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When Failures Cost Millions, Every Second Counts

Meet the AI positioned to diagnose semiconductor failures 35x faster than human experts

⚡ 91.7% research accuracy validated by industry experts
💰 $100B+ in annual semiconductor failure costs
🏆 Research validated by STMicroelectronics, Bosch, and Infineon
See QuadaptAI Latest Research → Request Private Demo Limited Access View Success Stories
Discover the Future

Core Technology

Four revolutionary components working in harmony.

The seamless orchestration of advanced machine learning, specialized domain models, autonomous coordination systems, and efficiency optimization engines delivers unprecedented performance while maintaining enterprise-grade reliability and scalability across diverse operational environments.

Q-RAG

Advanced Retrieval Augmented Generation 70% efficient and optimized architecture reduces computational overhead while maintaining high accuracy.

A breakthrough enabling enterprise-scale deployment at significantly lower operational costs compared to traditional methods.


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Q-Quant Compression

Efficient LLM compression algorithm for fast inference throughput and model performance.

Eliminating the need for expensive GPU clusters while maintaining performance typically reserved for high-end supercomputers.


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Q-FA Analysis

Specialized foundation models for semiconductor failure analysis.

Models positioned to instantly recognize subtle defect signatures and correlate complex variables that require hours of expert analysis.


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Q-Agentic Decision

Autonomous multi-agent coordination for complete failure analysis.

Autonomous coordination prevents traditional manual analysis delays caused by scheduling conflicts, expertise gaps, and team communication issues.


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Latest Research

Big GCVAE Architecture

Big GCVAE: Decision-making with Adaptive Transformer

Ezukwoke, K., et al. (2024). Journal of Intelligent Manufacturing

Advanced decision-making framework using adaptive transformers for semiconductor failure analysis.

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NLP Pipeline

Leveraging Pre-trained Models for Failure Analysis

Ezukwoke, K., et al. (2022). arXiv preprint

Novel approach using pre-trained models for efficient failure analysis triplet generation.

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System Workflow

Root Cause Prediction with Genetic Algorithm-ML

Rammal, A., et al. (2023). Scientific Reports

Hybrid approach combining genetic algorithms with machine learning for root cause analysis.

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Our Partners

Mines Saint-Étienne

TEAMS Incubator

France Digitale

Innovation Network