INSYTES provides Cauza, a no‑code Causal AI platform that transforms industrial data into clear cause‑and‑effect insights, enabling engineers and managers to identify true root causes and run what‑if simulations before implementing changes. By revealing actionable causes rather than mere correlations, the platform helps reduce trial‑and‑error, scale fixes, and make more confident decisions across manufacturing and other industrial settings.
Funding
Funding not disclosed
Founders
Product
Problem
Industrial operations generate large volumes of sensor and process data that can indicate when something is wrong, but the data alone does not reveal why the issue occurs. Without clear causal understanding, engineers rely on trial‑and‑error fixes that often fail to scale and can lead to costly, risky decisions.
Solution
Insytes offers Cauza, a no‑code causal AI platform that converts raw industrial data into explicit cause‑and‑effect insights. The system identifies true root causes rather than mere correlations and presents findings in plain language that non‑technical stakeholders can readily understand. Users can run what‑if simulations to evaluate potential interventions virtually before applying changes on the shop floor, reducing the need for costly experimentation. By automating the discovery of causal levers, Cauza enables consistent, repeatable fixes across plants and processes, improving performance while lowering emissions and energy consumption.
Target Audience
Primary customers are manufacturing engineers, plant managers, and operations analysts seeking data‑driven, scalable solutions to reduce downtime, improve quality, and lower energy use.
Features
- Automated causal inference engine built on peer‑reviewed research to isolate true root causes from complex industrial datasets
- No‑code interface that allows engineers and managers to upload data, define queries, and receive plain‑language explanations without programming
- Interactive what‑if simulation module for testing the impact of variable changes on outcomes before physical implementation
- Real‑time dashboards that visualize causal relationships and highlight the most influential factors driving defects or inefficiencies
- Integration capabilities for common industrial data sources and IoT platforms, enabling seamless data ingestion