Monthly Archives: April 2025

Amazon Q Business is a generative AI-powered assistant that answers question, provides summaries, generates content, and securely completes tasks based on enterprise data and information. It connects to company data sources, applications, and internal systems to provide relevant, contextual answers while maintaining organizational security and compliance standards.

Today, we’re excited to announce that Amazon Q Business now supports anonymous user access. With this new feature, you can now create Amazon Q Business applications with anonymous user mode, where user authentication is not required and content is publicly accessible. These anonymous user applications can be used in use cases such as public website Q&A, documentation portals, and customer self-service experiences.

This capability allows guest users to use Amazon Q Business generative AI capabilities to quickly find product information, get technical answers, navigate documentation, and troubleshoot issues. Your public-facing websites, documentation, and support portals can now deliver the same powerful AI-driven assistance that authenticated users receive, creating an experience that enriches the guest user journey across your digital environments.

With this launch, you can seamlessly integrate an anonymous Amazon Q Business application into your websites and web applications through two pathways: either by embedding the ready-to-use web experience into your websites using an iframe for quick deployment, or by using our Chat, ChatSync, and PutFeedback APIs to build completely customized interfaces within your own applications. For anonymous Amazon Q Business applications, we’ve implemented a simple consumption-based pricing model where you’re charged based on the number of Chat or ChatSync API operations your anonymous Amazon Q Business applications make.

In this post, we demonstrate how to build a public-facing generative AI application using Amazon Q Business for anonymous users.

Solution overview

In this solution, we walk you through creating an anonymous Amazon Q Business application using both the AWS Management Console and AWS Command Line Interface (AWS CLI). Our example demonstrates a practical scenario: helping website visitors find information on public-facing documentation websites.

We demonstrate how to test the implementation with sample queries through the built-in web experience URL. The resulting application can be customized and embedded directly into your websites (using the API or the iframe method), providing immediate value for your users.

Prerequisites

To follow along with this post, you will need the following:

• An AWS account.
• At least one Amazon Q Business Pro user that has admin permissions to set up and configure Amazon Q Business. For pricing information, see Amazon Q Business pricing.
• AWS Identity and Access Management (IAM) permissions to create and manage IAM roles and policies.
• Public content to index (documents, FAQs, knowledge base articles) that can be shared with unauthenticated users.
• A supported data source to connect, such as an Amazon Simple Storage Service (Amazon S3) bucket containing your public documents.
• The AWS CLI configured with appropriate permissions (if following the AWS CLI method).

Create an anonymous Amazon Q Business application using the console

In this section, we walk through the steps to implement the solution using the console.

Create an IAM role for the web experience

Before creating your Amazon Q Business application, you will need to set up an IAM role with the appropriate permissions:

1. On the IAM console, choose Roles in the navigation pane and choose Create role.
2. Choose AWS service as the trusted entity
3. Select Amazon Q Business from the service list.
4. Choose Next: Permissions.
5. Create a custom policy or attach the necessary read-only policies, and add permissions for anonymous access.

We strongly recommend that you use a restricted policy for the role, like the one shown in the following screenshot, which will be used to create the web experience for anonymous access application environments.

An example of a restricted role policy for calling the Chat API for anonymous access application environments would be arn:aws:qbusiness:<your-region>:<your-aws-account-id>:application/<your-application-id>.

6. Create an IAM role with a trust policy that allows the Amazon Q Business service principal to assume the role using AWS Security Token Service (AWS STS), specifically scoped to your application’s Amazon Resource Name (ARN) in the designated AWS Region.
   

Create an Amazon Q Business application

Now you’re ready to create your Amazon Q Business application:

1. On the Amazon Q Business console, choose Create application.
2. For Application name, enter a name (for example, SupportDocs-Assistant).
3. For User access, select Anonymous access for this application environment.
4. Select Web experience to create a managed web experience to access the Amazon Q Business application.
   

You will see a notice about consumption-based billing for anonymous Amazon Q Business applications. For more details on pricing, refer to Amazon Q Business pricing.

5. Leave the default service role option unless you have specific requirements.
6. For Encryption, use the default AWS managed key unless you need custom encryption.
7. For Web experience settings, you can use an existing IAM role from your account or authorize Amazon Q Business to generate a new role with appropriate permissions. For this post, we select Use an existing service role and choose the IAM role created earlier (QBusinessAnonymousWebRole).
8. Optionally, customize the web experience title and welcome message.
   
9. Review all your configuration options and choose Create to create the application.

You should see a confirmation that your anonymous access application has been created successfully.

You will find the necessary parameters and details of your Amazon Q Business application on the landing page displayed after successful creation like the following screenshot, which provides comprehensive information about your newly created Amazon Q Business application.

Add data sources

After you create your application, you need to add an index and data sources. To learn more, refer to Index. You will see a pop-up like the following indicating that anonymous access is enabled.

Complete the following steps:

1. From your application dashboard, choose Add index.
2. Name your index (for example, Supportdocs-External) and keep the default settings.
3. Choose Add an index.
   
4. After you create the index, you can add data sources to it.

For our example, we use the Amazon Q Business public documentation as our data source by adding the URL https://docs.aws.amazon.com/amazonq/latest/qbusiness-ug/what-is.html. The Web Crawler will automatically index the content from this documentation page, making it searchable through your anonymous Amazon Q Business application.

For more information about Web Crawler configuration options and best practices, refer to Connecting Web Crawler to Amazon Q Business.

5. From your index dashboard, choose Add data source.
6. Enter a name for your data source and optional description.
7. For Source, select Source URLs and enter the URLs of the public websites you want to index.
   
8. For Authentication, select No authentication.
   
9. Configure the sync run schedule and field mappings.
10. Choose Add data source.
    

Alternatively, you can add Amazon S3 as the data source:

1. From your index dashboard, choose Add data source.
2. Select Amazon S3 as the source.
3. Configure your S3 bucket settings (make sure the bucket has public access).
4. Complete the data source creation process.

You must only ingest publicly available data sources without access control lists (ACLs).

Generate an anonymous web experience URL

After your data sources are set up, complete the following steps:

1. From your application dashboard, choose your application.
2. In the Web experience settings section, choose Share one-time URL.
   

The anonymous web experience URL can be shared as a single-use link that must be redeemed and accessed within 5 minutes. After it’s activated, the Amazon Q Business session remains active with a configurable timeout ranging from 15–60 minutes. This enables you to experience the web interface and test its functionality before deploying or offering the anonymous application to guest users.

Test your anonymous Amazon Q Business application

To test the application, choose Preview web experience.

The following screenshot shows the welcome page for your anonymous Amazon Q Business application’s web interface. Let’s begin asking Amazon Q Business some questions about the Amazon Q index.

In the first query, we ask “What is Q index? How is it useful for ISV’s?” The following screenshot shows the response.

In the following query, we ask “How can Q index enrich generative AI experiences for ISVs?”

In our next query, we ask “How is Q index priced?”

Having successfully tested our anonymous Amazon Q Business application through the console, we will now explore how to create an equivalent application using the AWS CLI.

Create your anonymous application using the AWS CLI

Make sure that your AWS CLI is configured with permissions to create Amazon Q Business resources and IAM roles.

Create an IAM role for Amazon Q Business

First, create an IAM role that Amazon Q Business can assume to access necessary resources:

# Create trust policy document
cat > trust-policy.json << 'EOF'
{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Principal": {
        "Service": "qbusiness.amazonaws.com"
      },
      "Action": "sts:AssumeRole"
    }
  ]
}
EOF

# Create IAM role
aws iam create-role 
  --role-name QBusinessAnonymousAppRole 
  --assume-role-policy-document file://trust-policy.json

# Attach necessary permissions
aws iam attach-role-policy 
  --role-name QBusinessAnonymousAppRole

Create an anonymous Amazon Q Business application

Use the following code to create your application:

#bash
aws qbusiness create-application 
--display-name "PublicKnowledgeBase" 
--identity-type ANONYMOUS 
--role-arn "arn:aws:iam:: <ACCOUNT_ID>:role/QBusinessAnonymousAppRole" 
--description "This is the QBiz application for anonymous use-case"

Save the applicationId from the response:

#json

{
  "applicationId": "your-application-id",
  "applicationArn": "arn:aws:qbusiness:region:account-id:application/your-application-id"
}

Create a restrictive policy for anonymous access

We strongly recommend using the following restricted policy for the role that will be used to call the chat APIs for anonymous access application environments. This policy limits actions to only the necessary APIs and restricts access to only your specific application.

Create the IAM role with the following policy:

# Create restrictive policy document
cat > anonymous-access-policy.json << 'EOF'
{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Sid": "QBusinessConversationPermission",
      "Effect": "Allow",
      "Action": [
        "qbusiness:Chat",
        "qbusiness:ChatSync",
        "qbusiness:PutFeedback"
      ],
      "Resource": "arn:aws:qbusiness:<REGION>:<ACCOUNT_ID>:application/<APPLICATION_ID>"
    }
  ]
}
EOF

# Attach the policy to the role
aws iam put-role-policy 
  --role-name QBusinessAnonymousAppRole 
  --policy-name QBusinessAnonymousAccessPolicy 
  --policy-document file://anonymous-access-policy.json

Create an index

Create an index for your content, then upload documents using the BatchPutDocument API. For step-by-step guidance, see Select Retriever.

Test your anonymous Amazon Q Business application

To demonstrate the chat functionality using the AWS CLI, we uploaded Amazon Q Business documentation in PDF format to our index and tested the application using the following sample queries.

The following is an example chat interaction using the IAM role credentials. We first ask “What is Amazon Q index?”

#1)
#bash
aws qbusiness chat-sync 
  --application-id <APPLICATION_ID> 
  --user-message "What is Amazon Q index?"

The following screenshot shows part of the output from the chat-sync API when executed with our anonymous Amazon Q Business application ID, as shown in the previous command.

Next, we ask “How can Q index enrich generative AI experiences for ISV’s?”

2)
#bash
aws qbusiness chat-sync 
  --application-id <APPLICATION_ID> 
  --user-message "How can Q index enrich generative AI experiences for ISV's?"

The following screenshot shows part of the output from the chat-sync API when executed with our anonymous Amazon Q Business application ID.

Create a web experience for the anonymous web application

Use the following code to create the web experience:

#bash
aws qbusiness create-web-experience 
  --application-id <APPLICATION_ID> 
  --display-name "PublicKnowledgeBaseExperience" 
  --role-arn "arn:aws:iam::<ACCOUNT_ID>:role/QBusinessAnonymousAppRole" 
  --description "Web interface for my anonymous Q Business application"

To generate an anonymous URL, use the following code:

#bash
aws qbusiness create-anonymous-web-experience-url 
  --application-id <APPLICATION_ID> 
  --web-experience-id <WEB_EXPERIENCE_ID>

You can use the web experience URL generated by the preceding command and embed it into your web applications using an iframe.

Considerations

Consider the following when using anonymous access in Amazon Q Business:

• The following are the only chat APIs that support anonymous access application environments:
  • Chat
  • ChatSync
  • PutFeedback
• You should only ingest publicly available data sources without ACLs. Examples of public data sources include:
  • Data from the Amazon Q Business Web Crawler
  • Amazon S3 data without ACLs
• Amazon Q Business applications with anonymous access are billed on a consumption-based pricing model.
• Chat history is not available for anonymous application environments.
• Anonymous users and authenticated users are not supported on the same application environments.
• Plugins are not supported for anonymous application environments.
• Amazon QuickSight integration is not supported for anonymous application

Environments.

• Amazon Q Apps are not supported for anonymous application environments.
• Attachments are not supported for anonymous application environments.
• Admin controls and guardrails are read-only for anonymous application environments, except for blocked words.
• Topic rules using users and groups are not supported for anonymous application

The remaining Amazon Q Business functionality and features remain unchanged.

Clean up

When you are done with the solution, clean up the resources you created.

Conclusion

In this post, we introduced Amazon Q Business anonymous user access mode and demonstrated how to create, configure, and test an anonymous Amazon Q Business application using both the console and AWS CLI. This exciting feature extends enterprise-grade Amazon Q Business generative AI capabilities to your anonymous audiences without requiring authentication, opening up new possibilities for enhancing customer experiences on public websites, documentation portals, and self-service knowledge bases. This feature is available through a consumption pricing model that charges based on actual Chat and Chatsync API usage and index storage costs still applicable.

By following the implementation steps outlined in this post, you can quickly set up an Amazon Q Business application tailored for your external users, secured with appropriate IAM policies, and ready to embed in your end-user-facing applications.

To learn more about this anonymous access feature, see the Amazon Q Business User Guide. For detailed guidance on embedding Amazon Q Business in your web applications, see Add a generative AI experience to your website or web application with Amazon Q embedded. If you’re interested in building completely custom UI experiences with the Amazon Q Business API, check out Customizing an Amazon Q Business web experience.

About the authors

Vishnu Elangovan is a Worldwide Generative AI Solution Architect with over seven years of experience in Applied AI/ML. He holds a master’s degree in Data Science and specializes in building scalable artificial intelligence solutions. He loves building and tinkering with scalable AI/ML solutions and considers himself a lifelong learner. Outside his professional pursuits, he enjoys traveling, participating in sports, and exploring new problems to solve.

Jean-Pierre Dodel is a Principal Product Manager for Amazon Q Business, responsible for delivering key strategic product capabilities including structured data support in Q Business, RAG. and overall product accuracy optimizations. He brings extensive AI/ML and Enterprise search experience to the team with over 7 years of product leadership at AWS.

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With the advent of generative AI solutions, a paradigm shift is underway across industries, driven by organizations embracing foundation models (FMs) to unlock unprecedented opportunities. Amazon Bedrock has emerged as the preferred choice for numerous customers seeking to innovate and launch generative AI applications, leading to an exponential surge in demand for model inference capabilities. Amazon Bedrock customers aim to scale their worldwide applications to accommodate a variety of use cases. One such customer is FloQast.

Since its founding in 2013, FloQast has had the privilege of working with over 2,800 organizations across various industries and regions, helping them streamline their accounting operations. From automated reconciliations to tools that manage the entire close process, FloQast has seen firsthand how organizations, big and small, struggle to keep pace with their accounting needs as they scale. FloQast’s software (created by accountants, for accountants) brings AI and automation innovation into everyday accounting workflows. You can reconcile bank statements against internal ledgers, get real-time visibility into financial operations, and much more.

In this post, we share how FloQast built an AI-powered accounting transaction solution using Anthropic’s Claude 3 on Amazon Bedrock.

Accounting operations: Complexity amplified at scale

At the heart of every successful organization—whether small startups or large corporations—lies a well-oiled financial and accounting operation. Accounting is more than just a back-office function; it’s the backbone of every business. From processing payroll to generating financial statements, accounting is a ubiquitous force that touches every facet of business operations.

Consider this: when you sign in to a software system, a log is recorded to make sure there’s an accurate record of activity—essential for accountability and security. Similarly, when an incident occurs in IT, the responding team must provide a precise, documented history for future reference and troubleshooting. The same principle applies to accounting: when a financial event takes place, whether it’s receiving a bill from a vendor or signing a contract with a customer, it must be logged. These logs, known in accounting as journal entries, provide a clear financial record.

Now imagine this process scaled across hundreds, or even thousands, of transactions happening simultaneously in a large organization. The complexity of accounting increases exponentially with growth and diversification. As businesses expand, they encounter a vast array of transactions that require meticulous documentation, categorization, and reconciliation. At scale, upholding the accuracy of each financial event and maintaining compliance becomes a monumental challenge. With advancement in AI technology, the time is right to address such complexities with large language models (LLMs).

Amazon Bedrock has helped democratize access to LLMs, which have been challenging to host and manage. Amazon Bedrock offers a choice of industry-leading FMs along with a broad set of capabilities to build generative AI applications, simplifying development with security, privacy, and responsible AI. Because Amazon Bedrock is serverless, you don’t have to manage infrastructure to securely integrate and deploy generative AI capabilities into your application, handle spiky traffic patterns, and enable new features like cross-Region inference, which helps provide scalability and reliability across AWS Regions.

In this post, we highlight how the AI-powered accounting transformation platform uses Amazon Bedrock. FloQast addresses the most complex and custom aspects of financial processes (the final 20%)—those intricate, bespoke aspects of accounting that are highly specific to each organization and often require manual intervention. FloQast’s AI-powered solution uses advanced machine learning (ML) and natural language commands, enabling accounting teams to automate reconciliation with high accuracy and minimal technical setup.

FloQast AI Transaction Matching

Seamlessly integrated with the existing FloQast suite, the AI Transaction Matching product streamlines and automates your matching and reconciliation processes, delivering unparalleled precision and efficiency.

It offers the following key features:

• AI-driven matching – You can automatically match transactions across multiple data sources with high accuracy
• Flexible rule creation – You can use natural language to create custom matching rules tailored to your unique processes
• Exception handling – You can quickly identify and manage unmatched transactions or discrepancies
• Audit trail – You can maintain a comprehensive audit trail of matching activities for compliance and transparency
• High-volume processing – You can efficiently handle large volumes of transactions, suitable for businesses of all sizes
• Multi-source integration – You can seamlessly integrate and match transactions from various financial systems and data sources

Let’s review how it works:

1. Transaction data is gathered from bank statements and enterprise resource planning (ERP) systems.
2. An accountant will select specific transactions in both systems and choose Generate AI Rule.

The following screenshot shows the general ledger system on the left and the bank statement on the right.

3. Based on the selected transactions, text is generated (see the following screenshot).

4. At this point, the accountant has the option to either accept the generated text or edit the text.
5. The accountant chooses Save and apply to generate a rule in coded format that is further used to find additional matches, helping the accountant automate transaction reconciliation.

FloQast AI Transaction Matching offers the following benefits:

• Unified environment – It seamlessly integrates with your existing FloQast products for a single source of truth
• AI-powered automation – It uses advanced ML to handle complex matching scenarios
• User-friendly interface – It’s designed by accountants for how accountants work, providing ease of use and adoption
• Real-time insights – You can gain immediate visibility into your transaction data across systems
• Scalability – It can adapt as your transaction volumes grow and business evolves

FloQast AI Annotations

FloQast’s new AI Annotations feature empowers teams to seamlessly and automatically annotate and review sample documents, streamlining compliance and audit processes through advanced automation and ML.

It offers the following key features:

• Automated document annotation – You can upload sample documents to automatically annotate key data points with attributes specified in your testing criteria, saving time on manual reviews
• AI-powered analysis – You can use advanced AI and natural language models to analyze document text, highlighting relevant information according to predefined controls and testing attributes
• Bulk annotation for efficiency – You can select multiple documents or testing controls for bulk annotation, reducing time spent on repetitive document processing
• Structured storage and audit trail – You can maintain a structured record of each annotated document, capturing all extracted data, annotation responses, and status updates for streamlined compliance and audit trails
• Intuitive error handling – Smart checks identify and notify users of processing errors, making sure each annotation is complete and accurate.

The following diagram illustrates the architecture using AWS services.

The workflow starts with user authentication and authorization (steps 1-3). After those steps are complete, the workflow consists of the following steps:

4. Users upload supporting documents that provide audit evidence into a secure Amazon Simple Storage Service (Amazon S3) bucket.
5. The input documents are encrypted by Amazon S3 when consumed by Amazon Textract.
6. Amazon Textract (encrypts data in transit and at rest) extracts the data from the documents.
7. When complete, raw data is stored into an encrypted S3 bucket.
8. Data sanitization workflow kicks off using AWS Step Functions consisting of AWS Lambda functions.
9. Sanitized extracted data is written into an encrypted MongoDB.
10. Amazon Textract is polled to update the job status and written into Mongo DB.
11. The user starts the annotation process.
12. Application logic consumes data from Mongo DB and provides it to Anthropic’s Claude 3.5 Sonnet on Amazon Bedrock.
13. The LLM runs the audit rules (shown in the following screenshot) against the extracted data and generates an annotation for each audit rule, including pass/fail details of the audit rule.
14. Annotation results are filtered using Amazon Bedrock Guardrails to enhance content safety and privacy in generative AI applications.

FloQast AI Annotations offers the following benefits:

• Seamless integration with FloQast – This feature is integrated into the FloQast platform, providing access to annotation tools alongside your existing compliance and financial workflows
• Enhanced efficiency with AI-driven workflows – FloQast’s annotation feature uses AI to reduce manual workload, helping teams focus on high-value tasks rather than repetitive document review
• Scalable solution for high-volume document processing – Designed to handle substantial document volumes, FloQast AI Annotations adapts to the demands of growing teams and complex audit requirements
• Real-time document processing insights – You can stay informed with live tracking of each annotation job, with built-in monitoring for smooth and efficient workflows

FloQast’s AI technology choices

FloQast selected Amazon Bedrock because of its unmatched versatility, feature sets, and the robust suite of scalable AI models from top-tier providers like Anthropic. Anthropic’s Claude 3.5 Sonnet provides the advanced reasoning and contextual understanding necessary for handling complex financial workflows. However, a key feature of Amazon Bedrock—Amazon Bedrock Agents—is a game changer for FloQast. Amazon Bedrock Agents enables generative AI applications to run multi-step tasks across company systems and data sources. To learn more, see How Amazon Bedrock Agents works.

Amazon Bedrock Agents provides an intelligent orchestration layer, allowing FloQast to automate accounting workflows efficiently. It has added significant value in the following areas:

• Instruction handling and task automation – Amazon Bedrock Agents enables FloQast to submit natural language instructions that the AI interprets and executes autonomously.
• Session and memory management session – Attributes and promptSessionAttributes are passed between sessions related to a single workflow, but most user requests can be singular to a session.
• Code generation that demonstrates business understanding – Amazon Bedrock Agents offers valuable features through its secure code interpretation capabilities and flexible configuration options. Amazon Bedrock agents can be tailored to the correct persona and business context, while operating within a protected test environment. This allows accountants to submit natural language instructions and input data, which is then processed in a controlled manner that aligns with security best practices. When FloQast integrates with Amazon Bedrock Agents, accountants can submit custom requests, and the agent can generate and test code within an isolated secure environment, with appropriate technical oversight and guardrails in place. The combination of Amazon Bedrock Agents’ secure code interpretation features and FloQast’s deep knowledge of accounting practices enables financial teams to operate efficiently while maintaining proper controls.
• Data integration and output handling – By using Amazon Bedrock Agents, information is passed from upstream integrated financial systems, allowing FloQast to automate data retrieval and transformation tasks.
• Multi-step task orchestration – Amazon Bedrock agents are designed to handle multi-step tasks by orchestrating complex workflows. For example, after FloQast retrieves data from a financial system, that data is passed to the agent, which runs the necessary calculations, generates the output code, and presents the results for user approval—all in one automated process. This orchestration is especially useful in accounting, where multiple steps must be completed in the correct sequence to maintain compliance and accuracy.

The flexibility of Amazon Bedrock Agents to manage these tasks and integrate them seamlessly into existing workflows enables FloQast to achieve scale, reduce complexity, and implement automation required to cater to the evolving needs of FloQast’s customers.

Anthropic’s Claude 3.5 Sonnet on Amazon Bedrock provides the best results in FloQast’s evaluation of other models for the use case. FloQast doesn’t need to fine-tune the model as a model consumer, so they use Retrieval Augmented Generation (RAG) with few-shot classification on data collected on the user’s behalf, removing the overhead of fine-tuning an LLM. For this use case, this design mechanism produces a higher level of accuracy, a better security model that is understood by FloQast’s customers, and ease of use as a developer.

Conclusion

FloQast’s AI-powered accounting transformation solution has had a substantial impact on its users. By automating routine, time-consuming accounting processes, the solution has saved accounting teams countless hours, enabling them to shift away from manual spreadsheet work and focus on higher-value activities, such as reviewing financial outcomes, assessing business health, and making data-driven decisions. This solution has removed the tedium of data reconciliation, delivering measurable improvements, including a 38% reduction in reconciliation time, a 23% decrease in audit process duration and discrepancies, and a 44% improvement in workload management.

Learn more about the FloQast platform at FloQast.com. Contact evelyn.cantu@floqast.com for more information about the FloQast and AWS partnership.

About the authors

Kartik Bhatnagar is a data security-focused Solutions Architect at AWS, based in San Francisco, CA. He has experience working with startups and enterprises across the tech, fintech, healthcare, and media & entertainment industries, in roles including DevOps Engineer and Systems Architect. In his current role, he partners with AWS customers to design and implement scalable, secure, and cost-effective solutions on the AWS platform. Outside of work, he enjoys playing cricket and tennis, food hopping, and hiking.

Aidan Anderson is a dynamic technology leader with over a decade of experience in software engineering, security, and artificial intelligence. Currently serving as the Director of AI Engineering at FloQast, he is at the forefront of integrating AI and automation into accounting workflows, enhancing operational efficiency and accuracy for finance teams. Aidan’s portfolio spans leadership across security, product development, and platform engineering – where he’s consistently driven innovation, built high-performing teams, and delivered impactful solutions in fast-paced startup environments.

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As generative AI revolutionizes industries, organizations are eager to harness its potential. However, the journey from production-ready solutions to full-scale implementation can present distinct operational and technical considerations. This post explores key insights and lessons learned from AWS customers in Europe, Middle East, and Africa (EMEA) who have successfully navigated this transition, providing a roadmap for others looking to follow suit.

Building a solid business case: Operational excellence drives customer experience

The foundation of successful generative AI implementations are business cases with clear value propositions that fit with organizational goals, for example, improving efficiency, cost savings, or revenue growth. Typical examples include enhancing customer experience, optimizing operations, maintaining compliance with legal standards, improving level of services, or increasing employee productivity.

Companies in EMEA have used AWS services to transform their operations and improve customer experience using generative AI, with their stories illustrating how a strong business case can lead to tangible results across various industry verticals.

Il Sole 24 Ore, Italy’s leading multimedia publishing group, partnered with AWS Professional Services to boost the efficiency of a historic service, L’Esperto Risponde, where users can ask fiscal questions and receive responses from a team of experts. Il Sole 24 Ore leveraged its vast internal knowledge with a Retrieval Augmented Generation (RAG) solution powered by AWS. This solution maintained over 90% accuracy in responses and reduced the time spent by experts in searching and processing information, empowering them to focus on more strategic tasks. Additionally, the company is continuously incorporating end-user feedback to keep the service tailored to customer needs. For more information, you can watch the AWS Summit Milan 2024 presentation.

Booking.com, one of the world’s leading digital travel services, is using AWS to power emerging generative AI technology at scale, creating personalized customer experiences while achieving greater scalability and efficiency in its operations. Booking.com uses Amazon SageMaker AI to provide highly personalized customer accommodation recommendations.

“One of the things we really like about AWS’s approach to generative AI is choice. We love open source, and we feel it will play an important role in the evolution of generative AI,”

– Rob Francis, Chief Technology Officer of Booking.com.

With AWS support, Booking.com is enhancing its generative AI capabilities and positioning itself for future growth in the travel and hospitality industry. For more details, you can watch Booking.com’s keynote at AWS re:Invent 2023, their presentation on generative AI from idea to production on AWS at AWS London Summit 2024, and read the case study on how Booking.com helps customers experience a new world of travel using AWS and generative AI.

ENGIE is a global power and utilities company, with 25 business units operating worldwide. ENGIE’s One Data team partnered with AWS Professional Services to develop an AI-powered chatbot that enables natural language conversation search within ENGIE’s Common Data Hub data lake, over 3 petabytes of data. The solution complements traditional keyword-based search by allowing users to discover datasets through simple conversational queries, making it easier to find relevant data among tens of thousands of datasets. This dual approach to data discovery has accelerated the development of data-driven products and enhanced data assets sharing across the organization.

These examples demonstrate how companies across various sectors have successfully used AWS generative AI capabilities to address specific business challenges.

Getting ahead of implementation challenges

Though essential, a solid business case is only the first step. As organizations move their generative AI initiatives forward, they often encounter new challenges related to making the solution scalable, reliable, and compliant. Let’s explore what it takes to successfully advance generative AI projects from the preproduction phase, making sure that the original value of the business case is then fully realized in real-world application.

Achieving scale, reliability, and compliance

Factors to consider in transitioning to full-scale production include scalability, data governance, privacy, consistent and responsible AI behaviors, security, integration with existing systems, monitoring, end-user feedback collection, and business impact measurement. As organizations in EMEA have discovered, success in this transition requires a holistic approach that goes beyond mere technological considerations. With a multitude of customer learnings, paired with AWS expertise, we can identify key strategies for implementation.

Production-ready infrastructure, applications, and processes in the cloud

With the increase in scope, number, and complexity of generative AI applications, organizations have an increased need to reduce undifferentiated effort and set a high-quality bar for production-ready applications. Standard development best practices and effective cloud operating models, like AWS Well-Architected and the AWS Cloud Adoption Framework for Artificial Intelligence, Machine Learning, and Generative AI, are key to enabling teams to spend most of their time on tasks with high business value, rather than on recurrent, manual operations. Such an approach should include established industry standards such as infrastructure as code (IaC), continuous integration and continuous delivery (CI/CD), monitoring and observability, logging and auditing, and solutions for scalability and high availability.

For instance, Iveco Group, a global automotive leader active in the Commercial and Specialty Vehicles, Powertrain, adopted a structured cloud-operating model, leveraging IaC via Terraform for consistent and repeatable deployments across environments. A DevOps environment, via CI/CD pipelines, allows for frequent updates and testing of generative AI models and applications, allowing the developers to focus on improving and expanding the solutions rather then spending time on manual operations. This also helps make sure that generative AI solutions are optimized for performance, security, and cost-efficiency. This integrated approach not only accelerates the path from pre-production to full-scale implementation, but also enables them to adapt quickly to new generative AI advancements, manage complex dependencies, and scale resources as needed, ultimately driving innovation and competitive advantage in the rapidly evolving field of generative AI. See the re:Invent 2024 session for more information.

Accor Group, a major hospitality company that developed a generative AI-powered booking application, showcased how, even when working with new technologies like generative AI, fundamental software development principles remain a must-have. They implemented a three-layered comprehensive testing strategy. First, unit tests verify that the prompts consistently generate acceptable responses from the chatbot, even upon prompt modifications. Second, integration tests verify the end-to-end flow of the REST API and the chatbot’s interaction with the large language model (LLM). The final step is functional testing with predefined scenarios for manual testing and validation. They also implemented feedback systems, essential for the improvement flywheel of customer-facing applications, in the form of in-app surveys, instant feedback options (thumbs-up or thumbs-down), and a dedicated feedback portal for detailed user input. Finally, to measure the effectiveness of the solution and its business impact, they established a system to track room bookings made through the generative AI application.

Danske Bank, a leading Nordic bank, transitioned from a container-based on-premises setup to Amazon Elastic Container Service (Amazon ECS) with AWS Fargate. This allowed them to quickly move their API-based backend services to a cloud-native environment. This decoupled architecture, designed to be provider-agnostic, set them up for flexibility in leveraging different cloud-based generative AI tools and services as needed. The integration with Amazon Bedrock was seamless and impactful, as it provided faster access to multiple foundational models from more providers. This allowed the customer to rapidly experiment, iterate, and evaluate different models for their specific use cases. This case demonstrates how the combination of generative AI services and a cloud-native, API-driven architecture allowed this customer to iterate faster, and keep the focus on business value rather than integration of technologies.

The Schaeffler Group has been driving forward groundbreaking inventions and developments in the field of motion technology for over 75 years. The company developed a comprehensive generative AI framework, which establishes enterprise-grade governance and security guardrails for generative AI use case roll-out at scale with infrastructure blueprints. A generative AI inference gateway is integrated within the solution, offering centralized access to numerous foundational models while tracking usage and costs. Going forward, Schaeffler envisions to further integrate these capabilities into their wider generative AI and data landscape, including more fine-grained access controls to data assets and the adoption of generative AI agents.

These examples highlight a key theme for organizations across industries: Success in generative AI goes beyond developing standalone applications. A thorough cloud-based operating model is crucial for enterprises looking to keep pace with the rapidly evolving technology, with minimal operational overhead.

Security, compliance, and responsible AI

As an organization’s generative AI applications expand to handle increasingly sensitive data, security, compliance, and governance must be prioritized accordingly. This includes implementing authentication and access control, encrypting data at rest and in transit, monitoring and auditing of system access and usage, maintaining compliance with regulations (such as GDPR and the recent EU AI Act), as well as establishing clear policies for data handling and model usage.

Here are some examples of customers who have successfully navigated these critical requirements.

Il Sole24 Ore implemented a code of self-discipline for ethical AI application. It prescribes retention of high-quality standards and the centrality of trustworthy data. The principles include regulatory compliance, maintaining data provenance and reliability, incorporating human oversight via human-in-the-loop, inclusivity and diversity in data usage and algorithm adoption, responsibility and accountability, and digital education and communicative transparency. By adhering to these principles, Il Sole 24 Ore Group demonstrates its commitment to leveraging innovative technologies like generative AI in a safe and responsible manner, particularly in sensitive areas such as providing expert legal and tax advice. This approach allows them to harness the benefits of AI while mitigating potential risks and maintaining the trust of their users.

For Accor Group, the implementation of their next-generation booking application required direct customer interaction, emphasizing the critical need for responsible AI practices. To make sure the chatbot would deliver effective customer service while operating within strict ethical boundaries, they established specific safeguards to minimize misuse:

• Blocking responses to discriminatory queries
• Withholding responses to illegal activities
• Implementing guardrails to keep conversations within appropriate business context
• Installing protections against role-switching or tone-changing attempts during conversations
• Implementing robust technical defenses against prompt injections

Conclusion

The transition from preproduction to full-scale implementation for generative AI applications presents new challenges and opportunities. It requires identifying a solid business case, maintaining high standards for infrastructure and processes, strategic thinking in choosing an efficient cloud operating model, robust data governance, security, compliance, ethical AI practices, and more.

Organizations across EMEA have demonstrated how using AWS services can help overcome hurdles and accelerate the advantages of generative AI by embracing a holistic approach. By learning from these use cases, more enterprises can achieve successful deployments of generative AI solutions, and benefit from this transformative technology in a reliable, productive, and responsible manner.

Explore more generative AI use cases and customer succcess stories and discover how to accelerate your AI adoption on the cloud with specialized training and the support of AWS Professional Services and the Generative AI Innovation Center.

About the Authors

Dr. Giorgio Pessot is a Machine Learning Engineer at Amazon Web Services Professional Services. With a background in computational physics, he specializes in architecting enterprise-grade AI systems at the confluence of mathematical theory, DevOps, and cloud technologies, where technology and organizational processes converge to achieve business objectives. When he’s not whipping up cloud solutions, you’ll find Giorgio engineering culinary creations in his kitchen.

Daniel Zagyva is a Senior ML Engineer at AWS Professional Services. He specializes in developing scalable, production-grade machine learning solutions for AWS customers. His experience extends across different areas, including natural language processing, generative AI and machine learning operations.

Nicolò Cosimo Albanese is a Data Scientist and Machine Learning Engineer at Amazon Web Services Professional Services. With a Master of Science in Engineering and postgraduate degrees in Machine Learning and Biostatistics, he specializes in developing AI/ML solutions that drive business value for enterprise customers. His expertise lies at the intersection of statistical modeling, cloud technologies, and scalable machine learning systems.

Subhro Bose is a Data Architect in Emergent Technologies and Intelligence Platform in Amazon. He loves working on ways for emergent technologies such as AI/ML, big data, quantum, and more to help businesses across different industry verticals succeed within their innovation journey.

Diar Sabri is a Machine Learning Engineer at AWS who helps organizations transform their business through innovative AI solutions. With experience across multiple industries, he excels at bridging the gap between strategic vision and practical technology implementation, enabling customers to achieve meaningful business outcomes.

Aamna Najmi is a GenAI and Data Specialist at AWS. She assists customers across industries and regions in operationalizing and governing their generative AI systems at scale, ensuring they meet the highest standards of performance, safety, and ethical considerations, bringing a unique perspective of modern data strategies to complement the field of AI. In her spare time, she pursues her passion of experimenting with food and discovering new places.

Anwar Rizal is a Senior Machine Learning consultant for AWS Professional Services based in Paris. He works with AWS customers to develop data and AI solutions to sustainably grow their business.

Amer Elhabbash is a Senior Data & AI Delivery Consultant with AWS Professional Services. With over 25 years of international experience in IT spanning multiple fields and domains; Telecommunication, Software Engineering , Database, Data Analytics and AI. He helps AWS’ customers migrating their legacy data systems and building innovative cloud-native data-driven solutions.

Hassen Riahi is a Delivery Practice Manager Data & AI at AWS Professional Services. He holds a PhD in Mathematics & Computer Science on large-scale data management. He collaborates with AWS customers to build data-driven solutions.

Dr. Marco Guerriero leads Data and GenAI at AWS Professional Services for France and Europe South, holding a Ph.D. in Electrical and Computer Engineering from the University of Connecticut. His expertise spans machine learning, statistical inference, and mathematical optimization, with experience at organizations like NATO, GE, and ABB across defense, manufacturing, energy, and industrial automation sectors. With over 60 publications and five US patents to his name, Dr. Guerriero focuses on leveraging emerging technologies like GenAI and Quantum computing to drive business innovation across industries.

Sri Elaprolu is Director of the AWS Generative AI Innovation Center, where he leads a global team implementing cutting-edge AI solutions for enterprise and government organizations. During his 12-year tenure at AWS, he has led ML science teams partnering with organizations like the NFL, Cerner, and NASA. Prior to AWS, he spent 14 years at Northrop Grumman in product development and software engineering leadership roles. Sri holds a Master’s in Engineering Science and an MBA.

Dragica Boca is Managing Director of Professional Services EMEA at Amazon Web Services (AWS), leading enterprise cloud migration and generative AI transformation initiatives. With 30 years of technology consulting experience across Microsoft and IBM Global Business Services, she specializes in implementing production-ready AI solutions for Public Sector and Financial Services organizations. Dragica currently oversees large-scale GenAI implementations across EMEA, helping enterprises navigate the complexities of responsible AI deployment, scalable architecture, and sustainable adoption patterns.

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