappropriate content faster and more efficiently

Dynamic Ad Placement

Improve ad efficacy by letting AI find the best context in a movie or a podcast

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EXEMPLARY CLIENT CASE

LEADING GERMAN MEDIA COMPANY

SOLA

Challenge

Our client was confronted with the problem of labelling hundreds of videos for nudity on a daily basis. This manual process was resource and time intensive.

Solution

We trained and implemented a deep convolutional network algorithm with more than 100,000 pictures that is able to detect raw and partial nudity in videos.

Impact

Our solution helps the client to classify new videos faster and with less effort for the personnel. In addition, the algorithm forms the basis to scale and grow the client's video platform.



$20 BILLION
global AI security market by 2023

The global market for AI security will grow from around $4 billion in 2017 to $20 billion in 2023 demonstrating AI's huge potential to change the security industry*. AI helps you to better analyze threats and respond to attacks and security incidents by analyzing large amounts of data and automatically finding malicious patterns. In addition, AI improves surveillance quality by automating menial tasks previously carried out by security personnel.

WHERE AI BOOSTS THE SECURITY BUSINESS

Malware Detection

Let AI recognize malicious activities by analyzing actions and events in your network

Enhanced Human Analysis

Make security staff more effective by letting AI pre-filter large amounts of data and forward incidents with enriched information

Automate Repetitive Security Tasks

Empower security staff to focus on more important work by letting AI take care of low-value decision-making activities

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EXEMPLARY CLIENT CASE

Challenge

Verifying the authenticity of identifying documents over video is currently a difficult process done by specially trained agents. Our client asked us to automate the verification process.

Solution

We trained a deep convolutional neural network with more than 50,000 pictures that can distinguish real from fake ID cards in real-time, without the need of human interaction

Impact

With our ready-to-deploy solution we helped our client to launch a new and innovative product based on automated ID verification.

*Orbis Research, Global Artificial Intelligence in Security Market and Global Security Control Room Market report, 2018



$150 BILLION
savings in the U.S. until 2026

AI applications will create potentially $150 billion in savings in the U.S. healthcare industry until 2026*.  It is a set of technologies enabling machines to act, comprehend, sense and, learn in order to perform administrative and clinical healthcare functions. Not only is AI a significant opportunity for you to manage your bottom line, but also to capitalize new growth potential.

WHERE AI BOOSTS THE PHARMA & HEALTHCARE BUSINESS

AI-powered Diagnosis

Increase diagnosis quality and precision by giving doctors AI generated diagnosis suggestions

Administrative Workflow Assistance

Accelerate hospital processes by eliminating non-patient care activities such as writing chart notes, prescriptions or ordering tests

Drug Discovery & Testing

Reduce R&D costs by letting AI predict the drugs' success rates or use AI to get decision support for clinical trials

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You have a new use case for AI? We help you to validate your idea with an experimental prototype.
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EXEMPLARY CLIENT CASE

Challenge

At a later stage in the drug development process, a failed toxicity test means that the prior development investments are lost. Evotec wanted to know whether AI can predict liver toxicity early in the process to avoid failed investments.

Solution

We developed a machine learning algorithm that predicts liver toxicity directly from RNA sequence data, giving an early risk indication.

Impact

We helped our client to validate this specific deep learning use case. This will empower Evotec to make better high-risk investment decisions in the future.

*Accenture, Artificial Intelligence: Healthcare's New Nervous System, 2017

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