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Access to experienced engineers in emerging technologies is hard

Grid Dynamics excels at hiring, training, growing, managing, and retaining specialists in emerging technologies and making them available to clients on demand. We do that by building delivery centers in the cities with the highest number of top technical universities and rich traditions of math, science and innovation. We hire the top 10% of technical talent. Nearly 100% of our staff has advanced degrees.

Every year we deliver hundreds of courses in emerging technologies to our engineers using Grid University, our unique learning platform. Through our deep relationships with top local universities, forged over years of collaboration, and specialized recruiting programs, we can scale the hiring and staffing of new engineering teams to support complex technical programs faster than anyone in the industry.

Rapid prototyping with Lean Labs

Many companies struggle to foster innovation within their existing teams. The challenge is usually cultural—how to marry the need for experimentation, freedom to “fail fast” and a creative license to throw away the “old ways of doing things” with compliance to corporate standards and security practices. Grid Dynamics has a service offering that helps companies try fresh ideas quickly outside of the corporate bureaucracy before bringing them in-house for productization once they have proved to be successful.

We call this program Lean Labs, and it works like this: a dedicated, self-sufficient team staffed by Grid Dynamics engineers works on a customer problem with the client’s subject matter experts. The team moves fast, uses the latest technologies, and aims to solve the business use case to demonstrate measurable value to the business stakeholders. The project takes anywhere from 4 to 12 weeks and costs under $200K.

Cross-Functional Team

We bring all the technical staff, user experience designers and product management required to complete the project beginning to end.

Goal-Oriented Project

The team focuses on a singular goal with clear success criteria and time/budget parameters. Lean Labs are often delivered as fixed-price contracts.

Fast Business Impact

Lean Lab is meant to prove a concept that can be turned into a production pilot very quickly. The team provides productization support once the idea proved its viability to the business.

Our clients

Google logo
Paypal logo
macy's brand logo


Neiman Marcus logo
Grandvision logo
macy's brand logo
Lowes logo
Logo of American Eagle


Google logo
Verizon logo
IAS logo
2k logo
curiositystream brand logo


Jabil logo
Stanley Black&Decker logo
Levis logo
Boston Scientific logo
Tesla logo


Paypal logo
SunTrust logo
logo of travelers brand
Raymond James logo
risers logo
Marchmilennan logo


largest US retailers


largest US technology companies


world’s largest consumer goods companies


largest US financial companies

How rapid prototyping works



  • Business use case
  • Success criteria
  • Subject matter experts
  • Access to data & related services


  • Product manager
  • Engineers
  • Customer experience designers
  • Agile delivery practices


  • Timeline: under 3 months
  • Scope: 1–3 use cases
  • Feedback: weekly demos
  • Cost: under $200K


  • Working demo
  • Source code & documentation
  • Retrospective / lessons learned
  • Productization plan

Emerging technology center

Reinforcement learning


Reinforcement learning

Many problems in supply chain, revenue management, and personalization are traditionally solved by handcrafting an optimization task that can be tackled using standard numerical or combinatorial optimization algorithms. Reinforcement learning is a game changer: it provides a generic multi-step optimization component that can learn optimal control policies from system logs or simulators. We put a lot of effort into the development of applicable reinforcement learning solutions that adapt the latest advancements in this field to enterprise use cases.

Read our technical articles:


Representation learning


Representation learning

Many traditional models for personalization, recommendations, price management, and supply chain optimization are designed to use only one type of data, such as clickstream or sales transactions. In practice, it is almost always beneficial to combine multiple data sources to create more accurate models and gain a deeper understanding of the processes behind the data. We use deep learning and representation learning techniques to solve use cases like the following:

  • Improve demand forecasts for slow-moving and new products using a product similarity graph that accounts for numerical and categorical attributes, macroeconomic and market signals, own and competitor prices, and textual descriptions.
  • Improve product recommendations by combining customer behavior data, textual product descriptions and reviews, product images, and other data sources

Read our technical articles:


Semantic search

The main idea of semantic vector search is to represent both products and queries as semantic vectors in the multidimensional semantic vector space. Products and queries have to be mapped to vectors in such a way that similar products and queries close in meaning would be clustered together.
This is achieved by training a deep learning model based on all available catalog data and customer engagement history mined from the clickstream. The model takes into account all available data about the products, such as attributes, images, descriptions, reviews, prices, and promotions, to find the best possible vector representation.

Read our technical articles:


Interpretable AI


Interpretable AI

Advanced statistical and econometric models can provide deep insights into customer behavior, market demand, and equipment reliability. We have done many enterprise AI projects for our clients and learned that these insights are very important for getting tangible business results. Combining our domain knowledge with advanced technical expertise, we created a toolkit of interpretable models and decision support tools that helps to solve use cases like the following:

  • Understand the structure of the market demand and outcomes of promotion campaigns, taking into account demand cannibalization, pull forward, and halo effects.
  • Understand what drives customers toward conversion, churn, or in-app purchases by using clickstream, demographics, call transcripts, product reviews, and other data sources.

Read our technical articles:


Computer vision


Computer vision

Modern computer vision techniques are extremely powerful and can be applied to a wide range of enterprise problems, including product recommendations, visual search, quality control, and traffic analytics. We have extensive experience with a number of computer vision use cases and have created a comprehensive collection of models, development pipeline templates, and production deployment components that enable us to build end-to-end computer vision solutions with unparalleled productivity.

Read our technical articles:


Conversational interfaces


Conversational interfaces

As people become accustomed to Amazon Alexa or Google Home, they are learning that a conversational user interface (CUI) is an intuitive way of interacting with digital channels. According to data published by Voicebot, the number of Amazon Alexa skills in the US has more than doubled since 2018.
We use state-of-the-art NLP models to solve tasks such as intent classification, entities and relation extraction, and coreference to develop conversational agents.

Read our technical articles:


Digital transformation requires new technology partnerships and Agile co-innovation

Read our latest thinking emerging technology and market trend

Case studies

Personalizing in-game experience using reinforcement learning

– 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

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

Price optimization for video games using machine learning

– 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

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

What we do

Grid Dynamics provides a full range of digital transformation services including consulting, analytics and engineering

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    Emerging technology engineering

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