Explore our Machine Learning and AI expertise

CodeCoda uses machine learning, artificial intelligence, and neural networks to build self-taught enterprise solutions. Take advantage of our solid tech expertise!

Machine Learning processes and analyzes large amounts of data without explicit programming. Machine Learning scrutinizes and learns from previous experience, improving performance on the go. CodeCoda’s ML and AI scientists, having developed many self-taught systems for the Fintech, Banking, Aviation, Information and Content Management, Entertainment and Gaming industries, provide advanced hands-on knowledge to businesses wanting to leverage the power of Machine Learning.

We compose many machine learning models, both with Python and R, using multiple additional frameworks and libraries such as Caffe, DeepLearning4J, TensorFlow, Theano, Torch/Py Torch, Matlib, and many others.

Machine intelligence
is the
last invention
that humanity will
ever need
to make.

- Nick Bostrom

1

Why AI and ML?

For a wide array of tasks, machine learning is superior to traditional software designs. It stands behind state-of-the-art search engines, real-time data science, digital security, and artificial intelligence software. CodeCoda is leading developments in the sector of AI when it comes to cutting-edge machine learning and cognitive computing expertise. We help our clients to boost services and outperform the competition with the power of AI.

2

Use Case: Cybersecurity

Machine learning systems examine how users access corporate data and categorize behavioral patterns. Self-taught algorithms recognize user activities that might put valuable data at risk and act to isolate them. CodeCoda helps clients to build rigid cybersecurity systems that identify and prevent security breaches in real-time.

3

Use Case: Risk Analysis

Much like cybersecurity, machine learning algorithms are incredibly efficient at recognizing irregular financial activities. They analyze user actions and distinguish between various types of transactions. CodeCoda creates custom self-taught solutions that track anomalies on the fly and flag suspicious behavior. These systems can accurately detect cases of fraud and other financial risks.

Added Value of AI and Machine Learning Solutions

CodeCoda strives to add value to our clients operative business. Our custom developed AI solutions can help your business, using the below services and methodologies.

Our AI specialists, with over 10 years of relevant expertise in a variety of industries, deliver insightful solutions that harvest and process data in real-time. AI and Machine Learning can also be used as an extension to Business Intelligence, Data Science and Big Data solutions, providing additional value by adding real time processing and automated decision taking. React when the customer interacts!

Real-time data science

Self-taught analytical tools

AI algorithms and applications

Neural networks and Deep Learning

Automated customer interactions

Fraud detection

Intelligent cybersecurity

Breakthrough business insights

Natural language processing (NLP)

Delivery Process

How Machine Learning solutions are delivered

AI empowers business processes to be self-taught, thus adding efficiency to operations.

AI and ML solutions developed by CodeCoda give businesses a competitive advantage. We have been working with clients in a variety of industries like finance, banking, fintech, gaming, gambling and ecommerce.

Our Machine Learning Engineers have domain-specific knowledge in a variety of industries. We use a unique Machine Learning framework design process to satisfy our customers' demands for excellence. To achieve the best results, we apply a unique process layer:

1

Analysis

We analyze the project, data sets, and it's key metrics and set relevant KPIs. Performance is defined by whichever metric is most relevant to the success of the end product.

2

Selection

Machine Leaning engineers select the most suitable approach and algorithms predicting which methodology will achieve the most predicable forecasting outcome.

3

Implementation

Engineers start with a minimal model implementation that has very little uncertainty involved. We set up training and testing datasets for development, iterating over the process until the most optimal training model is achieved.

4

Measurment

We measure and validate business value. Eventually step 1 to 4 are repeated as often as it takes to achieve the required business outcome. Finally we set-up dashboards or integrate learning results with pre-existing solutions within the clients ecosystem.

What is Machine Learning good for?

01

Process Automation

Intelligent Process Automation (IPA) combines artificial intelligence and automation.

Intelligent Process Automation (IPA)

From automating manual data entry, to more complex use cases like automating insurance risk assessments. Thanks to cognitive technology, like natural language processing, machine vision and deep learning, machines can augment traditional rule-based automation. Over time models improve even further as more data is analyzed.

02

Sales Optimization

Chatbots as well as Digital Assistants are taking over the world.

Sales Optimization & Customer Service

Sales typically generate a lot of unstructured data which can be used to train machine learning algorithms. This comes as good news to enterprises that have been saving consumer data for years. Due to the high volume of customer interactions, the massive amounts of data captured and analyzed is the ideal teaching material required to fine tune ML algorithms.

03

Security

Improve threat analysis and respond to attacks and security incidents.

Security

Predictive analytics enables the early detection of infections and threats, while behavioral analytics ensures that any anomalies within the system does not go undetected. ML also makes it easy to monitor millions of data logs from connecting devices and generate profiles for varying behavioral patterns within clients ecosystem.

AI and Machine Learning Tech Stack

Tools and applications we use to build Machine Leaning and Artificial Intelligence solutions.

Python R Torch / PyTorch Matlib Caffe Tensorflow DeepLearning4J Keras
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