TQNN Labs Website

Official website and developer portal for the TQNN Fault-Tolerant Inference Platform.

The TQNN Labs website serves as the public entry point to the TQNN ecosystem, providing developers, researchers, and organizations with everything needed to understand and integrate confidence-aware, fault-tolerant inference.

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Purpose

This repository contains the source code for https://tqnnlabs.com.

The website provides:

• Platform overview
• Developer documentation
• Python SDK resources
• Cloud API information
• Benchmark results
• Pricing
• Explorer account registration

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What is TQNN?

TQNN is a managed fault-tolerant inference platform designed for real-world data.

Unlike conventional inference systems that assume clean and complete inputs, TQNN evaluates prediction confidence, input integrity, and uncertainty before returning controlled inference decisions.

Applications include:

• Industrial automation
• Fault diagnosis
• Predictive maintenance
• Sensor systems
• Manufacturing
• Scientific computing
• Safety-critical AI

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Features

• Modern responsive website
• Developer-first documentation
• Explorer account onboarding
• Cloud API integration
• Benchmark showcase
• Python SDK resources
• Mobile friendly
• Search engine optimized

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Repository Structure

/
├── index.html
├── assets/
│   ├── css/
│   ├── js/
│   └── images/
├── benchmarks/
├── docs/
├── robots.txt
├── sitemap.xml
└── README.md

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Related Repositories

Repository	Description
tqnn	Official Python SDK
tqnn-benchmarks	Public benchmarks and example notebooks
tqnnlabs-website	Official website and developer portal

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Technology

• HTML5
• CSS3
• JavaScript
• Netlify
• Google Cloud Run
• GitHub

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Local Development

Clone the repository:

git clone https://github.com/tqnnlabs/tqnnlabs-website.git

Run a local web server:

python -m http.server 8000

or

npx serve

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Deployment

Production deployments are automatically published through Netlify.

Every push to the main branch triggers a new deployment.

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Mission

TQNN exists because real-world data is rarely perfect.

Sensors fail.

Measurements become noisy.

Values go missing.

Confidence changes.

Instead of assuming perfect inputs, TQNN helps applications continue making informed, confidence-aware decisions when uncertainty is unavoidable.

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Learn More

Website

https://tqnnlabs.com

Python SDK

https://github.com/tqnnlabs/tqnn

Benchmarks

https://github.com/tqnnlabs/tqnn-benchmarks

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© TQNN Labs. All rights reserved.