CTERA launches engineering service for enterprise AI

CTERA has launched Forward Deployed Engineering, a service that places its engineers inside customer teams to move AI use cases into production using organisations' own unstructured data.

Available to users of the CTERA Intelligent Data Platform, the service is built around specific business outcomes. CTERA engineers work with customer teams to prepare governed data, build AI workflows around existing processes and transfer responsibility so customers can run and extend those workflows themselves.

The launch addresses a common problem in corporate AI projects: many businesses have access to AI models and tools but struggle to deploy them in reliable production environments. CTERA identifies the main barriers as a lack of specialist engineering talent, data infrastructure and integration expertise, particularly where large stores of file-based information have accumulated over many years across different systems.

The issue is especially acute with unstructured data, which often sits in duplicated, outdated or inconsistently permissioned file estates. For companies trying to connect AI systems to those repositories, mistakes in data preparation or access controls can lead to inaccurate responses or expose information to unauthorised users.

Research cited by CTERA comes from Futurum's first-half 2026 Data Intelligence Decision Maker survey of 818 data leaders. The findings show that a shortage of specialised talent is the fastest-rising factor behind AI project failures, while the challenge of insufficient skills and expertise more than doubled over a six-month period.

Brad Shimmin, Vice President and Practise Lead, Data Intelligence, Analytics and Infrastructure at Futurum Group, linked those issues to broader architectural and operational constraints inside enterprises.

"Enterprises are struggling to operationalise AI because they lack enough specialised engineering talent, data infrastructure and integration expertise to do it," said Brad Shimmin, Vice President and Practise Lead, Data Intelligence, Analytics and Infrastructure at Futurum Group. "Our research shows MLOps complexity, integration issues and talent shortages are the top three data-related factors in AI project failures, and that more than 90% of enterprises face architectural bottlenecks when building AI agents. Forward-deployed engineering teams address those failure factors directly, while filling the architectural gaps most enterprises face."

How it works

Each engagement is handled by a small team consisting of a Forward Deployed Engineer and a Deployment Strategist. They work within the customer's project structure alongside internal subject-matter experts, with access to operational environments and the data needed to validate a chosen use case.

The delivery model follows a phased approach. It starts with discovery and what CTERA describes as data grounding, moves to a first production use case, expands to additional workflows and ends with a handover to the customer team.

In practice, that means assessing the customer's file estate, including data volumes, access patterns and source systems, before identifying the datasets needed for a workflow. CTERA then classifies, organises and governs that data in place while keeping AI access aligned with existing user permissions.

The service is designed to fit into systems customers already use rather than replace them. Workflows can integrate with current software and AI tools through open standards, including the Model Context Protocol, and automation platforms such as n8n, with testing against live enterprise data before launch.

A central part of the model is the transfer of ownership. Customer engineers work alongside CTERA from the start and document configurations as they are built, with the engagement concluding once the customer can develop the next use case independently.

Data focus

The service builds on the CTERA Intelligent Data Platform, which provides governed access to enterprise file data across edge locations, data centres and cloud environments without requiring the data to be moved. That places the service within a broader market effort to make old and fragmented data estates usable for newer AI tools while preserving internal access rules.

For CTERA, the technical challenge in AI deployment is no longer limited to model choice. It is increasingly tied to identifying relevant data, connecting it to operational processes and ensuring internal teams can support it once outside engineers leave.

Oded Nagel, Chief Executive Officer of CTERA, said the company sees a gap between the availability of AI software and the practical work needed to connect it to real business operations.

"Enterprises aren't short of AI tools. What they're short of is the time and specialised skills to connect those tools to their own data and workflows safely," said Oded Nagel, Chief Executive Officer of CTERA. "Forward Deployed Engineering puts our engineers alongside customer teams to do that work, get a real use case into production and leave the customer able to run it themselves."