Seeq has launched Seeq Intelligence, adding agentic AI capabilities to its industrial analytics platform in a bid to accelerate operational decision-making across process and manufacturing environments.
The new offer bundles Seeq’s existing Enterprise package with additional “agent” functions designed to pull together real-time operational context, prior analyses, unstructured documentation, and decision history. The company’s framing is direct: industrial operations are managing more complexity with fewer experienced hands, while critical knowledge is often trapped in local workbooks, individual expertise, and siloed systems.
At the centre of the launch is Agent Q, described as a domain-aware AI analyst accessed through natural language. Seeq says the tool can assemble investigations spanning historical operational events, prior actions, documents, and know-how, returning traceable intelligence and prioritised recommended actions. Alongside Agent Q, Seeq Intelligence also introduces a “Build Your Own Agent” capability to automate repeatable workflows, with agents able to run on demand or through schedules and triggers.
Another element is “Agent Extensibility”, which Seeq positions as a secure way to connect Seeq agents to customer systems and information to retrieve up-to-date context — such as recent data windows or work orders — and to initiate workflows across those systems. A separate “Document Access” feature targets the persistent gap between plant data and the written layer of operations, enabling extraction and synthesis from unstructured or semi-structured documents into contextualised outputs for Q&A and summarisation.
Mark Derbecker, Chief Product Officer at Seeq, said: “Seeq Intelligence represents a step change in how industrial companies create value. By synthesizing context, history, and irreplaceable domain expertise with patented advanced AI, we’re giving organizations a continuously learning system that sharpens decision making and accelerates operational transformation. It’s about helping customers compete — and win — in a world where speed, insight, and adaptability define future leaders.”
The launch lands in a market that is rapidly converging around “industrial copilots” and workflow automation on top of time-series analytics stacks. The practical constraint has been less about model capability and more about grounding, data access, and repeatability: connecting to historians and event frames is one thing; connecting to the decisions that followed — and the documents that codified them — is where adoption either sticks or stalls.
Seeq’s approach is to make those repeatable methods explicit. In its product documentation, the company describes agent-building as chaining multi-step workflows, tools, and Seeq context into guided flows that can be invoked repeatedly, turning plant “one-offs” into reusable operational playbooks.
Matthew Littlefield, President and Research Lead at LNS Research, said: “Seeq Intelligence is a notable step forward in the fast-moving Industrial AI ecosystem. Agent Q can reach across the broad stack of operational technologies, incorporate the expertise and context contained in Seeq, and provide the agentic layer needed to change the speed, quality, and strategic priority of decisions.”
