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CTERA announced CTERA Forward Deployed Engineering on October 6, a service that places CTERA engineers inside customer environments to put specific AI use cases into production on the customer’s own unstructured file data. Each engagement is tied to a defined business outcome, runs against real enterprise file data, and is designed to leave the customer’s team able to run and extend the work once CTERA steps out. CTERA’s pitch is that most enterprises now have capable AI models and tools, and far fewer have AI producing reliable results in production. It cites Futurum’s 1H 2026 Data Intelligence Decision Maker survey of 818 data leaders, which found that a shortage of specialized talent is the fastest-rising factor behind AI project failures, and that insufficient skills and expertise as an organizational challenge more than doubled in six months. File data is where CTERA says the gap is widest. File estates built up over decades are often duplicated, stale, and inconsistently permissioned, and AI pointed at them without preparation can return confident but wrong answers or surface files to people who should not see them, two failures that undercut trust in an AI rollout. Brad Shimmin, vice president and practice lead for data intelligence, analytics, and infrastructure at Futurum Group, tied the service to his firm’s research: “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.” A Four-Phase Engagement Each engagement is staffed by a small team pairing a Forward Deployed Engineer with a Deployment Strategist, working as part of the customer’s project team with access to its environments and to the subject-matter experts who know how the work gets done. Engagements run in four phases: discovery and data grounding, a first production use case, scaling across additional workflows, and handover for independent operation. Discovery starts with the file estate itself, assessing volumes, access patterns, and source systems to check the proposed use cases against the data. The team then identifies the data sets each workflow needs and classifies, organizes, and governs them in place, so the AI works only with content each user is already authorized to access. Workflows connect to the systems and AI tools the customer already uses through open standards such as the Model Context Protocol and automation platforms such as n8n, which CTERA integrated with in June, and are validated against real data before go-live. Customer engineers work alongside CTERA from the first phase and document the configuration as it is built, and CTERA considers an engagement complete when the customer can add the next use case without it. The service runs on the CTERA Intelligent Data Platform, which provides AI-governed access to file data across edge locations, data centers, and cloud environments without moving it. It follows the AI-assisted archiving solution CTERA launched last month on its InsightAI engine, and extends the same platform from software into hands-on delivery. CTERA CEO Oded Nagel framed the service around time and skills: “Enterprises aren’t short of AI tools. What they’re short of is the time and specialized skills to connect those tools to their own data and workflows safely. 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.” Availability CTERA Forward Deployed Engineering is available now for organizations running the CTERA Intelligent Data Platform. Engagements can be structured on a time basis or as fixed-scope projects with milestone-based acceptance criteria, and CTERA has not published pricing for either model.
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