How Hitachi High-Tech and GlobalLogic Are Transforming Equipment Maintenance with Reliable AI

Hitachi High-Tech and GlobalLogic are advancing a strategic partnership to bring Reliable AI into one of the most demanding industrial environments there is: the service and maintenance of high-precision semiconductor equipment. The work is a flagship application of GlobalLogic’s VelocityAI service offering, with Reliable AI as its core engineering principle.

The next phase of the project is now underway, with engineering teams across Hitachi High-Tech and GlobalLogic working jointly on a hybrid reasoning foundation purpose-built for high-stakes operations.

The Proof of Concept is built on a curated subset of the LS9600 maintenance manual — the groundwork for a zero-hallucination reasoning engine that traces every answer back to authoritative source material.

Challenge

Semiconductor equipment runs at the edge of what is physically possible. Tools cost millions of dollars, fabs run continuously, and a single avoidable hour of downtime can cascade through customer schedules. Keeping equipment healthy depends on field engineers and maintenance technicians who carry deep institutional knowledge — most of it locked inside detailed service manuals and the experience of senior practitioners.

That knowledge is under pressure. Equipment complexity is rising, and the population of engineers who hold the deepest expertise is not keeping pace with demand. Generative AI is the obvious tool to close the gap. But in this environment, an AI co-pilot that improvises is worse than no co-pilot at all. The bar is not “helpful most of the time;” it is reliable enough to put in front of a technician working alone, at speed, on a tool that cannot fail.

The approach: hybrid reasoning, grounded in real documentation

The joint team set out to build what the partnership calls a hybrid reasoning brain — a system that pairs the flexibility of large language models with deterministic logic and a curated, versioned knowledge base, so that every answer the system gives can be traced back to authoritative source material.

As a Proof of Concept for the Reliable AI foundation, the team built an initial knowledge base from a subset of the LS9600 maintenance manual. This is the groundwork for a zero-hallucination reasoning engine: a system designed so that when it doesn’t know, it says so — and when it does answer, the path from question to recommendation is auditable. The POC is being built on Microsoft Azure, with the core hybrid reasoning approach developed jointly by GlobalLogic and Hitachi High-Tech.

Around that foundation sits a set of agents, each with a tightly scoped role:

• Maintenance Planner. Generates and schedules preventive maintenance events, weighing equipment health, tool usage, and fab production targets to prioritize what gets done when.
• Workforce Allocation. Assigns technicians by skill, certification, availability, and workload, and surfaces cross-training and shift adjustments that reduce downtime.
• Consumables. Tracks spare parts and consumables against maintenance demand, with proactive alerts to procurement to head off stock-outs before they become line stops.
• Maintenance Execution. Walks technicians through procedures step by step, grounded in the maintenance manual and safety procedures rather than improvised by the model.

The decomposition is deliberate. Each agent has a bounded scope, an explicit escalation path, and a clear point at which a human stays in the loop. That is what bounded autonomy looks like in practice: the system does not try to do everything, and it does not try to do anything outside the envelope it was designed for.

Capabilities and value delivered

GlobalLogic developed the initial working version of the agent rapidly, by customizing existing software components to meet Hitachi High-Tech’s specific requirements. An early visual prototype let both teams align on functionality before deeper engineering investment.

The benefits are concrete:

• For the people doing the work. Field engineers and maintenance technicians save time on complex operations, with less stress and a higher margin of safety.
• For the equipment and the line. Lower error rates on repair and maintenance procedures prolong the lifespan of valuable equipment, prevent production line downtime, and minimize return visits and wasted consumables.

Quantified business outcomes will follow as the solution moves from POC into broader production deployment.

Reliable AI isn’t a model that always answers. It’s a system that knows when not to — grounded in a curated knowledge base, paired with deterministic logic, and bounded by an explicit scope. That is what makes it deployable in environments where being wrong is not an option.

Why it matters beyond the fab

Semiconductor metrology and etching are among the lowest-tolerance service environments anywhere. If Reliable AI works here, it works wherever institutional knowledge is scarce and the cost of being wrong is real — energy, mobility, and medical equipment service among them.

That is the point of the partnership: a Reliable AI foundation that travels, delivered through VelocityAI. It is also what One Hitachi looks like in practice — product depth, digital engineering, and AI capability brought to bear on the same problem.