Overview

What is Nios?

Nios is an agentic AI platform for value chain raw material intelligence, built for procurement and sustainability teams.

Nios helps enterprises move beyond tier-1 upstream value chain visibility to uncover hidden raw material dependencies, trace likely sourcing geographies, and quantify environmental impacts such as biodiversity, carbon, water, land, and pollution, all the way across multi-stage value chains.

Today, organizations spend months on manual analyses, consultants, or static tools that fail to keep up with fast-changing market environment. Nios transforms this process by combining customer data, leading environmental databases, and AI agents to deliver continuous, decision-ready intelligence in minutes, not months.

What is an agentic AI system?

An agentic AI system is a network of specialized AI agents that collaborate to complete complex tasks. Each agent is powered by a large language model (LLM) such as Claude Sonnet 4, wrapped with role-specific instructions ("prompts") and often given access to external tools like databases, web search, or code execution.

A central orchestrator decides which agent to activate and which model to call (e.g., Claude for reasoning, code-focused LLMs for structured outputs). Agents pass results between each other, so the workflow builds step by step.

Think of it like a project team of experts: one member researches, another analyzes numbers, another writes up the findings, and the orchestrator coordinates who does what.

👉 Why it matters: Unlike a single chatbot, agentic AI can break down complex workflows into coordinated steps, producing results that are more structured, scalable, and closer to real expert work.

In short, instead of one model trying to do everything, an agentic AI system works like a team of LLM-powered specialists, coordinated to deliver structured results.

⚠️ Disclaimer: Outputs are probabilistic and should be interpreted as directional approximations, not exact facts. We are continuously working to increase transparency about how the agents operate, what assumptions and data they use, and the limitations of their results. Think of them as capable assistants: understanding what they can and cannot do helps you get the most out of them.

👉 Privacy note: Customer data processed by Nios agents is never used to train external models. Inputs are only used to perform the requested calculations and deliver results within the Nios system.