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Cloud-Based CRM Modernization Helped a UK Based Organization Scale Faster and Reduce Deployment Complexity

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Build Smart Workflow with Intelligent Automation and Analytics

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Scalable Generative AI Solutions with AWS

Enterprises are moving beyond pilots and need AI that can run reliably in production. Generative AI on AWS provides the foundation to build, deploy, and scale workloads with enterprise-grade security, fine-tuned models, and flexible deployment options. It integrates seamlessly with existing AWS tools, helping teams streamline workflows, reduce complexity, and meet compliance requirements without slowing down innovation.

From creating internal copilots that enhance productivity to automating content generation and improving developer workflows, Generative AI on AWS makes real business impact possible. It allows organizations to adopt AI at scale with confidence, ensuring stability, scalability, and measurable outcomes that go beyond experimentation.

Agentic AI advancements

Build and scale your agentic AI initiatives with secure infrastructure, flexible tools, and production-ready services designed to handle real-world business needs.

Amazon Bedrock AgentCore

AgentCore lets you create and manage AI agents that can handle real tasks across different business environments. You can use models from leading providers or your own and run them within secure AWS infrastructure. It supports multiple frameworks and gives you built-in tools to manage agent behavior, security, and orchestration in production systems.

Strands Agents 1.0

Strands 1.0 is a software development kit that helps you build AI systems made up of multiple interacting agents. Each agent can handle part of a task, and together they solve more complex problems. This SDK supports structured design, coordination logic, and testing tools, so you can take your AI solution from prototype to production with less trial and error.

Amazon Nova customization

Nova gives you control over how foundation models understand text, images, or user prompts. Using SageMaker, you can fine-tune these models on your own data and adjust them to fit your specific domain, task, or workflow. Nova customization supports transparency and control at every step.

A Full Stack of AWS Generative AI Services 

Build what you need using modular tools from across the AWS Generative AI ecosystem. Choose your models, add context, and scale confidently with managed infrastructure.

Application Services

  • Amazon Q Business

    Enterprise-grade assistant to surface answers across internal tools, documents, and apps.

  • Amazon Q Developer

    Support DevOps with natural language debugging, deployment support, and code generation.

  • Amazon Q in QuickSight

    Analyze dashboards and business data using simple questions and automated summaries.

  • Amazon Q in Connect

    Improve agent productivity with in-call suggestions based on accurate context.

Application Services

Model Access & Orchestration

  • Amazon Bedrock

    Central hub for building with FMs from providers like Anthropic, Meta, Cohere, and Mistral, all via one API.

  • Custom Models & Personalization

    Fine-tune or import your own models to deliver tailored experiences using secure datasets.

  • Bedrock Knowledge Bases

    Enable retrieval-augmented generation (RAG) using your data from S3, OpenSearch, MongoDB, and more.

  • Bedrock Agents & Function Calling

    Automate multistep logic with API workflows driven by natural prompts.

  • Guardrails

    Define policies and output filters to ensure accuracy, compliance, and safe use.

Model Access

Infrastructure to Train, Tune, and Deploy

  • Amazon SageMaker

    Build, test, and deploy ML and Generative AI AWS models with MLOps-ready pipelines.

  • AWS Trainium & Inferentia

    Reduce training and inference costs with purpose-built Gen AI chips.

  • Security & Governance

    Deploy with full encryption, access controls, and regional failover.

Model Access

Why Generative AI on AWS?

Generative AI is moving fast, but building with it in a secure, scalable, and reliable way still takes careful planning. Many teams hit roadblocks when trying to move from a working prototype to something that can run safely in production. That’s where AWS makes a difference.
AWS brings together everything teams need to move beyond experiments and build generative AI into real products and workflows.

AWS has decades of experience running cloud infrastructure for the most demanding industries. That same platform now supports generative AI services—so you can run AI workloads knowing that security, scalability, and compliance are covered. Whether your data is sensitive, regulated, or high-volume, AWS services are designed to meet enterprise standards. You get built-in monitoring, encryption, and identity controls that help you confidently deploy generative AI tools in real business environments.

With Amazon Bedrock, you’re not tied to one provider or model type. You can build and test with leading foundation models from trusted names like Anthropic (Claude), Mistral, Meta (LLaMA), and AI21, all through one unified interface. This flexibility makes it easier to try different approaches, compare performance, and adapt quickly as your needs change. You don’t have to manage multiple environments or integrations—AWS takes care of that for you.

Running generative models typically requires expensive GPU infrastructure and DevOps support. On AWS, those barriers disappear. You can call models through APIs, fine-tune them using managed services, and scale your workloads without worrying about provisioning hardware or building custom pipelines. That means faster setup, lower overhead, and fewer roadblocks when you need to move from testing to production.

AWS understands that enterprise teams need more than quick demos—they need reliability, governance, and adaptability. GenAI tools on AWS come with built-in observability features, fine-grained access controls, and the ability to customize agent behavior or model responses to fit your use case. You can manage data inputs, audit model activity, and integrate securely with internal systems—all while keeping pace with changing business demands.

Tools That Make AI Practical

Amazon Bedrock

Use your choice of models without provisioning servers. Add context, personalize, and deploy securely.

Bedrock Knowledge Bases

Pull trusted data from enterprise sources to ground responses with real-time relevance.

Bedrock Agents

Power internal workflows like procurement or onboarding, without custom logic or code.

Amazon Bedrock IDE

Design Gen AI apps visually. Test, debug, and deploy without deep DevOps support.

Amazon SageMaker

Evaluate model performance, experiment with training pipelines, and manage lifecycle governance.

Need help picking the right Gen AI tool?

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Common Use Cases We Help You Ship

Enterprise Copilots

Amazon Q + Bedrock Knowledge Bases for contextual assistance

Customer Service Automation

Amazon Q in Connect + Bedrock Agents for support

Content Generation

Bedrock FMs + custom models for product and marketing assets

Developer Productivity

Q Developer + Bedrock IDE for scripting, testing, and setup

Business Analytics

Q in QuickSight for AI-based insights and trend summaries

Resources to Learn and Build

PartyRock

Test generative AI ideas in a visual, no-code playground and see model feedback instantly as you iterate.

AWS Skill Builder

Build your foundational and advanced Gen AI skills through guided, self-paced learning paths.

Developer Center

Explore SDKs, API docs, and code examples to start building generative AI apps with real use cases.

Amazon Q Introduction

Learn how Amazon Q supports everyday tasks and boosts productivity across AWS environments.

Community Hub

Join discussions, forums, and learning groups to exchange ideas and stay current with Gen AI trends.

Custom Model Tools

Fine-tune foundation models using your own data and adapt them to fit your specific business needs.

Client Results

ASP GSP | Enterprise Modernization at Scale

Faced with performance limits and licensing debt, ASP GSP partnered with Cygnet.One to modernize across core applications using container-first models and CI/CD.

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30%

cost savings

40%

faster go-lives

100%

audit compliance post-migration

Client Results

BNPL Fintech | Microservices Migration on AWS

To support rapid growth, this fintech firm rearchitected with our container + serverless model.

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45%

faster releases

Zero

downtime across region rollouts

Full

observability from day one

Client Results

Renticy | Automating Operations with Serverless

We rebuilt legacy modules with AWS Lambda and Step Functions for faster scale and lower support overhead.

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60%

fewer manual errors

Real-time

reporting

5 minutes

Infrastructure scale-up

Work with AWS Partners to accelerate Gen AI development in your org 

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