Build Generative AI Applications on AWS: Leverage Your Internal Data with Amazon Bedrock

Generative AI has evolved from experimental technology into an essential business tool. Organizations are implementing AI solutions to automate document analysis and enhance customer support, but the real challenge lies in moving beyond generic answers to unlock insights that reflect a company's unique knowledge.

This is where your internal data becomes your greatest asset. This blog demonstrates how to use Amazon Bedrock to build AI applications that focus specifically on transforming your proprietary documents, from technical manuals to internal policies, into a secure and accurate knowledge assistant. We'll show you how to leverage what your company already knows to solve your most specific problems.

Your Internal Data is a Game-Changer

Public AI models are great at answering general questions because they've learned from the entire internet. But they don't know anything about the things that make your business special: your products, your customers, and how your team works. This is where your own company data gives you a huge advantage.

When you provide your business's private documents to an AI model, you’re not just making a simple chatbot. You're building a smart assistant that truly understands how your business works.

Here’s why that’s so important:

• It Gives Answers That Are Actually Relevant: A public AI model can give you a generic sales tip. But an AI model trained on your data can look at your actual sales reports, reference your company’s unique sales guide, and help draft an email to a specific customer based on their history with you. It knows what's actually going on in your business.
• It Creates an Advantage No One Can Copy: Any of your competitors can use the same public AI tools. But they don’t have access to your private files, like your internal technical guides, project histories, or customer support notes. When you use this information to power your AI model, you create something valuable that no one else can replicate.
• You Can Trust the Answers: Public AI models can sometimes make mistakes or invent information. When your AI model gets its answers directly from your own company documents, you can be much more confident that the information is accurate. For example, instead of a vague answer about HR rules, an employee gets a specific answer pulled straight from your official company handbook.

By focusing on your own data, you stop using AI as a generic solution and start building a tool that solves your company's real, day-to-day problems.

How Amazon Bedrock Works

Amazon Bedrock provides managed access to foundation models through a unified API, eliminating the complexity of managing multiple model deployments. The service operates on a serverless architecture, automatically handling scaling, availability, and infrastructure management.

Key architectural components include:

• Model Access Layer: Single interface to models from Anthropic, AI21 Labs, Cohere, Meta, Stability AI, and AWS
• Security Integration: Native VPC endpoints, AWS IAM policies, and encryption at rest and in transit
• Monitoring and Compliance: Amazon CloudWatch integration, AWS CloudTrail logging, and HIPAA eligibility
• Cost Management: Pay-per-token pricing model with no upfront commitments

The serverless nature of Amazon Bedrock is particularly valuable for enterprise deployments. Unlike self-managed model hosting, there's no need to provision GPU instances or manage model versioning. The service automatically scales based on request volume, ensuring consistent performance during peak usage periods.

Think of Amazon Bedrock as a toolbox filled with various specialized tools. Now that you understand how the toolbox works, the next step is to pick the right tool for your specific job. Since our goal is to build a smart assistant that understands your company's private data, choosing the right model is crucial. Different models have different strengths, and the best choice depends on what kind of information you're working with and what you need the AI to do.

Choosing the Right Model for Your Use Case

Amazon Bedrock provides access to a variety of powerful AI models. Each one excels at different things. You wouldn't use a sledgehammer to hang a picture frame – the same principle applies here. The key is to match the model to your specific business needs.

Anthropic's Claude Family

• Claude Opus: The most powerful and thoughtful model. Use this for complex tasks that require in-depth reasoning, such as analyzing detailed legal documents or lengthy financial reports from your internal archives. The latest model in this family is Claude 4 Opus.
• Claude Sonnet: A great balance of smarts and speed. This is your go-to for everyday business tasks, such as summarizing meeting notes, answering employee questions from an HR handbook, or drafting customer support emails. The latest model is Claude 4 Sonnet.
• Claude Haiku: The fastest and most affordable. Perfect for simple, high-volume tasks, such as categorizing customer feedback or quickly retrieving a specific piece of information from a technical manual. The latest model is Claude 3.5 Haiku.

AWS’ Models

• Amazon Titan Text: A strong, general-purpose model that’s great for summarizing internal reports and answering questions based on your documents.
• Amazon Titan Embeddings: This is a crucial background player. It’s specially designed to understand the meaning behind words, which is essential for powering the search function in your internal knowledge base.
• Amazon Nova: The latest generation model, optimized for creating natural-sounding conversational assistants and processing your internal documents efficiently. This family includes Nova Micro (suitable for basic tasks), Nova Lite (ideal for small processing tasks), Nova Pro (for more complex processing), and Nova Premier (the latest Nova model, capable of handling the most difficult tasks). 

Specialized Models for Specific Jobs

• Cohere Command: Excellent at following very specific instructions, making it perfect for automating structured business workflows.
• AI21 Labs Jamba: Designed for efficiency, these models are a great choice for generating content at a very large scale.
• Meta Llama: A family of flexible models that gives you the freedom to customize and build more tailored AI solutions.

Making Smart Choices: How to Balance Cost and Performance

Using the most powerful AI model for every single task is like paying for a sports car just to drive to the grocery store – it's overkill and gets expensive fast. The most effective approach is to utilize different models for various needs to achieve the best results while staying within the budget.

Here's a simple, cost-effective strategy:

• Simple Questions, Simple Models: For straightforward tasks like finding a policy in an employee handbook, use a fast and affordable model like Claude Haiku. You'll get quick answers and pay less.
• Complex Analysis, Powerful Models: When you need the AI to analyze a complex spreadsheet or summarize a 50-page technical document, bring in a more powerful model like Claude Opus. It costs more per use, but it delivers the high-quality reasoning you need for the job.
• Build a Smart Router: The best systems automatically send a user's request to the right model. For example, a system can be set up to route simple customer questions to Haiku but send requests for "in-depth competitive analysis" to Opus. This ensures you're only paying for high performance when you actually need it.

By considering model selection in this way, you transition from just using AI to strategically managing it for optimal business value.

Building a Knowledge Assistant with Your Internal Data

To demonstrate practical implementation, we'll build an internal knowledge assistant that answers employee queries using company documentation. This pattern addresses the common challenge of information discovery across distributed document repositories.

Architecture Overview

The solution implements a retrieval-augmented generation (RAG) pattern with the following components: