What Xebia’s OpenAI Select Partner Status Means for Enterprise AI

At a Glance

From AI Access to Enterprise Implementation

As generative AI capabilities improve, access to advanced models is becoming less of a differentiator. For many enterprises, the harder questions now concern implementation.

Which business processes should use AI? What data should models be able to access? How should permissions be enforced? Where should humans remain involved? How should organizations monitor quality, cost, and adoption?

These are questions about architecture, engineering, governance, and operating model.

OpenAI’s Services Partner guidance describes services firms as helping business customers implement ChatGPT and API capabilities. It also distinguishes between using OpenAI models and implying that a solution was jointly developed with OpenAI. 

What Xebia Brings to Enterprise AI

Xebia’s contribution sits mainly around the model.

Its Data & AI practice reports nearly 400 clients, with 70 industry-specific accelerators and a team of 800 professionals across the Americas, Europe, Asia Pacific, the Middle East, and Africa. Its portfolio includes Xebia Axis, an agentic data foundation, and Xebia Ace, focused on AI-native engineering. 

The company also has industry-focused solutions in financial planning, retail master data management, customer service, HR, airline customer profiles, demand forecasting, and risk analysis.

That portfolio matters because enterprise AI rarely operates as a standalone model deployment. Production systems typically also require data integration, identity and access management, application development, observability, governance, and workflow design.

The value of AI in the enterprise is not determined by the model alone. It depends on how well that intelligence is connected to data, applications, workflows, and the engineering discipline required to operate it at scale. Our focus is on helping organizations turn advanced AI capabilities into systems that are useful, governed, and measurable in production.

Preetpal Singh

Global Head of Product and Platform Engineering - Xebia

Production Examples

One Xebia implementation involved a global consumer goods company with information spread across SharePoint, Power BI, Databricks, product lifecycle management systems, documents, dashboards, and audio and video sources. 

Xebia developed an enterprise knowledge platform that used multimodal parsing, retrieval-augmented generation, access controls, quality monitoring, and governance. The system also included prompt validation, PII protection, compliance checks, hallucination detection, tracing, and business KPI measurement. 

According to Xebia's technical assessment, the implementation reduced knowledge discovery time from about 40 hours to two hours and governed access to more than 25,000 files. 

A second example involved a Dutch insurer processing roughly 7,000 claims per month. Xebia developed an AI-based claims assistant using Azure AI Search, Azure OpenAI, and OpenAI models. The solution helped employees retrieve relevant knowledge and compare historical claims. 

The assessment reports approximately €600,000 in annual savings from time reductions and more than €4.5 million in identified FTE efficiency gains. 

These examples illustrate a broader point: the language model is only one component of a production AI system. 

Why Data and Governance Matter

As organizations move from simple assistants toward agentic systems, integration requirements increase. 

AI systems may need to retrieve information from multiple sources, call enterprise tools, generate structured outputs, or participate in multi-step workflows. That requires controls around what the system can access, what actions it can take, how errors are detected, and how activity is monitored. 

Xebia's existing architectures include access filtering, PII safeguards, compliance checks, hallucination monitoring, cost and quality observability, and business KPI tracking. 

Data remains another major constraint. Enterprise information is often fragmented across databases, documents, analytics platforms, and legacy applications. Without a usable data foundation, capable models may still struggle to provide reliable answers or complete business processes. 

Xebia's broader data engineering work, including self-service and agentic data platforms, is therefore relevant to the OpenAI relationship. 

What the Select Partner Designation Changes

The OpenAI Select Partner designation strengthens the work Xebia was already doing.

Xebia had already used OpenAI models in production before receiving the designation. Its technical assessment documents implementations involving enterprise knowledge discovery and insurance claims processing using OpenAI models. 

What changes is the structure around how Xebia works with OpenAI technologies and supports customers adopting them.

For enterprises, the value of the relationship will ultimately be measured by execution rather than the designation itself.

The relevant outcomes are whether AI projects move into production, integrate with existing systems, operate within appropriate controls, and produce measurable improvements in productivity, cost, speed, or customer experience.

Xebia’s existing implementations provide evidence that it has addressed some of those challenges. The next step is applying that experience more broadly as organizations increase their use of OpenAI models and products across the enterprise.