What is Shashwat Prakash's area of expertise?

Shashwat Prakash is an AWS Conversational AI & Contact Center AI Engineer specializing in enterprise Voice AI, IVR Automation, Amazon Connect, Generative AI applications, RAG Pipelines, AI Agent systems with Tool Calling, Nova Sonic real-time voice AI, and Prompt Engineering. He has 6+ years of experience delivering production-grade AI solutions for enterprise clients.

Which AWS services does Shashwat Prakash specialize in?

Shashwat Prakash specializes in Amazon Connect (IVR & Contact Center), AWS Bedrock (Generative AI & LLM), Amazon Lex (NLP & Chatbots), Nova Sonic (Real-time Voice AI), Amazon Q in Connect, AWS AgentCore (MCP Server), AWS Lambda (Serverless), AWS DynamoDB, AWS API Gateway, AWS S3, AWS CloudFront, AWS Cognito, and AWS EventBridge.

What is Shashwat Prakash's experience with IVR and Voice AI?

Shashwat Prakash has extensive experience as an IVR AI Developer and Voice AI Engineer, building production Amazon Connect IVR flows, Nova Sonic real-time conversational voice AI, AI voicebots, Speech-to-Text pipelines, Text-to-Speech systems, and full contact center automation solutions for enterprise clients at PwC US and Genpact India.

Does Shashwat Prakash offer Generative AI and RAG Pipeline development?

Yes. Shashwat Prakash is an experienced Generative AI Engineer and RAG Pipeline Developer. He builds retrieval-augmented generation systems, vector database integrations, semantic search, LLM-powered applications, Prompt Engineering frameworks, and Agentic AI systems with Tool Calling using AWS Bedrock and other foundation models.

Is Shashwat Prakash available for hire as a Conversational AI consultant?

Yes. Shashwat Prakash is available as a Conversational AI Consultant and AWS Contact Center AI Engineer for enterprise projects. He can be reached through the contact form at shashwatprakash.com for consulting, collaboration, and full-time opportunities.

How do you migrate a .NET Framework application to .NET 6?

The recommended approach is incremental migration: (1) Start by upgrading to .NET Standard libraries for shared code; (2) Migrate the backend API project using the .NET Upgrade Assistant tool; (3) Replace deprecated APIs (e.g., System.Web, HttpContext) with their .NET Core equivalents; (4) Update dependency injection from Unity/Autofac to the built-in Microsoft.Extensions.DependencyInjection; and (5) Migrate module by module, running the old and new versions in parallel until each module is verified.

What are the performance benefits of migrating from .NET Framework to .NET 6?

.NET 6 delivers 2–4x faster throughput than .NET Framework for typical web workloads, thanks to the Kestrel web server, improved garbage collection, and async-first runtime design. Memory footprint is significantly reduced because .NET 6 uses a leaner runtime that doesn't carry Windows-only legacy APIs. Under concurrent load — like running multiple scraping agents simultaneously — the difference is dramatic.

Why use Angular for a web scraping platform frontend?

Angular's component-based architecture, TypeScript-first approach, and built-in RxJS observables make it well-suited for a dashboard-heavy application like a web scraping platform. Agents generate streams of results that map naturally to observable data patterns. Angular's CLI tooling also made the incremental migration from a legacy monolithic frontend straightforward.

Agenty AnalyticsDec 2020 – Feb 2022Software Engineer2 min read

Agenty Web Application — Full Stack Migration from .NET Framework to .NET 6

Migrated a legacy .NET Framework multi-agent scraping platform to .NET 6 and Angular, eliminating years of technical debt.

The Business Problem

Agenty's platform had accumulated significant technical debt on a legacy .NET Framework codebase. Performance was degrading under concurrent loads, the monolithic frontend was difficult to maintain, and adding new agent types required disproportionate effort.

Solution & Technical Overview

Led the modernisation of Agenty, a multi-agent web platform providing automated web scraping, change detection, and machine translation services. The application was originally built on legacy .NET Framework and had accumulated years of technical debt.

The project involved a complete technology stack migration: the backend was re-architected from .NET Framework to .NET 6, and the frontend was rebuilt using Angular. This wasn't a simple version upgrade — it required rethinking the architecture to leverage modern patterns like dependency injection, asynchronous programming, and component-based UI design.

Each agent module — scraping, change detection, and machine translation — was migrated incrementally to minimise disruption. The scraping agent handles automated data extraction from websites. The change detection agent monitors web pages and alerts users on content changes. The machine translation agent provides automated language translation.

The migration delivered faster startup times, reduced memory footprint, better performance under concurrent load, and a modern responsive UI. The new architecture also made the platform easier to maintain and extend with new agent types.

Key Outcomes & Results

Full stack migration from .NET Framework to .NET 6 and Angular
Incremental module-by-module migration to minimise production disruption
Improved concurrency performance and reduced memory footprint
Dependency injection and async programming patterns introduced throughout
Scraping, change detection, and machine translation agents all modernised

Technologies & Skills Used

C#.NET 6MySQLDapper ORMAngularGitHub

Frequently Asked Questions

Written by

Shashwat Prakash

AWS Conversational AI & Generative AI Developer with 6+ years building enterprise contact center solutions, voice AI agents, RAG pipelines, and AI-powered applications on AWS. Currently at PwC US, previously at Genpact and Agenty Analytics.

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