Introducing Argonos: Operational AI for Mission-Critical Work, Where Almost Right Is Still Wrong
By Brian Kirk, General Manager, Argonos Americas
Most AI today is built for productivity tasks: it writes emails, summarizes meetings, and drafts slide decks. That’s useful, but it isn’t where an engine maker, a drug developer, or a bank makes or loses money.
Today we launch Argonos, the Operational AI platform built for mission-critical work. I believe this is the essential next step in enterprise AI.
Over the past year, our team has spoken and worked with some of the most innovative leaders across manufacturing, aerospace, life sciences, energy, and government about putting agentic AI to work. Almost all of them have run the pilots and sat through the demos. AI adoption is happening, but when it comes to bringing AI into the heart of their operations, things stall.
The difficulty starts with context. These companies’ work runs through R&D, quality, safety, regulatory, and risk systems. AI needs access to the relevant records and an understanding of how products, processes, and decisions fit together. A fluent answer has to be grounded in the company’s own knowledge before people can put it to work.
These organizations are dealing with engine tests, molecule development, power grids, and complex supply chains. In this work, an AI answer that is almost right is still wrong. A confident response built on an outdated part revision, a misread compliance standard, or an incomplete test record doesn’t just waste time; it risks recalls, line stoppages, and regulatory penalties. Or worse.
Argonos gives people and AI agents that foundation. It connects organizational knowledge to the work at hand, with controls over what people and agents can access, what agents can do, and where the platform runs.
Sinequa is the foundation
For our existing customers and partners, the most important message I can share today is one of continuity and commitment.
Sinequa sits at the core of Argonos. We’ve spent two decades helping companies find and understand what they know. Its retrieval and relevance capabilities connect AI to enterprise records, including complex technical information. Sinequa remains a focus of ongoing research and development, and improvements to this foundation strengthen what Argonos can do. Your contracts, your logins and the people you work with stay the same.
Cummins offers one example. Its published customer story describes how Sinequa connects engineers to product data, engineering standards, and technical history, with cited answers they can check. That is the kind of company context AI needs to support engineering work. Read the Cummins case study.
What Operational AI looks like
Operational AI brings company knowledge into the decisions and workflows that matter to the business. Argonos is built on three principles:
- Enterprise AI must be grounded: Frontier AI models are smart, but they don’t know your business. Argonos gives them your business context: what your company knows, how it fits together, and who’s allowed to see what. Every answer points back to the record it came from.
- Enterprise AI must be governed: Every agent works inside the permissions, guardrails, and budget you set, and every action is logged.
- Enterprise AI must be independent: This means you keep operational control over your AI deployment, from the infrastructure and models used to the configurations you decide. Our engineers work with your team to configure the platform for your systems and workflows, including the experience your employees use, and then give you full autonomy over the system – no back doors.
Built for global operations
For a global company, where an AI deployment runs can determine whether and how it can be used across the business. A service that works for one location may not meet another location’s cybersecurity requirements or policies on approved technology. Deployment needs to fit the places where people actually work.
On-premises deployment lets organizations keep their knowledge platform in their own environment and approve external model connections separately. It can help teams extend AI to operations that cannot all use the same SaaS service, subject to each location’s requirements. Companies also need the flexibility to adapt the platform to their workflows and develop new capabilities with a technology partner as their needs change.
Grateful to our partners and looking ahead
I’m grateful to our customers and innovation partners, including Cummins, Blue Origin, and ST Microelectronics, and to the engineers, specialists, and partners who help us understand the work AI needs to support.
Their requirements keep our work focused on useful answers, business-fit configurations, and deployment choices that make AI practical across a global organization.
If you have work where almost right isn’t good enough, we’d like to hear about it.
Welcome to Argonos.
