Architecting a Vernacular AI Agent for India's Linguistic Diversity

The Challenge

India's linguistic landscape presents a unique technological challenge: over 22 official languages, hundreds of dialects, and more than 90% of the population speaking languages other than English as their primary language. Digital literacy is surging in tier-2 and tier-3 cities where vernacular languages dominate, yet most advanced AI systems remain optimized for English. This creates a critical accessibility gap for government services, healthcare, education, and commerce—sectors increasingly dependent on digital interfaces but unable to serve their primary user base effectively.

The Solution

A Vernacular AI Agent built on Amazon Bedrock and Sarvam AI provides voice-first, multilingual access to AI-powered services. The architecture combines AWS's scalable infrastructure with Sarvam AI's Indian language optimization, enabling natural conversations in regional languages with automatic language detection, culturally appropriate responses, and intelligent document processing across text and image formats.

Architecture Overview

The solution architecture comprises four layers: Core AI Engine, Data Management, Application Infrastructure, and Security & Access Control.

Core AI Engine: Multilingual Intelligence

Amazon Bedrock with Claude 3 Sonnet serves as the foundational AI model, providing 400,000 input tokens and 20,000 output tokens per interaction. This capacity enables processing complete conversation histories, entire documents, and complex queries while maintaining context across multiple languages.

Sarvam AI Integration powers multilingual speech-to-text and text-to-speech capabilities, specifically optimized for Indian languages and dialects. The integration ensures accurate transcription of regional accents and natural-sounding voice output that respects linguistic nuances and cultural context.

Amazon Bedrock Agents handle domain-specific information retrieval with configurable agent IDs and alias IDs for different use cases:

• Government Schemes Agent: Retrieves information about government programs, eligibility criteria, and application procedures
• MSME Programs Agent: Provides details on micro, small, and medium enterprise support initiatives
• Custom System Prompts: Configurable behavior rules ensure agents prioritize specialized tools for specific queries while maintaining conversational flow

Data Management Layer

Amazon S3 - 15 GB bucket with lifecycle policies:

• Document Storage: Supports PDF, DOCX, TXT, CSV, JSON formats (up to 5 documents at 4.5MB each)
• Image Storage: Handles PNG, JPG, JPEG, GIF, BMP, WEBP formats (up to 20 images at 3.75MB each)
• Audio Files: Stores voice recordings for processing and playback
• Versioning: Maintains document history for audit and retrieval

Amazon RDS (PostgreSQL) - Relational data management:

• User Sessions: Session IDs, user preferences, language settings, authentication tokens
• Conversation History: Message logs, timestamps, language metadata, user interactions (limited to last 50 messages for performance optimization)
• Metadata: Document references, processing status, agent invocation logs

Amazon OpenSearch (t3.small.search) - Semantic search and retrieval:

• Vector Database: Stores document embeddings (1536-dimensional vectors, 1000 tokens per page, 800 words per page)
• Multilingual Indexing: Supports semantic search across Indian languages
• Knowledge Base: Indexed content from government schemes, MSME programs, and domain-specific information

Application Infrastructure

Streamlit Web Framework provides the user interface with voice-first interaction:

• Voice Recording: streamlit-audiorecorder enables intuitive voice capture in regional languages
• Audio Processing: Pydub and FFmpeg handle high-quality voice capture, format conversion, and playback
• Real-time Transcription: Immediate speech-to-text conversion through Sarvam AI
• Language Detection: Automatic identification of spoken language with response matching

AWS Lambda (Python 3.13) - 1024 MB memory, 30-second timeout:

• Audio Transcription: Processes voice input through Sarvam AI speech-to-text
• Document Parsing: Extracts text from PDF, DOCX, and other formats
• Image Analysis: Processes visual content with vernacular language descriptions
• API Orchestration: Coordinates calls to Bedrock, Sarvam AI, and storage services
• Key Dependencies: boto3 for AWS SDK integration, requests for HTTP communication, python-dotenv for configuration management

Amazon API Gateway - Secure, scalable API endpoints:

• Built-in Throttling: Rate limiting to prevent abuse
• Authentication: OAuth 2.0 and API key validation
• Request Routing: Directs traffic to appropriate Lambda functions
• Monitoring: CloudWatch integration for performance tracking

Security and Access Control

IAM Roles and Permissions:

• Bedrock Access: Specific permissions for bedrock:InvokeModel and bedrock:InvokeAgent actions
• S3 Access: Read/write permissions for document and audio storage
• RDS Access: Database connection credentials with least-privilege access
• Lambda Execution: VPC-secured functions with minimal required permissions

Credential Management:

• Environment Variables: Secure storage of API keys and configuration through python-dotenv
• API Key Rotation: Automated rotation policies with AWS Secrets Manager
• Audit Logging: AWS CloudTrail tracks all API calls and access patterns

Encryption:

• Data at Rest: AES-256 encryption for S3 and RDS
• Data in Transit: TLS 1.3 for all API communications
• Voice Data: Encrypted audio files with automatic deletion after processing

Conversation Management

• Session Persistence: Maintains context across multi-turn conversations with automatic language detection and response generation
• Chat History Optimization: Limits conversation history to last 50 messages to prevent memory issues and optimize performance while maintaining sufficient context
• Real-time Processing: Immediate transcription, translation, and response generation with latency under 2 seconds for voice interactions

Implementation Considerations

• Language Model Configuration: Configure Claude 3 Sonnet with system prompts that prioritize specialized Bedrock Agents for domain-specific queries (government schemes, MSME programs) while maintaining natural conversational flow.
• Sarvam AI Integration: Implement speech-to-text and text-to-speech endpoints with language-specific models optimized for Indian languages. Configure audio format conversion (WAV, MP3, OGG) for compatibility across devices.
• Document Processing Pipeline: Build Lambda functions to extract text from multiple formats (PDF, DOCX, TXT, CSV, JSON), generate embeddings using 1536-dimensional vectors (1000 tokens per page, 800 words per page), and index content in OpenSearch for semantic retrieval.
• Voice Interface Design: Implement streamlit-audiorecorder for browser-based voice capture, Pydub for audio processing, and FFmpeg for format conversion. Ensure mobile responsiveness for tier-2 and tier-3 city users.
• Performance Optimization: Implement conversation history limiting (50 messages), caching for frequently accessed documents, and parallel processing for document analysis and voice transcription.
• Monitoring and Logging: Configure CloudWatch for Lambda execution metrics, API Gateway request tracking, and error logging. Set up alarms for high latency (>2s) and error rates (>2%).

Conclusion

This Vernacular AI Agent architecture demonstrates how Amazon Bedrock's advanced language understanding, combined with Sarvam AI's Indian language optimization and AWS's scalable infrastructure, can bridge India's linguistic divide. By processing 400,000 tokens of context per interaction, supporting voice-first interactions in multiple Indian languages, and handling diverse document formats, the solution makes AI-powered services accessible to millions of non-English speakers.