Industrial Data Hub (IDH) provides a cloud‑native platform that automatically ingests, aligns, and cleans process and transactional data from systems such as CMMS, LIMS, EH&S, and MES. It delivers one‑click generation of AI‑ready 3D tensors compatible with any modern AI framework, eliminating manual preprocessing and accelerating model development for industrial enterprises.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises deploying advanced analytics and AI often struggle with fragmented, noisy, and misaligned time‑series data from disparate sources such as CMMS, LIMS, EH&S, and MES systems. Preparing this data for AI models requires extensive manual cleaning, transformation, and integration, which is time‑consuming and error‑prone.
Solution
The Industrial Data Hub (IDH) offers a cost‑effective platform that automatically collects, aggregates, and cleans process and transactional data across an organization. By aligning disparate data streams into consistent time‑series formats, IDH generates AI‑ready 3D tensors with a single click. These tensors are compatible with any modern AI framework, enabling rapid model development, training, and deployment without custom preprocessing pipelines. The platform integrates directly with existing enterprise systems, streamlining the flow of clean data from source to AI application and reducing the overhead of data engineering.
Target Audience
Primary customers are industrial enterprises and manufacturers that need to feed clean, aligned process data into AI and advanced analytics workflows, including operations teams, data scientists, and engineering departments.
Features
- Automated ingestion from multiple industrial systems (CMMS, LIMS, EH&S, MES, etc.) via connectors and APIs
- Real‑time data alignment and cleaning to produce consistent, gap‑filled time‑series datasets
- One‑click generation of 3D tensor structures ready for use in TensorFlow, PyTorch, and other AI tools
- Scalable cloud‑native architecture that handles high‑volume sensor and transactional data streams
- Built‑in data validation and quality metrics to ensure reliability of AI inputs
- Export interfaces for seamless integration with downstream analytics, modeling, and deployment pipelines