Azure AI Services: Vision, Search & Beyond
Explore Azure AI Services — Vision, Speech, Language, and AI Search — and understand when to use these specialised capabilities alongside Copilot and Foundry.
Specialised AI for specific problems
If Copilot is a general-purpose assistant and Foundry is a custom kitchen, Azure AI Services are specialist tools — a microscope, a translator, a scanner.
Azure AI Services are ready-made AI capabilities you can plug into your applications:
- Vision — AI that understands images. It can read text from photos, detect objects, and analyse scenes.
- Speech — AI that converts speech to text and text to speech. Real-time transcription, voice interfaces.
- Language — AI that understands text. Sentiment analysis, key phrase extraction, entity recognition.
- Translator — AI that translates text between 100+ languages. A separate service from Language.
- AI Search — Enterprise search that finds the right information across millions of documents. Powers the RAG pattern for grounding AI in your data.
Service-by-service breakdown
Azure AI Vision
Vision gives AI the ability to understand images and video.
| Capability | What It Does | Business Example |
|---|---|---|
| Image analysis | Describes image content, detects objects, reads text | Product catalogue automation — AI tags product photos |
| OCR | Extracts printed and handwritten text from images | Digitise paper forms, scan receipts, read whiteboards |
| Object detection | Identifies and locates specific objects in images | Retail shelf monitoring — detect out-of-stock products |
| Face detection | Detects faces (not identification — privacy-safe) | Count foot traffic in retail without identifying individuals |
| Custom Vision | Train custom image classifiers | Manufacturing quality control — detect defective products. Note: Microsoft is migrating away from Custom Vision — new projects should use Florence/Azure AI Vision model customisation in Foundry. |
Azure AI Speech
Speech bridges the gap between human voice and digital text.
| Capability | What It Does | Business Example |
|---|---|---|
| Speech-to-text (STT) | Converts spoken audio to text | Meeting transcription, call centre analysis |
| Text-to-speech (TTS) | Converts text to natural-sounding voice | Voice interfaces, accessibility, automated announcements |
| Speech translation | Real-time translation of spoken language | Multilingual meetings without interpreters |
| Speaker recognition | Identifies who is speaking | Call centre authentication by voice |
Azure AI Language
Language processes and understands text at scale.
| Capability | What It Does | Business Example |
|---|---|---|
| Sentiment analysis | Determines positive, negative, or neutral tone | Monitor customer feedback across reviews and social media |
| Named entity recognition | Identifies people, places, organisations, dates | Extract key information from contracts and legal documents |
| Key phrase extraction | Pulls out the most important terms from text | Summarise support tickets to identify trending issues |
| Text summarisation | Condenses long text into key points | Auto-summarise lengthy reports or articles |
Azure AI Translator
Translator is a separate service from Azure AI Language, purpose-built for text translation.
| Capability | What It Does | Business Example |
|---|---|---|
| Text translation | Translates text between 100+ languages | Multilingual documentation, global communication |
| Document translation | Translates entire documents while preserving formatting | Localise contracts, manuals, and marketing materials |
| Custom Translator | Train custom translation models with your terminology | Industry-specific translations (legal, medical, technical) |
Azure AI Search
AI Search is the enterprise search engine that powers intelligent information retrieval.
| Capability | What It Does | Business Example |
|---|---|---|
| Full-text search | Traditional keyword search across indexed content | Search across millions of documents |
| Vector search | Semantic search that understands meaning, not just keywords | ”Find policies about flexible working” finds results even if they don’t contain those exact words |
| Semantic ranking | Re-ranks results by relevance using AI | Most relevant document appears first, even in large result sets |
| RAG pattern | Retrieves relevant documents to ground AI responses | Copilot or Foundry app answers questions using YOUR documents as evidence |
| Multi-source indexing | Indexes content from SharePoint, Blob Storage, SQL, and custom sources | One search experience across all your data |
What is RAG and why does it matter?
RAG (Retrieval-Augmented Generation) is the pattern that connects AI models to your data:
- User asks a question
- AI Search retrieves the most relevant documents from your data
- Those documents are sent to the AI model as context
- The model generates an answer grounded in YOUR data, not just its training knowledge
RAG is how enterprise AI avoids hallucination — by grounding responses in real documents. Azure AI Search is the most common retrieval engine for RAG in the Microsoft ecosystem.
When to use Azure AI Services vs Copilot vs Foundry
| Need | Azure AI Services | Copilot for M365 | Microsoft Foundry |
|---|---|---|---|
| Image analysis and OCR | Yes — Vision API | No | Can integrate Vision APIs |
| Speech transcription | Yes — Speech API | Teams has built-in transcription | Can integrate Speech APIs |
| Sentiment analysis at scale | Yes — Language API | No | Can integrate Language APIs |
| Enterprise document search | Yes — AI Search | Copilot Chat uses Graph (not Search) | Uses AI Search for RAG |
| Draft emails and documents | No | Yes — built-in | No |
| Custom AI model training | No (pre-built APIs) | No | Yes — fine-tuning and custom models |
| Meeting summaries | No | Yes — Teams Copilot | No |
The key insight: Azure AI Services, Copilot, and Foundry are complementary. A single solution might use all three:
- Copilot for everyday productivity
- AI Search for document retrieval (RAG)
- Vision for image processing
- Foundry for custom model orchestration
🔄 PacificSteel uses Vision for quality inspection
Tomás, DT Lead at PacificSteel Manufacturing, is piloting AI-powered quality inspection on the production line.
The problem: Steel sheets are inspected manually. Inspectors check 500 sheets per shift and miss about 3% of defects. A missed defect costs $12,000 in rework and customer returns.
The solution: Azure AI Vision with custom image classification.
- Training: Tomás’s team photographs 10,000 steel sheets — 8,000 good, 2,000 with various defects (scratches, pitting, discolouration). They train a custom vision model in Azure AI Services.
- Deployment: Cameras on the production line capture images of every sheet. The Vision API classifies each sheet in real time.
- Integration: Defective sheets are automatically flagged and diverted. The system logs defect types and locations for trend analysis.
Results after 90 days (in this scenario):
- Defect detection rate: improved from 97% to over 99%
- Time per inspection: under 1 second (vs 45 seconds manual)
- Estimated annual savings: in the range of $500,000–$1,000,000 in reduced rework (depending on defect rates and production volume)
Why this couldn't be done with Copilot
This is a classic example of where Azure AI Services are the right choice:
- Real-time image classification is not something Copilot does
- The solution needs to run on the production line, not inside Microsoft 365
- It requires a custom-trained vision model specific to steel defects
- The API-based architecture integrates with existing manufacturing systems
If the exam describes a specialised AI need (vision, speech, document processing) on a production or operational system, the answer is almost always Azure AI Services — not Copilot.
Elena's consulting firm has a client — a legal firm that wants to automatically extract key information (parties, dates, amounts, clauses) from thousands of contract PDFs. Which Azure AI Service should Elena recommend?
Ravi wants to build an AI assistant for TechVantage that answers employee questions using information from SharePoint, Confluence, and an internal SQL database. Which Azure AI capability enables searching across all these sources?
🎬 Video coming soon
Next up: Matching the Right AI Model to Your Business Need — learn how to choose between large models, small models, and open-source options.