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
RETAIL
HI-TECH
MANUFACTURING & CPG
FINANCE & INSURANCE
7/25
largest US retailers
4/10
largest US technology companies
3/10
world’s largest consumer goods companies
2/10
largest US financial companies
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:
- Customer intelligence and personalization
- Supply chain optimization
- Building a next best action model using reinforcement learning
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:
- Customer2vec: Representation learning for customer analytics and personalization
- How deep learning improves recommendations for 80% of your catalog
Semantic search
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:
- Building a reverse image search engine with convolutional neural networks
- Virtual backgrounds: Real-time deep semantic segmentation on mobile devices
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:
- Virtual backgrounds: Real-time deep semantic segmentation on mobile devices
- Building a question answering system for online store
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
To improve the gaming experience for players, a leading video game publisher sought to personalize in-game interactions, streamline model development, and increase long-term engagement and lifetime value (LTV) of users. Grid Dynamics addressed these challenges by implementing a reinforcement learning-based personalization platform, successfully delivering a minimum viable product (MVP) within just 8 weeks.
This innovative solution significantly improved user engagement, achieving up to a 25% increase in dollar-per-user metrics compared to existing baselines. The result is a more immersive and tailored gaming experience that not only captivates players but also fosters lasting loyalty and value for the publisher.
Price optimization for video games using machine learning
To optimize promotions across various channels and countries, a video game publisher encountered challenges in accurately forecasting demand 24 months ahead and effectively managing new game releases. Grid Dynamics addressed these issues by developing robust demand forecasting models and what-if analysis tools for promotion scenarios, successfully delivering a minimum viable product (MVP) in just 6 weeks.
This strategic solution transformed the publisher’s operations by replacing manual processes with data-driven optimization, resulting in significantly increased promotion efficiency compared to existing baselines. The outcome is a more streamlined approach to promotional strategies, enhancing the publisher’s ability to align with market demands and improve overall sales performance.
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