Introduction Chatbots aren’t new. But GenAI-powered chatbots? That’s a different ball game, especially when deployed securely, scalably, and serverlessly on AWS. In this guide, we’ll show you how to build and deploy a production-grade chatbot on AWS using: High-Level Architecture SQL User → Amazon Lex → AWS Lambda → Amazon Bedrock (Claude/Titan) → Response → […]
Deploying Chatbots on AWS with Bedrock + Lex + Lambda Read More »
Introduction In 2025, GenAI is no longer limited to just words on a screen.From images to text, audio to documents, multi-modal models are now shaping how we interact with AI-powered applications.So, where does AWS stand in this multi-modal future?Let’s explore what’s possible right now on AWS when it comes to multi-modal GenAI and how to
Multi-Modal Models on AWS: What’s Possible Today? Read More »
Introduction Large language models are brilliant, but they forget things.They can’t answer questions about your private docs or industry-specific data unless you fine-tune them or… use RAG.RAG (Retrieval-Augmented Generation) is the fastest, safest way to make GenAI models useful for your data, without retraining anything.In this post, we’ll explain how to build a RAG pipeline
RAG on AWS: Retrieval-Augmented Generation Architecture & Best Practices Read More »
Introduction As GenAI moves from demos to production, security and compliance move from afterthought to foundation.Whether you’re building a chatbot, document processor, or AI agent, trust matters.And in regulated industries like finance, healthcare, or the public sector, it’s non-negotiable.In this post, we’ll walk through the key security and compliance controls for building GenAI apps on
Security & Compliance for GenAI Apps on AWS Read More »
Introduction For financial services companies, manual review is one of the biggest operational burdens, especially in areas like KYC (Know Your Customer), fraud detection, and compliance reporting.In this case study, we explore how a mid-sized fintech firm used AWS’s GenAI stack to automate document review, reduce turnaround time, and improve accuracy. Company Snapshot Problem Breakdown
Case Study: How One Fintech Company Used AWS GenAI to Cut Manual Review by 70% Read More »
Introduction Automation isn’t new. Businesses have been scripting workflows and streamlining processes for decades.But in 2025, AI-fueled automation has fundamentally changed the game.This is no longer about “if-this-then-that.” It’s about if-this-then-predict-that, adjust-this, and learn-from-that, without writing thousands of lines of logic.In this kickoff post, we define what AI-powered automation really means today, how it’s different
Automation with AI: What It Really Means in 2025 Read More »
Introduction In the age of GenAI, automation is no longer just about replacing human effort—it’s about enhancing decision-making, injecting intelligence into repetitive workflows, and freeing teams from manual bottlenecks.With AWS Step Functions + Lambda + Bedrock, you can build AI-powered automation pipelines that are not only event-driven but also context-aware, scalable, and enterprise-secure.This post walks
How to Automate Business Workflows with AWS Step Functions + Lambda + GenAI Read More »
Introduction Customer support is evolving. In 2025, users don’t want to wait in queues or repeat themselves; they expect instant, intelligent responses.With Amazon Lex handling conversations and Amazon Bedrock generating contextual replies, you can automate 60–80% of common support interactions, without sacrificing personalization.This post walks through how to build an AI-powered customer support bot using
AI-Powered Automation for Customer Support Using Lex + Bedrock Read More »
Introduction DevOps thrives on automation. But as environments grow more complex, static automation rules can create just as much friction as they remove.AWS already gives us powerful native automation tools like EventBridge and CodePipeline.Now, by integrating AI into that workflow, you can build adaptive pipelines; pipelines that change behavior based on context, risk level, and
Workflow Automation in DevOps: AI + EventBridge + CodePipeline Read More »
Introduction Logs, alerts, and support tickets are the lifeblood of operational visibility in DevOps and IT teams.But the sheer volume can overwhelm even the best engineers, especially when you’re working across multiple AWS accounts, services, and regions.Instead of manually parsing hundreds of lines of logs or clicking through dozens of alerts, you can now use
How to Use AI to Summarize Logs, Alerts & Tickets Automatically Read More »
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