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Home/Services/Azure AI/AI Language
Azure AI Language · NLP & Text Intelligence India

Extract Intelligence from
Text at Enterprise Scale

Azure AI Language (formerly Text Analytics + LUIS + QnA Maker) is Microsoft's unified NLP service — providing sentiment analysis, key phrase extraction, named entity recognition, PII detection, Conversational Language Understanding (CLU), custom text classification, and question answering via REST APIs. SchwettmannTech implements Azure AI Language for Indian enterprises building multilingual customer analytics, support automation, compliance document processing, and conversational AI applications.

Azure AI Language Certified Partner India
Hindi · Tamil · Telugu NLP Models Built
CLU & Custom NLP Models Deployed
95%
Intent recognition accuracy (CLU)
40+
Languages including Indian scripts
PII
DPDP Act compliant PII detection
Days
Custom NLP model deployment time
Azure AI Language · NLP Analytics Dashboard
Live
48,200
Texts Analysed Today
↑ Auto-scale
94.8%
CLU Accuracy
↑ Custom Model
18ms
API Latency
↓ Optimised
0
PII Leaks
✓ DPDP Act
CLU — Support Ticket Intent Classification
28 intents · Hindi+English · 94.8% accuracy
Live
PII Detection — Customer Communications
Aadhaar masking · PAN redaction · DPDP compliant
Active
Sentiment Analysis — Product Reviews
48,200 reviews/day · Aspect-based · 5 categories
Running
Custom Q&A — Policy Knowledge Base
2,400 Q&A pairs · Confidence scoring · D365 Bot
Serving
Language AI: Aspect-based sentiment analysis detected "delivery speed" as the top negative driver in product reviews this week (sentiment score -0.72 across 2,100 mentions). Recommend escalating to supply chain team for investigation.
95%
CLU Intent Recognition Accuracy
40+
Languages including Hindi Tamil Telugu
PII
DPDP Act Compliant Detection & Redaction
Days
Custom NLP Model Training Time
Conversational Language UnderstandingCustom Text ClassificationSentiment AnalysisAspect-based SentimentNamed Entity RecognitionPII DetectionKey Phrase ExtractionLanguage DetectionCustom Q&AHindi NLPTamil NLPTelugu NLPConversational Language UnderstandingCustom Text ClassificationSentiment AnalysisAspect-based SentimentNamed Entity RecognitionPII DetectionKey Phrase ExtractionLanguage DetectionCustom Q&AHindi NLPTamil NLPTelugu NLP
Services

Azure AI Language Implementation Services

Our certified NLP engineers build custom Azure AI Language models and integrate them into your Dynamics 365, Power Platform, and custom applications.

Conversational Language Understanding (CLU)
Replace legacy LUIS apps with the new CLU service — training custom intent recognition and entity extraction models for chatbots, virtual agents, and voice applications. CLU handles multi-turn conversations, multi-intent utterances, and multilingual inputs including Hindi, Tamil, and Telugu.
CLU · Intent Recognition · Entity Extraction · Multilingual · Bot Integration
Custom Text Classification
Train custom single-label and multi-label text classifiers on your domain-specific documents — support ticket categorisation, contract clause classification, news/regulatory monitoring, and product review tagging. Models train in hours on labelled datasets and deploy as managed Azure endpoints.
Custom Classification · Document Tagging · Support Routing · Domain NLP
Sentiment Analysis & Opinion Mining
Analyse customer feedback, social media, survey responses, and review text for sentiment and opinion. Aspect-based opinion mining identifies which specific product or service attributes drive positive or negative sentiment — enabling targeted improvement actions rather than overall score tracking.
Sentiment Analysis · Opinion Mining · Aspect-based · Customer Feedback
PII Detection & Redaction
Detect and redact personally identifiable information — names, Aadhaar numbers, PAN cards, phone numbers, email addresses, bank accounts, and custom India-specific entity types — from documents, emails, and chat logs. Essential for DPDP Act 2023 compliance in document processing workflows.
PII Detection · Aadhaar Masking · PAN Redaction · DPDP Act · Compliance
Named Entity Recognition
Extract people, organisations, locations, dates, quantities, and custom entities from unstructured text. NER populates Dynamics 365 records from email content, extracts contract parties and dates from legal documents, and identifies product and competitor mentions from social media monitoring feeds.
NER · Entity Extraction · D365 Auto-populate · Contract Parsing · Social Media
Custom Question Answering
Build FAQ knowledge bases and document Q&A systems using Azure Custom Question Answering — training on your policy documents, product manuals, and HR handbooks. Confidence-scored answers with source citations integrate directly into Copilot Studio bots and D365 knowledge articles.
Custom Q&A · FAQ Bot · Policy Knowledge · Source Citations · Copilot Studio
Capabilities

Complete Capability Coverage

Our certified team covers every facet of this service — from strategy and implementation to managed operations and continuous optimisation.

🧠NLP

Unified Language Intelligence

Azure AI Language unifies six NLP capabilities — sentiment, NER, key phrases, PII, CLU, and Q&A — under a single API endpoint, billing model, and management console. No more managing separate LUIS, Text Analytics, and QnA Maker resources.

  • Single API Endpoint
  • Unified Billing
  • Consistent Auth
  • One Management Console
🇮🇳India

Indian Language NLP

Azure AI Language supports Devanagari, Tamil, Telugu, Kannada, Bengali, and Gujarati scripts for sentiment analysis, NER, and key phrase extraction. CLU supports Hindi and regional Indian languages for conversational understanding — critical for India-market applications.

  • Hindi Sentiment
  • Tamil NER
  • Telugu CLU
  • Multi-script Support
🔒Compliance

PII & DPDP Compliance

PII detection identifies 40+ personal data types including India-specific entities (Aadhaar, PAN, IFSC codes). Redaction removes or masks PII before storage or downstream processing — creating DPDP Act compliant text processing pipelines.

🔗Integration

Power Platform Native

Azure AI Language integrates with Power Automate via built-in connectors — no code needed for sentiment tagging, entity extraction, and PII redaction in document processing flows. Power Apps canvas apps call Language APIs directly for real-time text analysis.

Analytics

Customer Intelligence

Aggregate sentiment and opinion mining results in Power BI — tracking sentiment trends by product, region, channel, and time period. Surface the specific aspects driving satisfaction or dissatisfaction for targeted business action.

Automation

CLU for Bot & IVR

CLU is the NLU backbone for Copilot Studio bots and Azure Bot Service applications — providing more accurate intent recognition than the previous LUIS service, with support for overlapping intents, ambiguous utterances, and code-switched Hindi-English text common in Indian enterprise contexts.

Speed

Hours Not Months

Custom NLP model training in Azure AI Language takes hours, not the weeks or months required for building equivalent models from scratch. Custom text classifier: training on 100 labelled examples completes in under 2 hours. Custom NER: 200 labelled entities trains in 3–4 hours.

Delivery

Our Azure AI Language Delivery Process

From labelling your training data to production NLP integration — typically 1–3 weeks.

1
Phase 1 — Week 1
Use Case Definition & Data Labelling
Define NLP task and label training data
Define the NLP problem (classification, NER, CLU, or Q&A). Collect and label training data using Azure Language Studio's built-in labelling tool. For CLU: 15+ utterances per intent. For classification: 50+ examples per class. For NER: 200+ entity annotations.
Use Case SpecData LabellingAzure Language Studio
2
Phase 2 — Week 1–2
Model Training & Evaluation
Train, evaluate & tune custom model
Train custom model in Language Studio. Evaluate precision, recall, and F1 on validation set. Tune labelling coverage for low-performing intents or entity types. Compare against pre-trained baselines to validate custom training benefit.
Model TrainingF1 EvaluationError AnalysisTuning
3
Phase 3 — Week 2–3
Integration & Deployment
Connect to apps and deploy endpoint
Deploy trained model to Language Studio managed endpoint. Integrate via REST API or Power Automate connector into Dynamics 365, bot, or custom application. Configure PII detection pipeline if in scope. Set up monitoring.
API IntegrationD365 / Bot ConnectPII PipelineMonitoring
Industries

Azure AI Language for Indian Industries

NLP solutions deployed for Indian language content and India-specific compliance requirements.

BFSI · Compliance NLP
Healthcare · Clinical Text
Manufacturing · QC Reports
Retail · Review Analytics
Telecom · IVR NLU
EdTech · Content Tagging
Real Estate · Contract NLP
Energy · Regulatory Text
India-Specific NLP Capabilities

SchwettmannTech has trained custom Azure AI Language models on Indian enterprise datasets: GST/tax document entity extraction (GSTIN, HSN codes, tax amounts), Indian legal document clause classification (RERA, IBC), code-switched Hindi-English sentiment analysis for customer feedback, and CLU models for Hindi IVR applications with regional accent variation. PII detection extended with custom India-specific patterns: Aadhaar, PAN, IFSC, EPIC (Voter ID), and passport number formats.

Language AI Impact

Proven Results: Azure AI Language Results

Outcomes from SchwettmannTech's Azure AI Language implementations across Indian enterprises.

95%
CLU intent recognition accuracy on custom Indian enterprise models
80%
Reduction in manual support ticket classification after custom NLP deployment
PII
100% of documents processed through PII detection pipeline for DPDP compliance
Days
Custom NLP model from labelled data to production endpoint
Customer Stories

What Our Clients Say

"SchwettmannTech built a custom CLU model for our Hindi and English contact centre IVR. The model understands Indian English accents, code-switching between Hindi and English, and our 34 specific service intents with 96.2% accuracy — far beyond the generic LUIS models we tried previously. IVR containment rate improved from 42% to 71%."

AT
Arun Tiwari
Head of Digital Channels · Insurance, Pune

"We process 15,000 customer emails a day in Hindi and English. Azure AI Language's custom text classifier now automatically routes them to the right team with 94% accuracy — billing queries, technical issues, complaints, and general enquiries. Manual triage time dropped from 3 hours to 15 minutes daily. The DPDP PII redaction pipeline runs on every email before it's stored in D365."

PM
Priya Mahajan
VP Customer Operations · NBFC, Mumbai

"Our legal team reviews hundreds of contracts a week. SchwettmannTech's custom NER model extracts parties, effective dates, payment terms, penalty clauses, and governing law from contract PDFs — populating D365 fields automatically. Contracts that took 45 minutes to log now take 3 minutes of human review. The model handles Indian legal language including references to Indian acts and court jurisdictions."

RK
Rajesh Krishnan
Head of Legal · Infrastructure Company, Delhi
FAQs

Common Azure AI Language Questions

Have an NLP use case? Our language AI specialists provide free feasibility assessments including data requirements and expected accuracy benchmarks.

Talk to an NLP Specialist
Pre-trained Language service capabilities (sentiment analysis, NER, key phrase extraction, PII detection) work out-of-the-box on general text without any training — useful for standard analytics tasks. Custom capabilities (Custom Text Classification, Custom NER, CLU, Custom Q&A) require labelled training data but deliver significantly higher accuracy on domain-specific content: legal, medical, financial, or industry-specific text that the pre-trained general models haven't been optimised for. For most enterprise applications with domain-specific vocabulary and entities, custom models outperform pre-trained by 15–30% accuracy.
Azure AI Language's custom capabilities are designed to work with small datasets: Custom Text Classification requires as few as 10 labelled examples per class (50+ recommended for production); Custom NER requires 50+ entity annotations per type; CLU requires 15+ utterances per intent for initial training, 30–50+ for good production accuracy. This is dramatically less than training general deep learning NLP models from scratch. SchwettmannTech helps with the most time-consuming part — data labelling — using Language Studio's efficient labelling UI and active learning to prioritise which examples to label next.
Yes — Azure AI Language supports Unicode input including Devanagari (Hindi, Marathi, Sanskrit), Tamil, Telugu, Kannada, Bengali, Gujarati, and Punjabi Gurmukhi scripts for sentiment analysis, NER, and key phrase extraction. CLU supports Hindi and regional Indian languages for intent recognition. For custom models, the training data you provide can be in any of these scripts — the service handles tokenisation and script-specific processing internally. Code-switched text (Hindi sentences containing English words) is also handled, which is essential for real-world Indian enterprise NLP where employees and customers frequently mix languages.
The pre-trained PII detection model identifies standard international PII types (names, phone numbers, email, addresses, dates, credit card numbers). For India-specific identifiers — Aadhaar numbers (12-digit format), PAN cards (ABCDE1234F format), IFSC codes, EPIC voter ID numbers, and passport numbers — we extend PII detection using custom entity recognition trained on Indian PII patterns. This combined approach ensures DPDP Act 2023 compliance by detecting all Indian personal data types before documents are stored or transmitted. Redaction replaces detected PII with redacted markers, producing a clean version safe for storage or downstream processing.
Yes — this is one of our most common D365 integration patterns. When a support ticket arrives via email or web form, a Power Automate flow calls Azure AI Language's CLU endpoint to classify the intent (billing query, technical issue, complaint, etc.) and extract key entities (product name, issue description, account number). The classification and entities are written back to the D365 Case record, routing the case to the correct queue and pre-populating fields — eliminating manual triage. We've implemented this for contact centres processing 5,000–50,000 cases/day with 92–96% classification accuracy.

Add NLP Intelligence to Your Enterprise Applications

Book a free Azure AI Language Discovery Call. We'll assess your text data, demonstrate CLU or sentiment analysis on your samples, and deliver an NLP implementation plan.

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