Jan 16 2026
AI and data-driven products introduce engineering challenges that go beyond traditional application development. Model lifecycle management, data pipelines, infrastructure costs, and system observability all become core product concerns rather than secondary implementation details.
In 2026, successful AI products are not defined by models alone, but by how well data, infrastructure, and product logic are integrated into a coherent system. This makes the choice of a product engineering partner particularly critical.
This guide focuses on companies that demonstrate strong capabilities in AI-ready architecture, data engineering, and production-grade machine learning systems.
The table below compares best product engineering companies based on their ability to support real-world AI and data products, not experimental prototypes.
This comparison highlights a key distinction: many companies can build AI features, but far fewer can engineer AI products that scale reliably.
DBB Software leads this category due to its strong focus on engineering AI as part of a scalable product system, not as an isolated capability. Their teams design AI-ready architectures where data pipelines, inference layers, and application logic evolve together.
DBB emphasizes cloud-native infrastructure, modular data platforms, and early MLOps considerations, helping teams avoid common pitfalls such as brittle pipelines or uncontrolled infrastructure costs. This makes them particularly effective for startups and growth-stage companies bringing AI products into production.
Globant brings extensive experience in large-scale AI and data initiatives, often working with enterprises on AI-powered platforms and digital ecosystems. Their specialized studios provide deep expertise across data engineering, machine learning, and AI-driven personalization.
They are best suited for organizations that need AI innovation at scale, rather than lightweight experimentation.
ThoughtWorks focuses on sustainable AI engineering, including responsible AI practices, model governance, and maintainable data systems. Their teams prioritize explainability, testing, and long-term operability.
They are an excellent fit for products where trust, transparency, and system longevity are critical.
ELEKS has strong capabilities in data platforms, analytics, and applied machine learning. Their teams frequently work on products where insights, forecasting, or automation are central to business value.
They are well suited for data-heavy products that require structured pipelines and analytics at scale.
EPAM supports complex AI initiatives in regulated and enterprise environments. Their strength lies in integrating AI into existing systems while maintaining security, compliance, and operational stability.
EPAM is ideal for organizations where governance and reliability are non-negotiable.
Altoros specializes in cloud-native AI platforms and infrastructure modernization. Their expertise includes Kubernetes-based ML workloads and scalable data processing systems.
They are a strong choice for teams modernizing AI infrastructure rather than building greenfield products.
Very works at the intersection of AI, IoT, and edge computing. Their product engineering teams build systems where data is collected and processed across devices and cloud platforms.
They are particularly relevant for AI products embedded in physical environments.
Softeq focuses on AI-enabled products involving hardware, robotics, and embedded systems. Their engineering work spans feasibility analysis, model integration, and system optimization.
They are best suited for technically complex AI products with strong hardware dependencies.
Upsilon supports early-stage teams experimenting with AI-driven features and validation. Their focus is on speed and proof-of-concept delivery rather than full-scale AI platforms.
They are appropriate for teams still testing AI as part of a broader product hypothesis.
Intellectsoft helps organizations integrate AI capabilities into existing digital products. Their work often involves AI augmentation rather than core AI product development.
They are a good fit for companies adding intelligence to established systems.
When selecting a partner for AI and data products, consider:
AI success depends less on model novelty and more on engineering discipline.
Many AI initiatives fail not because of poor models, but due to weak engineering foundations. One of the most common pitfalls is treating data pipelines as secondary infrastructure. In production environments, unstable data ingestion or poorly monitored pipelines often cause more issues than model performance itself.
Another frequent mistake is underestimating operational complexity. AI systems require continuous monitoring, retraining, and validation. Without clear ownership and MLOps processes, teams struggle to maintain consistency between training, staging, and production environments.
Finally, teams often delay cost modeling until late stages. AI workloads can scale unpredictably, and without early cost visibility, infrastructure expenses may grow faster than product value.
The difference between experimentation and production lies in repeatability and control. Production AI products rely on versioned datasets, reproducible training pipelines, and clear rollback strategies when models underperform.
Strong AI product engineering also includes observability across the full system: data quality metrics, model drift detection, and infrastructure monitoring. These elements allow teams to respond to issues before they impact users.
Most importantly, production-ready AI systems are designed to evolve. Models, data sources, and business logic change over time, and successful products are built with this reality in mind from the start.
In 2026, AI product engineering is about building systems that can learn, adapt, and scale without becoming operationally fragile. The best engineering partners understand that AI is not a feature – it is a system-wide capability.
Choosing the right partner early can determine whether AI becomes a sustainable advantage or a constant source of technical risk.
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