Category: Generative AI

TEKsystems Global Services (TGS) helped a large media and entertainment enterprise transform their Amazon Connect Customer automated evaluation system from 66.67 percent accuracy to 100 percent. They developed a strategic classification framework that matches each evaluation question to the optimal automation method, either generative AI, conversational analytics categories, or contact metrics. This layered approach eliminated AI hallucinations, enabled full interaction coverage, and turned quality assurance from an unreliable manual process into a trusted, production-ready system that delivers actionable coaching insights at scale.

As generative AI adoption accelerates, the gap between deploying automation and making it trustworthy is becoming the critical differentiator. The next frontier of multi-modal insights and real-time coaching starts with the right foundation.

If your RAG deployment has plateaued—struggling with cross-domain reasoning, noisy retrieval at scale, or a model that never truly learns your business—you’re not alone. In this post, we explore how 4MINDS’s continuous model adaptation on AWS moves beyond RAG’s stateless architecture by building enterprise knowledge directly into model parameters, enabling AI that improves with every interaction. Learn how this AWS Marketplace solution integrates with Amazon Bedrock and Amazon SageMaker to deliver domain-adapted models without the operational burden of traditional fine-tuning.

Aikido Security and Kiro help development teams secure the AI development lifecycle without slowing release velocity. The Aikido Kiro Power brings automated vulnerability detection, secrets scanning, and infrastructure as code checks directly into the agentic development workflow, with findings surfacing in context and fixes applying automatically in under 30 seconds. This post explores how the Aikido Power works, how to install it, and how organizations across startups, ISVs, and regulated enterprises benefit from continuous security that runs alongside every code change.

The AI-Driven App Modernization Platform from Pega and AWS enables enterprises to incrementally transform legacy applications from mainframes to monolithic applications into intelligent, cloud-native workflows using AI-powered discovery, low-code design, and the strangler fig pattern, cutting modernization timelines from years to months without disrupting critical business operations.

DevRev built real-time AI guardrails into its enterprise agent system, powered by Amazon Nova 2 Lite through Amazon Bedrock. The guardrail layer evaluates every user input against default and custom policies in 247ms average latency without degrading the conversational experience. This post explores how DevRev delivers high support ticket automation and cost reduction while keeping content on-policy and safety controls firm.

Learn how DXC Technology used Amazon Q Developer to help its customer modernize over 80 microservices and multiple micro-frontend applications, upgrading from older Angular and .NET versions while achieving a 60–75% reduction in overall upgrade effort. By combining structured prompt engineering with AI-driven automation, DXC transformed a high-risk, months-long modernization initiative into a fast, secure, and scalable success, saving an estimated 1,200 to 1,400 developer hours while strengthening security and compliance across the platform.

As AI workloads scale on AWS, the API layer becomes the primary security surface where every prompt, model response, and agent action flows through. AWS foundational controls and Wallarm runtime API security work together to deliver continuous discovery, behavioral threat detection, and output validation, helping organizations secure agentic systems without re-architecting their environments.

As cloud-native environments expand, security and operations leaders face mounting pressure to reduce response times while managing growing alert volumes and team capacity. Elastic’s AI-powered features on Amazon Bedrock automate the path from detection to resolution across both security and observability workflows. Learn how conversational agents, automated workflows, and custom AI agents accelerate incident response and root cause analysis on AWS.

Learn how the integration of Amazon Nova multimodal intelligence with Bria Fibo’s deterministic visual foundation model solves the enterprise challenges of unpredictable outputs, inconsistent branding, and legal uncertainty in AI image generation. This post explores how structured Visual Generation Language (VGL) and 100% licensed training data deliver reproducible, brand-consistent visuals at scale—transforming generative AI from an experimental technology into a production-ready content pipeline available in AWS Marketplace and Amazon SageMaker AI.

What if one database could handle transactions, analytics, and semantic vector search for your AI agents—all without managing separate systems? TiDB Cloud on AWS, integrated with Amazon Bedrock, delivers a unified, serverless architecture purpose-built for the demands of autonomous AI. In this post, PingCAP and AWS walk you through the reference architecture, real-world use cases, and the key design decisions to make your generative AI applications production ready using the TiDB console.