AI and data platforms (91)

Investment committees ask risk and liquidity questions that traditional books of record can’t answer, especially when private assets report quarterly and derivatives create synthetic exposures that stay invisible in holdings-based views. ABOR and IBOR show what you own and where it’s held, but they...

By Jason Bloomberg and Eric Newcomer, Intellyx Bitemporal data separates when something happened in your business from when your systems recorded it. For financial services firms managing regulatory reporting, audit requirements, and data quality challenges, this architectural approach provi...

As an enterprise leader, you’ve likely seen countless AI prototype demos over the last few years promising empty buzzwords like “transformation”, “efficiency”, and “competitive edge”. But how many of those prototypes actually work in production? Over the past decade, multiple AI hype cycles ha...

A Fortune 500 wealth management firm partnered with Grid Dynamics to boost financial advisor productivity and accelerate decisions with two connected capabilities: a financial services AI copilot for rapid knowledge discovery and an AWS‑native reporting platform for fast, trusted metrics. In plain...

Running agent-based systems across your enterprise comes with tough problems. The main ones are keeping costs down, scaling up fast, and making sure nothing breaks when things go wrong. This white paper gets into the real challenges that come up when teams move from simple agent pilots to a ful...

Discover how insurers can leverage AI to simplify regulatory compliance, optimize pricing, and enhance customer service in today's competitive insurance market.

Against the backdrop of escalating healthcare costs, surpassing $6 trillion by 2028, and a looming shortage of 10 million health workers by 2030, the urgency of 100% digital health integration is stark. This alarming trajectory demands a shift towards prevention and proactive care to curtail skyroc...

In the past decade, the automotive industry has witnessed a revolution, prioritizing an enjoyable and seamless user experience. Whether it's EVs, internal combustion engines, or hybrids, performance and quality have become key value propositions of this automotive evolution. With embedded systems...

MLOps and DataOps principles, such as infrastructure-as-a-code management, continuous integration and continuous delivery, proper monitoring, and a standard approach to working with data assets, are essential components of a modern data estate. In this case study, we show how we helped a global...

In today's data-driven world, efficiently managing data is critical to business growth and competitive advantage. However, many organizations struggle to extract maximum value from their data due to outdated data architectures that limit their ability to store, process, and analyze large volumes of...

The commercial vehicle manufacturing industry has been held back by the limitations of enormous on-premises systems for far too long. And now, since the outset of the global pandemic, and the war in Ukraine, the sense of urgency to maintain availability, adapt to the changing supply chain landsca...

With increasingly data-driven business ecosystems around the world, leading technology, retail, manufacturing, and finance companies turn to Grid Dynamics to build data and machine learning platforms with MLOps processes to translate data into business value, capture substantial value from improved...

A meal kit company that specializes in delivering healthy, organic, ready meal kits was about to start its journey to the cloud. The company was recently acquired by a leader in the food and beverage industry who intended to merge the business under one umbrella, with the same brand, customer man...

The manufacturing industry has been held back by the limitations of enormous on-premises systems for far too long. And now, since the outset of the global pandemic and the war in Ukraine, the sense of urgency to maintain availability, adapt to the changing supply chain landscape, and meet customer...

When it comes to insurance, customers don’t want to feel like their claim is just another drop in the ocean. This presents insurance companies with a monumental task: how to offer a tailored, user-friendly experience that fits each customer’s needs? Especially given that insurance companies have...

Data is the lifeblood of insurance. Having accurate information regarding customers' demographics, medical history, and socioeconomic status allows insurance companies to understand their clients well enough to offer protection tailored to their specific needs. However, times are changing. Insur...

Data quality control is a mission-critical capability for virtually any modern enterprise. Why? Because data quality issues can disrupt business processes and services, invalidate any type of analytics performed in the company, and damage a company's reputation. However, despite the importance of d...

User experience and customer interactions have become critical differentiators, driving a paradigm shift in the insurance industry. As business success is measured by the quality of customer experiences, insurers now have a chance to gain a competitive edge by delivering unparalleled service in thi...

On their quest to future-proof their smart manufacturing operations and create a more connected, predictable environment, Jabil Inc, a leading global manufacturing solutions provider, required a cloud-native data platform to meet these goals. Grid Dynamics, partnering with AWS, took the task hea...

With a new AWS-based solution, Grid Dynamics significantly improved the performance of this top tier agency's advertising efforts which allowed them to enhance their business. Publishers were able to manage the cost of their ads space by defining a more specific audience which would later be sold b...

Modern business intelligence (BI) tools provide users with a wide range of data consolidation, preparation, querying, and reporting capabilities that empower them to make better business decisions. However, the adoption process is non trivial even for the leading BI solutions. In this article...

Every business is focused on a rapid time to market and return on investment. It’s no longer enough to implement a data lake, businesses require a data platform that can provide immediately actionable insights. But building a data platform from the ground up can take a significant amount of tim...

Every company wants to take advantage of their data to turn it into actionable insights. It can be used to build customer 360, reduce customer churn, plan marketing campaigns, and optimize pricing, inventory, and supply chain. Data is also key to increasing productivity and the efficiency of the wo...

Over the last ten years, the role of data in modern enterprise has continued to evolve at a rapid pace. Companies launched initiatives to define and execute their data strategy, appointed Chief Data Officers, and created large teams to collect, manage, and generate insights from data. With increase...

In this article, we detail the results of our performance comparison of popular cloud-based Enterprise Data Warehouses using popular benchmarks. The goal of the investigation was not to do the most comprehensive comparison of them possible, but instead to uncover usable and valuable information to...

We recently built an analytics platform for a startup company whose mobile game acts as a digital advertising platform while providing rewards and instant winning experiences to its users. The app is in the top three in the lifestyle category on Google Play Store and top 10 in the Apple App Store,...

The retail industry is embracing big data, analytics, and machine learning (ML) to improve customer engagement, optimize operations, and drive sales. Increasingly, we’re seeing business intelligence systems that were once based on historic data, offline modeling, and traditional reporting being r...

Modern enterprises increasingly rely on deep learning to power mission-critical workflows such as global demand forecasting, inventory optimization, supply chain prediction, video-based defect detection, and financial risk modeling. These workloads demonstrate rapidly increasing GPU requirements, g...

In a dynamic world where advertising success depends on data-driven decisions, the partnership between Yieldmo, a digital advertising platform, and Grid Dynamics has birthed a groundbreaking solution–a scalable, configuration-driven Machine Learning Platform. This innovative platform addresses...

Co-created by Grid Dynamics Director of Data Engineering, Dmitry Mezhensky, and Yieldmo Head of Analytics and Data Science, Sergei Izrailev Yieldmo, a Grid Dynamics client, is an advertising platform that helps brands improve digital ad experiences through creative tech and artificial intellig...

MLOps and DataOps principles, such as infrastructure-as-a-code management, continuous integration and continuous delivery, proper monitoring, and a standard approach to working with data assets, are essential components of a modern data estate. In this case study, we show how we helped a global...

Adoption of machine learning (ML) methods across all industries has drastically increased over the last few years. Starting from a handful of ML models, companies now find themselves supporting hundreds of models in production. Operating these models requires the development of comprehensive capa...

With increasingly data-driven business ecosystems around the world, leading technology, retail, manufacturing, and finance companies turn to Grid Dynamics to build data and machine learning platforms with MLOps processes to translate data into business value, capture substantial value from improved...

Modern enterprises operate with tremendous amounts of data, and data management has become an integral part of  business decision-making processes, KPI management and reporting. However, getting advanced insights from data and ML models is a sophisticated process that many companies still stru...

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 an...

Personalization and recommendation models are widely adopted by technology and entertainment companies as highly efficient tools for digital experience improvement and conversion optimization. The traditional model development and productization process, however, requires significant engineering ef...

The retail industry is embracing big data, analytics, and machine learning (ML) to improve customer engagement, optimize operations, and drive sales. Increasingly, we’re seeing business intelligence systems that were once based on historic data, offline modeling, and traditional reporting being r...

You can wait for vendors to make token economics more transparent or act now to understand your AI costs, reduce waste, keep innovating, and direct more of your budget toward measurable business value. The choice is yours. Because you chose to act now. This discussion helps you measure the p...

An agentic AI initiative can beat every adoption target and still destroy its own margin. Every prompt, model call, tool invocation, and agent decision adds a variable cost that grows with usage. What looked affordable in a pilot can turn into a real P&L exposure within months, once adoption ex...

A Fortune 500 wealth management firm partnered with Grid Dynamics to boost financial advisor productivity and accelerate decisions with two connected capabilities: a financial services AI copilot for rapid knowledge discovery and an AWS‑native reporting platform for fast, trusted metrics. In plain...

Discover how to transform LLM prototypes into powerful commercial solutions with LLMOps & RAG. Download our free ebook for expert strategies and insights.

The rapid adoption of generative AI in enterprises has created both excitement and challenges for organizations seeking to harness its potential. As companies move from proof of concept (POC) to production, they face numerous obstacles that require expert guidance to overcome. In this context, the...

Originally published on Top Company Guide The automotive industry is facing unprecedented challenges with increasing competition, shifting consumer preferences, and the drive for sustainability. These challenges place immense pressure on manufacturers to innovate and optimize every aspect of the...

Deploying open-source large language models (LLMs) like Mixtral-8x7B, Mistral, and Llama 2 requires a significantly different LLMOps architecture compared to using closed-source models from providers like OpenAI, Google, and Anthropic, even though data vectorization, embeddings creation and observa...

In today's competitive business environment, automation of business processes, especially document processing workflows, has become critical for companies seeking to improve efficiency and reduce manual errors. Traditional methods often struggle to keep up with the volume and complexity of the ta...

Building solutions using closed-source large language models (LLMs), including models like GPT-4 from OpenAI, or PaLM2 from Google, is a markedly different process to creating private machine learning (ML) models, so traditional MLOps playbooks and best practices might appear irrelevant when appl...

The diversity of modern data technologies leads to new challenges in establishing a consistent and accurate data view for data consumers. In light of this issue, a semantic data layer introduces a means of harmonizing a single point of view for business metrics, no matter how many different data...

ITOps is at the heart of today’s enterprise technology innovation teams. Often, they’re tasked with overseeing complex and diverse IT environments and face unprecedented challenges due to the volume and variety of data they encounter.  In this white paper, we explore how an effective AIOps...

A global leader in business and consumer credit reporting and fraud prevention services partnered with Grid Dynamics to modernize how it communicates with millions of customers in real time. Every delayed or missed customer-facing, event-triggered notification can erode trust, disrupt experiences,...

From retail to manufacturing, and from financial services to healthcare, every industry is eager to capitalize on the potential of artificial intelligence. But AI-ready data is essential to realizing that promise. To truly unlock that potential, AI solutions for enterprises must be built on a fou...

Most enterprise leaders dip their toe into AI, only to realize their data isn’t ready—whether that means insufficient data, legacy data formats, lack of data accessibility, or poorly performing data infrastructure. In fact, Gartner predicts that through 2026, organizations will abandon 60% of AI pr...

Explore how AI, Agentic workflows, and cloud-native technologies are reshaping insurance underwriting, claims, and risk management in 2025.

An average company adopting cloud today could achieve 180% ROI in business benefits, although few are getting close to these returns.  Migrating or replatforming to a cloud data platform is a complex process. Even a basic "lift-and-shift" migration requires careful planning—designing the target...

How telemedicine, the IoMT, integrated healthcare data, digital health commercialization, and GenAI-powered drug R&D will shape the year ahead.

With the recent advancements in generative AI, and the ongoing explosion of multimodal biomedical data from large biobanks, electronic health records, medical imaging, and biosensors, it is now possible to capture the complexity of human health and disease more accurately. These breakthroughs a...

Without data observability in their production pipeline, this Fortune 500 manufacturer encountered multiple data issues that drastically impacted time-to-market, added significant development overhead, and complicated the product development roadmap. A data observability solution simplified, accele...

Originally published in Forbes Enterprise companies accumulated massive amounts of data over the years in pursuit of the promised rewards of big data. The question that now often comes from management is, “There must be some value, somewhere—can’t you find it?” The data democratization moveme...

Data quality control is a mission-critical capability for virtually any modern enterprise. Why? Because data quality issues can disrupt business processes and services, invalidate any type of analytics performed in the company, and damage a company's reputation. However, despite the importance of d...

Having complete control over data quality can seem daunting and prohibitively expensive. But is that really true? Is it as difficult as it seems? There are many tools out there in the marketplace to help with data quality, but it’s true that many of them are pricey, have a steep learning curve,...

It’s no secret that leading a healthy lifestyle improves your overall quality of life. A healthy lifestyle involves following a good diet, limiting excessive alcohol or smoking, taking the time to exercise, and getting enough sleep. This same philosophy can be applied to the way your business t...

After the initial excitement that data lakes would help companies maximize the utility of their data, many companies became disillusioned by rapidly diminishing returns from their big data efforts. While it was easy to put large volumes of data in the lakes, turning that data into insights and real...

In an era marked by growing velocity, volume, and variety in data, managing this constant influx of competing data points and information is crucial for businesses aiming to stay ahead. Data platforms help achieve this—but building and customizing them is key to success. In our experience workin...

The quarterly report showed a 40% drop in sales over the last quarter. The executive team panicked. The entire company is collapsing! Or is it? After the investigation concluded, they discovered that the sales were okay. The report was incorrect because an automated system had failed. The infrast...

In early 2016 one of our online media customers came to us with a problem. Our customer, a media giant, hosts articles from its newspapers and magazines on its websites. Each of the articles’ web pages has three ad blocks, and the customer buys paid redirects to the article pages. They then analyze...

In this blog post we share our experience delivering deduplicated data during In-Stream Processing for a large-scale RTB (real time bidding) platform. A common problem in such systems is the existence of duplicate data records that can cause false results when processed by the analytic queries. One...

In the previous blog post we went through the details of how to set up the Docker infrastructure with Mesos and Marathon, and how to bootstrap the environment to get it ready to host application services. Now it is time to deploy some applications. Our blog’s purpose is not only to show you a fi...

In the previous post we discussed which models we tried for sentiment classification and which one has demonstrated the best performance. In this post, we’ll show you how to visualize our under-the-hood findings so that others can see the results of our analysis. You can see our twitter senti...

In the previous post we discussed how we created an appropriate data dictionary. In this post we’ll address the process of building the training data sets and preparing the data for analysis. The training process aims to reveal hidden dependencies and patterns in the data that will be analyzed...

In the previous post we discussed the structure of the tweet data. In this post we’ll address the process of selecting or building the right data dictionary for our purpose. What constitutes a good dictionary? A crucial data set for any kind of text mining is a dictionary. As for sentiment...

In the previous post we outlined the basic scientific method used and formalized the problem statement we are solving, which is, “Based on of the tweets of English-speaking population of the United States related to selected new movie releases, can we identify patterns in the public’s sentiments t...

This article introduces the Grid Dynamics blueprint for in-stream processing. It is based on our experience and the lessons we have learned from multiple large-scale client implementations. We have included cloud-ready configuration examples for Apache Kafka, Spark...

This post contains a brief survey of better-known products related to in-stream processing that are available on the market at the time of this writing. In this survey, we focus specifically on critical architectural differentiations, rather than functional differences, that affect why custome...

Now that we have introduced the high-level concepts behind In-Stream Processing and how it fits into the Big Data and Fast Data landscapes, it is time to dive deeper and explain how In-Stream Processing works. As we already know, In-Stream Processing is a service that takes events as input and...

In-stream processing is a powerful technology that can scan huge volumes of data coming from sensors, credit card swipes, clickstreams and other inputs, and find actionable insights nearly instantaneously. For example, in-stream processing can detect a single fraudulent transaction in a stream...

You can wait for vendors to make token economics more transparent or act now to understand your AI costs, reduce waste, keep innovating, and direct more of your budget toward measurable business value. The choice is yours. Because you chose to act now. This discussion helps you measure the p...

An agentic AI initiative can beat every adoption target and still destroy its own margin. Every prompt, model call, tool invocation, and agent decision adds a variable cost that grows with usage. What looked affordable in a pilot can turn into a real P&L exposure within months, once adoption ex...

A leading sleep solutions retailer built an AI sales enablement assistant to give more than 6,000 store associates instant access to product knowledge, promotions, and coaching. Adoption grew fast. Visibility into production performance did not keep pace. Golden datasets and manual log reviews were...

With the rapid growth of AI agent usage in production, Grid Dynamics began facing challenges in determining whether agents were healthy, identifying abnormal behavior, and understanding when agent behavior deviated from expected outcomes. This article provides an actionable approach to building eval...

Ask five people in your organization what AI agent evaluation means, and you will get five different answers. Product teams track conversions and outcomes. Engineers test prompts and tool calls. SREs monitor uptime, latency, and token usage. Security teams run red-team exercises. Everyone evaluates...

A leading global payments technology company needed a practical way to deploy AI agents across regulated business domains without sacrificing control, transparency, or reliability. The organization partnered with Grid Dynamics to implement a production-safe agentic AI platform that standardizes how...

As AI agents move from experimentation to enterprise deployment, the real opportunity lies in scaling them with reliability, observability, and governance. A Fortune 500 global manufacturer operating more than 100 plants worldwide recognized this early and set out to turn decades of distributed kno...

Agentic AI in financial services now touches fraud, AML, onboarding, investment suitability, and servicing. But with 76% of firms planning to implement agentic AI within the next year, the hard part is not the models themselves; it’s ensuring that the data, controls, and integration patterns with e...

Choosing the right agentic AI framework matters. Crew AI, Google ADK, LangGraph, and OpenAI Agents SDK each solve different problems, from rapid multi-agent prototyping to durable, stateful workflows and cloud-native enterprise agentic AI deployments.  This comprehensive white paper examine...

As an enterprise leader, you’ve likely seen countless AI prototype demos over the last few years promising empty buzzwords like “transformation”, “efficiency”, and “competitive edge”. But how many of those prototypes actually work in production? Over the past decade, multiple AI hype cycles ha...

Agents of change are here. Agentic AI is rewriting the rules of retail, manufacturing, financial services, and insurance, making workflows autonomous, compliant, and customer-first.

Running agent-based systems across your enterprise comes with tough problems. The main ones are keeping costs down, scaling up fast, and making sure nothing breaks when things go wrong. This white paper gets into the real challenges that come up when teams move from simple agent pilots to a ful...

From retail to manufacturing, and from financial services to healthcare, every industry is eager to capitalize on the potential of artificial intelligence. But AI-ready data is essential to realizing that promise. To truly unlock that potential, AI solutions for enterprises must be built on a fou...

For technical leaders seeking a comprehensive understanding of Agentic AI technology—including architectural innovations, implementation frameworks, and detailed technical guidance—download the full white paper for an in-depth analysis, technical deep dive, and actionable strategies to accelera...

Wealth managers, asset managers, and banks face mounting pressure when it comes to regulatory remediation—the process of responding quickly and accurately to inquiries from regulators like FINRA and the SEC. These demand answers to complex questions about past transactions, communications, and deci...

Discover how to transform LLM prototypes into powerful commercial solutions with LLMOps & RAG. Download our free ebook for expert strategies and insights.

Deploying open-source large language models (LLMs) like Mixtral-8x7B, Mistral, and Llama 2 requires a significantly different LLMOps architecture compared to using closed-source models from providers like OpenAI, Google, and Anthropic, even though data vectorization, embeddings creation and observa...

In today's competitive business environment, automation of business processes, especially document processing workflows, has become critical for companies seeking to improve efficiency and reduce manual errors. Traditional methods often struggle to keep up with the volume and complexity of the ta...