Zeta Alpha

R&D teams have more tools and data than ever, but real questions span multiple systems, turning research into context switching and evidence-trail reconstruction. This post shows how an orchestrating, multi-agent approach connects internal knowledge, lite

Who owns the intelligence created when your team uses AI? Inspired by Satya Nadella's Reverse Information Paradox, we break down why industrial AI, R&D know-how, and custom workflows must stay inside the enterprise.

Hand-crafting AI agents for domains like biochemistry takes immense human effort. To solve this, we built a "Proposer" meta-optimizer to analyze execution traces and autonomously write custom agent code. In this deep dive from Amsterdam Tech Week, we pull

Zeta Alpha introduces a new generation of multimodal agents specialized in manufacturing and engineering knowledge. Bringing 3D CAD into the same workflows as documents and tabular data, the agents can reason over 3D geometry and functionality to deliver

General-purpose AI hits a wall when encountering table-heavy validation reports or complex system diagrams where a single value can impact safety and compliance. Watch our full Future Labs Basel session recording with Sartorius to see how we built a custo

Discovery slows down when molecular structures are completely siloed from the patents and research reports that give them context. Discover how Zeta Alpha's new Advanced Chemistry & Bio Tools allow autonomous agents to interact with 3D structures, compute

Introducing a new type of AI agents in the Zeta Alpha platform that we call superagents. Within this new architecture you can coordinate AI work for complex long horizon tasks in industrial research, business analysis or engineering validation, through mu

Zeta Alpha was all-in at Hannover Messe 2026, the world's largest industrial trade fair. We used the opportunity to launch 3D CAD support and multi-agents, discuss sovereign European AI and connect with industrial companies on the state of AI implementati

The real question is not whether AI will have an impact, but where it can create the most value, and how to design it for industrial complexity, control, and sovereignty, and make the whole company and its people smarter.

As industrial AI matures from initial proof-of-concepts toward high-stakes, reliable implementation, the manufacturing sector faces a specific challenge: how do you transform decades of fragmented technical documentation into a competitive advantage? In a