Plaud Inc. builds a high-performance, secure AI infrastructure on Microsoft Azure

Business needs & challenges

Plaud aims to transform conversations into actionable intelligence at a global scale. Achieving this goal requires overcoming several critical technical and operational challenges.

Compliance, security, and privacy protection
As a provider of note-taking devices serving a large global user base, Plaud must address privacy, security and compliance requirements associated with voice data storage and transmission, including GDPR, SOC 2, HIPAA, ISO 27001, and ISO 27701.
These requirements significantly increase technical complexity and impose stringent demands on system architecture, data governance, and operational processes.

Long-form audio processing, stability, and scalability
Plaud must deliver high accuracy in speech recognition and natural language processing across long-form audio and multilingual scenarios, while maintaining low latency.
At the same time, rapid global user growth requires the platform to maintain cross-region stability under high concurrency. This also demands reliable synchronization between edge devices and backend services, along with high availability at scale.

Balancing AI performance and cost
Plaud relies on large language models to generate summaries and insights, where performance and cost must be carefully balanced.
Unoptimized model usage can lead to rapidly escalating costs and degraded response times. A key challenge is sustaining high-speed AI inference while preventing exponential cost growth.

“Our goal is to transform conversations into actionable intelligence at scale. This requires us to balance performance, cost, and compliance from the ground up,” said Kuhn Liu, Head of backend R&D at Plaud.

Solution

To address these challenges, Plaud adopted Azure OpenAI in Foundry Models as part of its AI infrastructure to support key model inference, transcription, summarization, and compliance-sensitive workloads. According to Plaud, this approach helped improve development efficiency, support system performance, and address compliance requirements across multiple markets.

Based on Plaud’s internal evaluation, development and model deployment cycles were reduced from weeks to days, and transcription accuracy and processing speed increased by 35% compared to previous implementation approach. The solution also helped support Plaud’s compliance objectives across major global frameworks and standards. During implementation, Plaud leveraged several core Azure capabilities:

Long-form audio processing
Azure Speech in Foundry Tools for fast transcriptions provides high-speed, asynchronous speech-to-text capabilities optimized for long-form audio and complex real-world environments.
With this service, Plaud achieved: 

• Efficient asynchronous transcription

• Enhanced long-form audio processing

• Improved multilingual support

Word Error Rate (WER) was reduced compared to previous transcription workflow, and system robustness in complex acoustic environments was strengthened, helping support more consistent transcription quality across diverse audio scenarios.

Performance and cost optimization
Azure OpenAI in Foundry Models supports elastic scaling and flexible deployment options, helping support service continuity during traffic spikes.

• Provisioned (PTUs) provides dedicated compute resources for critical workloads, helping maintain more consistent latency under high concurrency and reducing performance variability in mission-critical scenarios.

• Standard (Pay-as-you-go) supports flexible, on-demand usage for long-tail workloads and testing scenarios, helping Plaud align costs with actual usage and demand patterns.

With this hybrid architecture, Plaud achieved the following outcomes: 

• A 35 reduction in overall compute costs.

• A 30% improvement in Inference performance under high-load conditions.

Additionally, Azure OpenAI in Foundry Models provides managed access to GPT and Whisper models, allowing Plaud to generate summaries, extract intent, and produce structured outputs without managing model infrastructure, helping reduce operational overhead and improve development efficiency.

Compliance, security, and privacy
Azure’s enterprise-grade security and compliance framework helped Plaud support its data protection and compliance objectives. 

Plaud implemented private model deployment to support processing user data within controlled environments and to help prevent customer data from being used for public model training.

Azure natively supports compliance frameworks and standards including GDPR, SOC 2, ISO 27001, and ISO 27701, helping Plaud address data residency and privacy requirements across different regulatory environments. 

In addition: 

• Content Safety in Foundry Control Plane provides automated content moderation and real-time risk detection capabilities. 

• Intelligent filtering helps support platform safety and compliance objectives.

As a result, based on Plaud's internal evaluation, the implementation contributed to: 

• strengthened alignment with major global security and privacy frameworks and standards.

• a 30% improvement in data processing efficiency and system responsiveness. 

• a 50% reduction in compliance audit preparation time.

“Our partnership with Microsoft Azure enables us to scale globally while maintaining high performance, cost efficiency, and strong compliance standards,” said Kuhn Liu, Head of backend R&D at Plaud.

Results

According to Plaud, building on Azure contributed to improvements across performance, cost efficiency, and compliance-related operations.

Performance improvements
Plaud improved speech recognition and text processing performance across multilingual and complex scenarios, achieving the following outcomes based on Plaud’s internal evaluation: 

• a 35% increase in transcription accuracy and processing speed compared to previous workflows. 

• a 30% improvement in inference performance under high-load conditions.

Cost efficiency and operational agility
Azure’s elastic infrastructure and Azure OpenAI Provisioned (PTUs) depoyment enable dynamic allocation of large language model resources based on real-time demand.

• Even as the user base doubled, overall compute costs decreased by 35%. 

• Integrated monitoring, logging, and automation tools streamlined operations.

Plaud’s DevOps team can now efficiently manage the entire pipeline—from data capture to insight generation—while reducing development and deployment cycles from weeks to days.

Trust and compliance
Plaud strengthened alignment with major global compliance frameworks and standards while reducing audit preparation time by 50%, helping reinforce customer trust in its platform.
Through its collaboration with Microsoft Azure, Plaud successfully addressed key challenges in speech recognition, scalability, cost control, and compliance—enabling it to compete globally while delivering intelligent, efficient conversational insights.
Today, Plaud serves over 2 million users worldwide. Professionals using Plaud products save more than 260 hours annually, contributing to improved productivity and efficiency.

Future
Plaud will continue to deepen its collaboration with Microsoft Azure to further advance its AI infrastructure.
Key focus areas include: 

• Optimizing AI inference performance. 

• Expanding conversational intelligence capabilities for enterprise scenarios. 

• Strengthening AI infrastructure to support global scale and compliance requirements.

“Azure provides the foundation for us to build reliable, scalable AI systems that professionals around the world can trust,” said Kuhn Liu, Head of backend R&D at Plaud.