Azure AI Language

Azure AI Language is a cloud-based service designed to help you easily get insights from unstructured text data. It uses a combination of SLMs and LLMs, including task-optimized decoder models and encoder models, for Language AI solutions. It provides premium quality at an affordable price, excels in scale and low latency. With it, you can extract, classify, and summarize information to gain insights. You can also customize and finetune them for your specific needs. It empowers you to integrate natural language into apps, bots, and IoT devices. For example, it can redact sensitive data, segment long meetings into chapters, analyze health records, and orchestrate conversational bots on your custom intents and factual answers to ensure consistency and control.

Core Features

• Extract Classify and Understand Information
  • Description: Extract and distill key insights from unstructured data, such as named entities, medical information, important statements, etc. and analyze sentiment and my opinion.
  • Key Features: Named Entity Recognition (NER), Custom Extraction, Key Phrase Extraction, Health Information Extraction, Text Summarization, Extractive summarization, Abstractive summarization, Sentiment Analysis, Language Detection.
• Enhanced Conversational Experiences
  • Description: Customize your conversational experience with a deterministic and repeatable solution; distill insights from long conversion, empower intelligent conversational agents that can understand, respond, and orchestrate responses in a natural, context-aware manner
  • Key Features: Conversation Summarization, Conversational Language Understanding (CLU), Question Answering (Q&A), and Orchestration Workflow
• Data Privacy and Compliance
  • Description: Identify personally identifiable information, masking it as needed to help you to adhere to your privacy policies.
  • Key Features: PII Detection, PII Redaction.

Use Cases

• Protect privacy data with PII detection: Use PII detection to identify and redact sensitive information before sending your data to LLMs or other cloud services. Redact personal information to protect your customers’ privacy from call center transcription, reduce unconscious bias from resumes, apply sensitivity labels for documents, or clean your data and reduce unfairness for data science.
• Reduce hallucinations and derive insights with Name Entity Recognition and Text Analytics for health: Use Named Entity Recognition or Text Analytics for health to reduce hallucinations from LLMs by prompting the model with extracted entity values (e.g., product names, price numbers, MedDRA code, etc.). Build knowledge graphs based on entities detected in documents to enhance search quality. Extract key information to enable business process automation. Derive insights into popular information from customer reviews, emails, and calls.
• Meeting Summarization for Efficient Recaps and Chaptering: Using summarization features, long meetings can be effectively condensed into quick recaps and organized into timestamped chapters with detailed narratives, making the information more accessible to both participants and those who missed the meeting.
• Call Center Summarization: Using summarization features, customer service calls can be efficiently summarized into concise recaps with focused notes on customer issues and the resolutions provided by agents. This allows agents and supervisors to quickly review key details, improving follow-up actions and overall customer satisfaction.
• Build deterministic and repeatable conversational AI experience: Use conversational language understanding (CLU) to define the top user intents and key information you want to track over the conversations. Build your Q&A bot with custom question answering to control the wording in answers for critical questions with hallucination worry-free. Route user queries over orchestration workflow based on users’ intents or questions.
• Analyze healthcare data with Text Analytics for health: Use Text Analytics for health to extract insights and statistics, develop predictive models and flag possible errors from clinical notes, research documents and medical reports by identifying medical entities, entity relationships and assertions. Auto-annotate and curate clinical data such as automating clinical coding and digitizing manually created data by using entity linking to Unified Medical Language System (UMLS) Metathesaurus and other Text Analytics for health features.

Benefits

• Premium Quality: Pre-trained task-optimized models ensure premium quality as they are built on vast, diverse datasets and fine-tuned by experts to deliver accurate and reliable results across various use cases
• Low Maintenance: Ready to use APIs, constantly enhanced models, and flexible deployment options reduce the need for ongoing prompt rewriting, manual training, or extensive customization. This allows you to focus business insights rather than managing infrastructure.
• Enterprise Scalability: Scalable across multiple environments, from on-premises containers to cloud-based services. adaptable to different workflows and data volumes without sacrificing performance. seamlessly integrated into various enterprise systems

Technical Details

• Deployment: Azure AI Language is composed of many natural language process capabilities. All are available as cloud and most of them also have container offerings.
• Requirements: Azure AI Language requirements may vary slightly depending on the model you are using. Reference the following docs for more information: Data limits for Language service features - Azure AI services | Microsoft Learn .
• Support: Azure AI Language is part of Azure AI Services. Support options for AI Services can be found here: Azure AI services support and help options .

Pricing

Azure AI Language offers competitive pricing. The pricing model includes pay-as-go and discounts based on volume commitments. Explore Azure AI Language pricing options here .