Athena launches FabOrchestrator, an agentic AI platform for manufacturing execution systems

In short: Athena Technology Solutions, a Fremont-based MES integrator with roughly 120 employees, has launched FabOrchestrator, an agentic AI platform for manufacturing that automates reporting, support tickets, system modelling, and code generation for semiconductor and electronics factories. Built in partnership with Bangalore-based LLM at Scale.AI, it layers LLM capabilities on top of the Siemens Opcenter and Critical Manufacturing MES platforms that Athena implements.

Athena Technology Solutions, a Fremont-based MES integrator with roughly 120 employees, has announced FabOrchestrator, a product it describes as the manufacturing industry’s first “Agentic AI Foundry,” designed to automate reporting, support tickets, system modelling, and code generation inside semiconductor and electronics factories.

The product, built in partnership with LLM at Scale.AI, a Bangalore-based enterprise AI platform, wraps large language model capabilities around the manufacturing execution systems that Athena implements for its customers. It is a bet that the same agentic AI pattern reshaping software development and customer support can be applied to the highly specific, data-dense environment of a production fab.

What FabOrchestrator does

The platform has four components. FabInsight lets factory engineers query production data in plain English and receive reports and analysis without writing SQL or navigating multiple dashboards. An AI Support Engineer handles routine MES support tickets automatically, escalating complex issues to human engineers. A Modeling Agent answers questions about MES configuration and guides teams through system upgrades. A Back-end Agent generates code snippets to accelerate MES implementation work.

None of these capabilities are individually novel. Natural-language querying of enterprise data, automated ticket triage, and AI-assisted code generation are features that dozens of companies now offer across every industry vertical. What Athena is attempting is to package them specifically for manufacturing execution, where the data structures, workflows, and domain knowledge are sufficiently specialised that general-purpose AI tools tend to produce unreliable results.

“This is a major advancement for the MES ecosystem,” said Senthil Ranganathan, Athena’s founder and CEO. Ranganathan founded the company in 2011 and has spent two decades in manufacturing systems across the disk drive, semiconductor, and solar industries.

The MES context

Manufacturing execution systems are the software backbone of any modern factory. They track every wafer, component, and assembly through the production process, recording what happened, when, by which machine, and under what conditions. In semiconductor fabs, where a single chip can pass through hundreds of process steps over several weeks, MES data is both critical and voluminous.

The problem Athena is targeting is that extracting value from this data typically requires specialist knowledge. Writing reports means understanding the MES data model. Configuring the system for new products means navigating complex modelling rules. Troubleshooting issues means knowing which of thousands of parameters might be relevant. These tasks consume engineering hours that could otherwise go toward improving yield or throughput.

Athena operates as an implementation partner for Siemens Opcenter and Critical Manufacturing, two of the major MES platforms used in semiconductor and electronics manufacturing. Its business has been built on deploying, customising, and supporting these systems. FabOrchestrator represents an attempt to layer AI on top of that existing expertise, turning the domain knowledge its consultants carry into something that can be delivered as software rather than billable hours.

The partnership

The AI platform underneath FabOrchestrator comes from LLM at Scale.AI, a Bangalore-based company founded in 2023 that specialises in multi-agent orchestration for enterprise applications. The company claims clients including JTC, CBRE, JLL, Cushman and Wakefield, Johnson Controls, and the State of California, primarily in facilities management and real estate. Its research partnerships include MIT, UC Davis, and NTU Singapore.

The partnership makes commercial sense for both sides. LLM at Scale.AI gets access to manufacturing domain expertise and a customer base it would struggle to reach independently. Athena gets an AI platform without having to build one from scratch. Whether the combination produces something that works reliably on a factory floor, where incorrect data or a misrouted process step can cost millions, is the question that matters.

Market timing

Athena is entering a market that larger companies are also targeting. Microsoft’s industry blog reported that 65% of manufacturers are implementing AI-powered MES by 2026. Infor, Siemens, and other major industrial software vendors are all building agentic capabilities into their platforms. Siemens recently acquired Canopus AI for semiconductor metrology. NVIDIA is pushing its own manufacturing AI stack through the Isaac and Omniverse platforms.

For a company of Athena’s size, roughly $8 million in revenue, the challenge is competing with vendors that have orders of magnitude more resources. The counter-argument is that MES implementation is deeply domain-specific work, and the large platform vendors have historically struggled to deliver the kind of hands-on, factory-floor expertise that smaller integrators provide. If FabOrchestrator can genuinely reduce the engineering hours required for MES reporting, configuration, and support, it addresses a real pain point that its customers experience daily.

The broader trend is unmistakable. Every layer of the manufacturing software stack, from enterprise resource planning down to equipment control, is being wrapped in AI interfaces that promise to make specialist systems accessible to non-specialist users. The risk, particularly in environments like semiconductor fabs where precision matters and errors propagate expensively, is that natural-language interfaces create a false sense of understanding. An engineer who queries a system in plain English and receives a confident answer may not recognise when that answer is subtly wrong.

Athena has not disclosed pricing, customer commitments, or deployment timelines for FabOrchestrator. The product is a first entry from a small but established MES integrator into a space that is attracting attention from every major industrial software vendor. Whether it can carve out a defensible position depends on how well it bridges the gap between generic AI capabilities and the exacting demands of semiconductor manufacturing, a domain where getting it mostly right is not good enough.