Highlights
- Field Service is ConforgeLabs' visual industrial intelligence platform for high-pressure equipment such as boilers, pressure vessels, heat exchangers, piping and rotating machinery.
- Three AI agents work together: the Inspec Agent reads failure images and flags similar past cases, the Analyzer Agent builds the structured root cause case, and the Service Agent answers questions from an organization's own SOPs and maintenance records.
- Every investigation starts from existing knowledge and similar historical RCA cases instead of a blank page.
- A human reviewer checks and approves every AI-drafted case in an auditable dashboard before it becomes final.
- Teams load their own SOPs, SMPs and protocols into the Knowledge Base, so answers reflect their own standards, not generic internet knowledge.
- Approved investigations are saved and resurfaced automatically the next time a similar failure happens, building institutional memory over time.
- Field Service sits inside ConforgeLabs' broader manufacturing intelligence platform, alongside warranty, supplier, RFQ, engineering drawing and inventory intelligence, connected to ERP, MES, PLM, quality and maintenance systems.
The problem: failure data exists, but the intelligence is scattered
When high-pressure industrial equipment fails, the facts needed to diagnose it are rarely in one place. A technician may have a photo of the damage. Equipment history sits in the ERP system. Inspection records are buried in maintenance documents. The right SOP lives in a separate knowledge repository. The root cause of the last similar failure may exist only in an old investigation report.
Every minute spent hunting for that information delays diagnosis, corrective action and the return to reliable operations. Field Service, ConforgeLabs' visual industrial intelligence and asset reliability system, is built to close that gap for high-pressure industrial equipment.
How Field Service works: three AI agents, one investigation
Instead of starting every investigation from scratch, teams upload a failure image and the related equipment data. Field Service checks the image quality, interprets the failure mechanism and surfaces similar historical RCA cases, so the investigation begins with what the organization already knows rather than a manual search through years of records.
Three specialized agents carry that investigation from evidence to action:
- Inspec Agent: analyzes uploaded failure images and related information, checks evidence quality, identifies the failure mechanism and surfaces similar past cases.
- Analyzer Agent: combines material details, visual analysis, failure mechanisms, likely root causes, inspection priorities and corrective actions into one structured case, saving progress automatically as it goes.
- Service Agent: answers plain-language questions against the organization's own SOPs, maintenance practices, protocols and past RCA reports, replacing manual searches through scattered documents.
Together, the three agents turn the question from "What does this image show?" into "What does this failure resemble, what caused similar failures before, and what should we investigate next?"
AI-assisted, not AI-only
Industrial reliability decisions carry real consequences, so an AI-generated recommendation is never the final answer on its own. Field Service is built around a human-in-the-loop model: the platform drafts the investigation, and a designated reviewer evaluates and approves it.
Completed investigations land in a review dashboard, where the reviewer can examine the agent's work in an auditable view before approving it or sending it back for further analysis. Once a case is approved, it becomes part of the organization's institutional memory. The next time a similar failure happens, Field Service can surface that reviewed investigation instead of making the team rebuild the analysis from scratch. Reviewer feedback also feeds back into future investigations, so the system compounds organizational knowledge rather than just automating a single task.
Put Your Engineering Knowledge to Work
Build Your Industrial Knowledge BaseBuilt on your own standards, not generic answers
Industrial equipment does not run on generic internet knowledge. A boiler manufacturer, refinery, power plant or heavy-equipment operator has its own inspection intervals, maintenance practices, material specifications, escalation procedures and failure history.
Field Service lets organizations load their own SMPs, SOPs and operational protocols into its Knowledge Base, so every investigation and Service Agent answer is checked against the standards the organization already follows, not a generic baseline.
Field Service is built for high-pressure industrial assets: boilers and pressure vessels, heat exchangers, piping systems, rotating equipment and similar equipment where reliability failures carry real operational consequences. Beyond individual investigations, the platform also tracks usage, manages members, collects reviewer feedback and gives visibility into cost, so organizations can see how adoption is going across teams and sites.
Part of ConforgeLabs' intelligence layer for manufacturing
Field Service is one piece of a broader ConforgeLabs vision: an intelligence layer for complex manufacturing operations. ConforgeLabs brings specialized AI agents into functions across the manufacturing lifecycle, including warranty intelligence, supplier intelligence, RFQ intelligence, engineering drawing validation, inventory intelligence and Field Service, with a platform that connects operational data across ERP, MES, PLM, quality, maintenance, supply chain and other enterprise systems.
The approach works with the systems manufacturers already use rather than forcing a rip-and-replace transformation. Its agentic architecture connects enterprise data with AI agents that can search, compare, reason and execute, with governance, explainability and human oversight built into the operating model.
Conclusion
Every failure investigation contains knowledge, but most of it traditionally disappears into reports, spreadsheets, emails and the memory of individual experts. Field Service captures that knowledge, connects it to new evidence and makes it available the next time a similar failure occurs.
For industrial organizations, that shifts reliability work from reactive investigation toward a connected model where every case informs the next one, every recommendation stays subject to human review, and operational knowledge compounds as an asset instead of disappearing after each incident. The goal is not just faster diagnosis. It is a system that learns from every failure and helps teams make the next decision with more context, which is the intelligence ConforgeLabs is building into industrial service.
