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Data Science & AI

Optimize enterprise operations with mathematical precision

Solutions

Customer intelligence and personalization

Automatically optimize billions of micro-decisions related to customer experience, personalization, message targeting, and timing using machine learning and advanced analytics. From small business to enterprise, enhance the customer experience and achieve tangible results using real-time, personalized components.

Anomaly detection

Minimize risks in your customer-facing, manufacturing, and IT management processes using machine learning.

Trade promotion optimization

Adopt a scientific approach to trade promotion management to maximize profits, prevent losses, improve inventory turnover, and optimize customer experience. Improve performance and enhance your competitive edge using advanced data-driven models.

Supply chain optimization

Optimize hundreds of supply chain and pricing parameters using artificial intelligence, predictive models, and simulations. You can simplify demand planning and inventory planning and reduce lead times and stock-outs using our software solutions that optimize your supply chain processes and decisions.

Revenue and price optimization

Take the guesswork out of price and promotion management, and use advanced econometric and machine learning models to improve both price optimization strategy and tactical pricing decisions.

Marketing spend optimization

Use advanced statistical models to optimally allocate budgets across marketing channels and measure the true contribution of each channel or campaign. Leverage marketing data to gain insights for potential growth and areas of investment to improve the bottom line.

Conversational AI

Modern conversational AI is redefining our workday, our shopping experience, our homes, and our daily habits. As more and more conversational devices and virtual assistants enter our lives, customers and employees expect to be able to engage in conversation with your enterprise from any device or channel, on‑demand, day or night.  We design and implement conversational AIs that will empower your team and delight your customers.

Fraud detection and prevention

Protect your applications, services, and operations from fraud using machine learning and decision automation. Our software provides real-time fraud analytics and prevention by integrating with your transactional systems and data streams.

Analytical data platform

Increase speed to insights. Manage enterprise data assets, migrate from on-premise EDW to cloud, improve data accessibility and quality, and increase the return on investment with our solution and accelerator.

Predictive maintenance

Prevent failures and reduce operational and maintenance costs using data science and machine learning

Our clients

Retail
Hi-tech
Manufacturing
Finance
Introduction to Algorithmic Marketing
Book
Introduction to Algorithmic Marketing
Introduction to Algorithmic Marketing is a comprehensive guide to advanced marketing automation for marketing strategists, data scientists, product managers, and software engineers.
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A Plug-and-play Platform for In-App and In-game Experience Personalization

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Why Grid Dynamics

Industry specializations

The commercial success of data science and enterprise AI projects often depends on domain expertise, at least as much as on statistical and machine learning skills. Grid Dynamics has experts, blueprints, and accelerators that are laser-focused on specific business functions, such as marketing and supply chain, and industries, such as retail, manufacturing, and finance.

ROI models and reference implementations

At Grid Dynamics, we are highly focused on delivering business value to our clients in a predictable way. We have developed unique ROI models to streamline typical data science engagements and created reference implementations that enable us to evaluate multiple standard models and techniques promptly.

Global data science talent

We have spent more than a decade building a global network of data engineering and data science professionals. This enables us to staff data science projects rapidly, involve top talent, and provide our clients with flexible options regarding the location and composition of engineering and consulting teams.

Rapid prototyping

Solid reference implementations, global engineering networks, and experience with multiple industry and enterprise domains, enables us to develop and deliver prototypes and minimum viable products (MVPs) in a matter of weeks. We readily offer proof-of-concept events for the toughest problems.

Experience with industry leaders

We have worked with S&P 500 companies on the most challenging engineering and data-related problems for more than a decade. We offer unique insight into the state of the industry, world-class best practices, and development methods used by the leading and most innovative companies across the globe.

Cutting-edge technology

We invest heavily in researching enterprise AI problems, studying how the latest deep learning, reinforcement learning, computer vision, and other technologies can be applied to marketing, supply chain, manufacturing, and IT use cases. This effort, combined with our unique industrial experience, helps us to deliver truly innovative and highly efficient solutions.

Events

Grid Dynamics is an organizer of the Data Points Summit, a leading regional conference on data science, enterprise AI, and data engineering. Learn more about past and upcoming events.
Learn more about events

Case studies & Whitepapers

Personalizing in-game experience using reinforcement learning Download PDF
Personalizing in-game experience using reinforcement learning

Problem
- Personalize in-game experience
- Reduce model development effort
- Increase long-term engagement / LTV
 
Grid Dynamics' solution
- Reinforcement learning based personalization platform
- MVP delivered in 8 weeks

Results
Up to 25% dollar-per-user improvement compared with the baselines

Price optimization for video games using machine learning Download PDF
Price optimization for video games using machine learning

Problem
- Optimize promotions across many channels and countries 
- Forecast the demand 24-month ahead
- Properly handle new game releases
 
Grid Dynamics' solution
- Demand forecasting models
- What-if analysis tools for promotion scenarios
- MVP delivered in 6 weeks

Results
- Manual process replaced by data-driven optimization
- Increased promotion efficiency compared with the baselines

The Essential Guide to Transforming IT Operations with AIOps Download PDF
The Essential Guide to Transforming IT Operations with AIOps

Modern IT operations have to deal with dynamic mixes of public cloud platforms and services, cloud-native and serverless applications, and on-premise deployments. These systems, services, and applications generate enormous amounts of data that are challenging to collect, analyze, and use for issue detection and remediation. In this white paper, we discuss how this challenge can be addressed using machine learning and artificial intelligence methods, what aspects of IT operations can be improved using such techniques, and how companies should plan their capability roadmaps in this area. 

Related card image
In this article, we discuss how traditional targeting and personalization models such as look-alike and collaborative filtering can be combined with reinforcement learning to optimize multi-step marketing action policies (aka Next Best Action policies). These techniques can be applied to various business problems such as customer retention, customer experience optimization, and promotion targeting.
Read more

Building a next best action model using reinforcement learning

In this article, we discuss how traditional targeting and personalization models such as look-alike and collaborative filtering can be combined with reinforcement learning to optimize multi-step marketing action policies (aka Next Best Action policies). These techniques can be applied to various business problems such as customer retention, customer experience optimization, and promotion targeting.
Read more
In this article, we discuss how traditional targeting and personalization models such as look-alike and collaborative filtering can be combined with reinforcement learning to optimize multi-step marketing action policies (aka Next Best Action policies). These techniques can be applied to various business problems such as customer retention, customer experience optimization, and promotion targeting.
Read more
Related card image
This article describes a reference implementation of a price and revenue management tool that showcases the main capabilities of AI-based price optimization software and features several advanced techniques. We start with a strategic vision of a price analytics solution, then build a demand model, and finally develop a fully automated optimization application.
Read more

Predictive analytics for promotion and price optimization

This article describes a reference implementation of a price and revenue management tool that showcases the main capabilities of AI-based price optimization software and features several advanced techniques. We start with a strategic vision of a price analytics solution, then build a demand model, and finally develop a fully automated optimization application.
Read more
This article describes a reference implementation of a price and revenue management tool that showcases the main capabilities of AI-based price optimization software and features several advanced techniques. We start with a strategic vision of a price analytics solution, then build a demand model, and finally develop a fully automated optimization application.
Read more
Related card image
This article is a hands-on tutorial that describes how to develop, debug, and evaluate RL optimizers using PyTorch and RLlib. This approach can be applied to a number of supply chain problems such as stock level optimization, transportation cost minimization, and warehouse space optimization.
Read more

Deep reinforcement learning for supply chain and price optimization

This article is a hands-on tutorial that describes how to develop, debug, and evaluate RL optimizers using PyTorch and RLlib. This approach can be applied to a number of supply chain problems such as stock level optimization, transportation cost minimization, and warehouse space optimization.
Read more
This article is a hands-on tutorial that describes how to develop, debug, and evaluate RL optimizers using PyTorch and RLlib. This approach can be applied to a number of supply chain problems such as stock level optimization, transportation cost minimization, and warehouse space optimization.
Read more

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