Xebia & Snowflake: Turning Trusted Data into Real-Time and AI-Ready Insight

Xebia has been a Snowflake partner since 2022. Since then, our work has moved well beyond proofs of concept. We have built real-time streaming platforms for companies that cannot afford to run on yesterday's data, migrated regulated businesses that cannot afford to get governance wrong, and designed data lakehouse architectures that hold up under real production load. We are now building on that foundation to let business users interact with governed data in natural language, prepare data platforms for AI agents, and augment our own engineering expertise with AI to deliver them faster and more effectively. This foundation is what makes governed AI and agent-driven use cases possible. 

In this post, we explore what this delivery experience has taught us and the three areas where Xebia creates distinctive value on Snowflake: real-time streaming, natural-language access to data, and open data lakehouse architecture. 

These capabilities increasingly converge around the same challenge: building trusted, governed data foundations that work not only for people and analytics, but for AI agents too. We’ll also look at how Axis combines Xebia’s engineering expertise with AI-powered delivery, proven patterns, and accelerators to help organizations build these agent-ready foundations faster.

Real-Time Streaming on Snowflake: From Yesterday’s Data to Decisions in the Moment

Most data platforms still run on yesterday's data. Insight waits for the overnight batch, and by the time a dashboard updates, the moment to act on it has often passed. For many businesses that is an acceptable trade-off. For payments, energy markets, and logistics, it is not. In these industries the difference between "now" and "this morning" is the difference between a good decision and a missed opportunity. 

Real-time streaming is one of Xebia’s most distinctive strengths on Snowflake. For a leading global energy company operating in real-time power markets across multiple countries, we migrated data pipelines from BigQuery to Snowflake and dbt while introducing an event-driven architecture with Kafka and Snowpipe Streaming, supporting 15-minute energy pricing windows. At Abacai, a UK motor insurer running 15 business domains on a single Snowflake platform, we added streaming ingestion through Kafka and Snowpipe alongside batch loads, so claims and underwriting data can move in near real time across the platform. (Read the Abacai customer story) 

Real-time doesn't have to mean a separate streaming stack bolted onto the warehouse. By using Snowflake-native streaming capabilities alongside technologies such as Kafka, we can keep real-time data within the same governed platform as batch and analytical workloads. Reducing the need for separate infrastructure and operational overhead. 

Talk to Your Data: Natural-Language Analytics on Snowflake 

The next challenge is not getting more data, but making the data businesses already have easier to use. Dashboards and SQL still leave many business questions dependent on analysts and technical teams. Business users, meanwhile, want to ask questions in plain English, receive grounded answers, and continue the conversation as new questions emerge. Without requesting another report or waiting for a new dashboard.

On Snowflake, this experience can be built directly on the enterprise data platform. Cortex Analyst allows business users to query structured data in natural language and supports multi-turn conversations that retain the context of previous questions. Cortex Search extends natural-language access to unstructured content, and Snowflake Intelligence provides a conversational interface for working with data and AI agents.  

What makes these capabilities valuable in an enterprise setting is what sits underneath them: a governed semantic layer that gives AI the business context it needs to interpret data more reliably. We’ve already piloted this at Abacai, where an early Snowflake Agents proof of concept lets business users query claims and underwriting data through semantic views instead of writing SQL or waiting on an analyst. We have also applied this conversational analytics approach in the investment industry, demonstrating its relevance across different types of enterprise data and business questions. The result is a more direct path from a business question to a trusted answer, in the user’s own words and without adding another request to an analyst’s queue.

Open Data Lakehouse: Bringing Openness and Governance Together 

The third area is how organizations design for openness without compromising governance. Organizations have traditionally had to balance the flexibility and openness of a data lake with the performance and governance of a data warehouse. An open data lakehouse on Snowflake brings those capabilities closer together, providing a governed foundation for structured, semi-structured, and unstructured data while supporting open and interoperable architectures. 

In one of our projects, we built a single Snowflake platform spanning 15 business domains across the full insurance lifecycle, using infrastructure as code with Terraform, 50+ RBAC roles, and streaming ingestion alongside batch loads. In another project in the energy and utilities industry, we explored the open side of this pattern through a proof of concept using Snowflake and Apache Iceberg tables. The project tested how organizations can combine open, portable storage with Snowflake's governance and performance capabilities. Whether the priority is governance at scale or keeping data open and interoperable, the goal is the same: avoid creating another silo while preserving the flexibility to support future platforms, tools, and AI use cases.

Explore Xebia’s Open Data Lakehouse approach.

Xebia Axis: Engineering Agent-Ready Data Foundations

Streaming, talk to your data, and the open data lakehouse all point toward the same next challenge: most data foundations were built for humans, not for AI agents. When an AI agent encounters poorly documented data, inconsistent definitions, or missing business context, it has little reliable context to reason over, limiting the quality and trustworthiness of its answers. 

Xebia Axis brings these capabilities together through Xebia’s approach to building agent-ready data foundations. It combines governed data, consistent business definitions, and semantic layers with the engineering skills, proven patterns, and accelerators needed to put them into practice. At the same time, Xebia Axis uses AI to augment the work of our data engineers, helping them assess, build, and migrate data platforms more efficiently.

Rather than a one-size-fits-all product, Xebia Axis is adapted to each organization's data environment, building on the migration, platform engineering, and governance work we already deliver today. 

Discover how Xebia Axis accelerates the journey to agent-ready data foundations.

Why the Xebia-Snowflake Partnership Is Different 

Xebia brings a clear focus to the Snowflake partnership: solving the challenges where deep engineering expertise makes the greatest difference. These are the situations when data has to move in real time, when business users need to interact with it directly, and when the architecture underneath needs to be governed, AI-ready, and built to scale.

Our expertise comes not only from implementing Snowflake for customers, but also from contributing to its wider engineering ecosystem. Xebia maintains a widely adopted suite of open-source Terraform modules for Snowflake, with tens of thousands of downloads, and has contributed to the official Snowflake Terraform Provider.

We bring proven customer outcomes, including a production data platform built in four months for Volt.io, a global real-time payments company. Read the Volt.io customer story. We also bring experience from projects across e-commerce, financial services, energy, insurance, and other industries.

Combined with Snowflake’s platform capabilities, this delivery and engineering experience helps organizations move from ambition to secure, production-ready solutions and measurable business outcomes.

Where This Goes Next 

Real-time streaming, talk to your data, the open data lakehouse, and Xebia Axis are increasingly connected. Fresh data matters more when business users and AI agents can act on it directly. Natural-language access becomes more valuable when it is grounded in governed data and clear business context. And open architecture gives organizations the flexibility to keep evolving as new AI use cases emerge. 

For Xebia and Snowflake, the next step is to bring these capabilities together for more customers and continue turning what we learn from real-world delivery into repeatable approaches. 

If you are running data on Snowflake and facing any of these challenges, whether you need fresher data, simpler access to insight, or a more open and AI-ready foundation, we would be glad to talk about what comes next. Talk to our Snowflake experts.