Product Lineup
From exploratory analysis by data scientists to edge-based diagnostics on the factory floor and API integration into existing systems. toorPIA technology delivered in the optimal form for your use case. Please contact us for Pricing & Licensing information.
toorPIA Analysis Package
An on-premises analytics platform that lets you use toorPIA in a JupyterHub + JupyterLab environment. Installed in Docker containers on your private cloud, your in-house data scientists can access the JupyterLab environment through a browser and perform exploration and analysis of high-dimensional data with toorPIA.
JupyterHub handles user authentication and session management, with each user working in an independent JupyterLab environment. The toorPIA engine is invoked as analysis commands, and results are interactively visualized within the Notebook. Data never leaves your premises, ensuring secure operations.
- • On-Premises Deployment — Secure operations without data leaving your premises. Runs in Docker containers on your private cloud
- • JupyterHub Integration — Multi-user authentication and session management. Instant access from a browser
- • MapInspector — Select clusters on projected maps and instantly compare key attribute differences. Toggle between heatmap and scatter plot views
- • Interactive Analysis — Execute commands in the Notebook to explore map creation, cluster comparison, and attribute extraction in real time
For how it tells high-dimensional states apart, see the technology page →
Running toorPIA in JupyterLab and performing cluster analysis with MapInspector
System architecture: Web Browser → JupyterHub → toorPIA Engine (inside Docker container)
vibeCheck Introduction Video — From recording to precursor detection
Three-step workflow: Field measurement (1–2 min, no explosion-proofing required) → Transfer to vibeCheck unit in the office → Automatic analysis and results
vibeCheck data management screen: centralized management of recording data, pipeline settings, and diagnostic results per equipment
Excels with low-speed rotating machinery and conveyors where conventional frequency analysis struggles to detect issues
vibeCheck
Catch the fall before it starts.
An edge diagnostic device that lets you start precursor monitoring right now using your iPhone or off-the-shelf equipment, even in facilities where permanent sensors cannot be installed.
No fixed sensors needed
Ideal for facilities where wiring work or equipment downtime make permanent sensor installation impractical. Just capture sound during daily inspections
iPhone recording works
Record with your iPhone's built-in mic or wired/wireless mics. Off-the-shelf piezo pickups are also supported. Use the equipment you already have
Perfect for PoC
Validate the effect at small scale without expensive upfront investment. Smoothly transition to full rollout with a low-risk, staged approach
A Raspberry Pi 5-based edge-processing diagnostic device. Recording hardware is flexible to suit your needs — iPhone's built-in mic, wired/wireless mics, off-the-shelf piezo pickups, or 32-bit float audio recorders. No specialized instruments needed. Start advanced precursor monitoring simply by capturing sound during daily operations.
Recorded audio data is filtered and denoised by DSP modules, then converted into frequency spectra via STFT. The resulting high-dimensional vectors are projected into 2D by toorPIA, and precursor monitoring through comparison with the base map is performed entirely on the device. No data transmission to the cloud is required.
Operation Overview
The vibeCheck unit is set up in an office or control room, not at the equipment site. At the field, operators capture sound as part of daily inspections using an iPhone, a piezo pickup with an audio recorder, or similar devices. Recorded data is imported into the vibeCheck unit in the office, and diagnostic results are reviewed on the dashboard. No permanent equipment needs to be installed at the field.
Operation Workflow
Initial setup takes 5 steps. After that, operations are automated to just 3 steps.
After setup, just 3 steps — Simply import audio data and assign it to equipment; the configured pipeline runs automatically and diagnostic results are displayed. Alerts notify you automatically when precursors are detected.
To see how this workflow takes root on the factory floor, see our deployment approach →
Key Features
- • Edge-complete — No cloud required. Ideal for security-conscious environments
- • Pipeline Comparison — Compare and evaluate different DSP/STFT settings to find optimal analysis conditions
- • Web Browser Operation — No dedicated software required. Intuitive dashboard UI
- • Remote Maintenance — Secure remote access via Soracom connectivity
toorPIA API
toorPIA's dimensionality reduction engine and precursor monitoring system delivered as a REST API. By integrating into your existing systems and data pipelines, you can monitor high-dimensional data for early warning signs that emerge in transitional regions.
From base map creation to new data projection and diagnostic score retrieval, everything is completed via API calls. Continuously monitors tens of thousands of map points in the background, with support for filtering by status and tags, and automated integration from external systems through API Key issuance.
Key Endpoints
- • Map Creation — Generate base maps from high-dimensional data. Automatic learning of normal regions
- • Data Addition & Projection — Project new data onto the base map and return 2D coordinates
- • Precursor Monitoring — Returns distance scores from the normal region and transition stages (NORMAL/WARNING/DANGER), quantifying precursory signs before anomalies develop
- • Map Management — Filtering by status and tags, dashboard integration
Technical Specifications
- • REST API / JSON format
- • Supports both batch processing and streaming
- • API Key authentication
- • Integrates with existing ETL/MLOps workflows
LLM Integration
The Python client toorpia includes a built-in MCP server, enabling LLMs such as Claude to directly perform dimensionality reduction and precursor monitoring via toorPIA.
- • Install the client and MCP server together with
pip install - • LLMs can interactively perform data analysis and precursor monitoring
For the evidence behind the engine's performance, see the open benchmark →
Create base map (submit high-dimensional data)
Project new data onto existing map
Retrieve diagnostic results
{
"map_id": "m-2026-0401-001",
"status": "warning",
"score": 0.73,
"position": { "x": 0.42, "y": -0.18 },
"distance_from_rg": 1.84,
"threshold_rg": 1.0,
"in_normal_area": false
}Pricing & Licensing
We will propose the optimal plan based on your usage scale and deployment model.
Please feel free to contact us.
