AWS Machine Learning Blog

Accenture creates a Knowledge Assist solution using generative AI services on AWS

This post is co-written with Ilan Geller and Shuyu Yang from Accenture.

Enterprises today face major challenges when it comes to using their information and knowledge bases for both internal and external business operations. With constantly evolving operations, processes, policies, and compliance requirements, it can be extremely difficult for employees and customers to stay up to date. At the same time, the unstructured nature of much of this content makes it time consuming to find answers using traditional search.

Internally, employees can often spend countless hours hunting down information they need to do their jobs, leading to frustration and reduced productivity. And when they can’t find answers, they have to escalate issues or make decisions without complete context, which can create risk.

Externally, customers can also find it frustrating to locate the information they are seeking. Although enterprise knowledge bases have, over time, improved the customer experience, they can still be cumbersome and difficult to use. Whether seeking answers to a product-related question or needing information about operating hours and locations, a poor experience can lead to frustration, or worse, a customer defection.

In either case, as knowledge management becomes more complex, generative AI presents a game-changing opportunity for enterprises to connect people to the information they need to perform and innovate. With the right strategy, these intelligent solutions can transform how knowledge is captured, organized, and used across an organization.

To help tackle this challenge, Accenture collaborated with AWS to build an innovative generative AI solution called Knowledge Assist. By using AWS generative AI services, the team has developed a system that can ingest and comprehend massive amounts of unstructured enterprise content.

Rather than traditional keyword searches, users can now ask questions and extract precise answers in a straightforward, conversational interface. Generative AI understands context and relationships within the knowledge base to deliver personalized and accurate responses. As it fields more queries, the system continuously improves its language processing through machine learning (ML) algorithms.

Since launching this AI assistance framework, companies have seen dramatic improvements in employee knowledge retention and productivity. By providing quick and precise access to information and enabling employees to self-serve, this solution reduces training time for new hires by over 50% and cuts escalations by up to 40%.

With the power of generative AI, enterprises can transform how knowledge is captured, organized, and shared across the organization. By unlocking their existing knowledge bases, companies can boost employee productivity and customer satisfaction. As Accenture’s collaboration with AWS demonstrates, the future of enterprise knowledge management lies in AI-driven systems that evolve through interactions between humans and machines.

Accenture is working with AWS to help clients deploy Amazon Bedrock, utilize the most advanced foundational models such as Amazon Titan, and deploy industry-leading technologies such as Amazon SageMaker JumpStart and Amazon Inferentia alongside other AWS ML services.

This post provides an overview of an end-to-end generative AI solution developed by Accenture for a production use case using Amazon Bedrock and other AWS services.

Solution overview

A large public health sector client serves millions of citizens every day, and they demand easy access to up-to-date information in an ever-changing health landscape. Accenture has integrated this generative AI functionality into an existing FAQ bot, allowing the chatbot to provide answers to a broader array of user questions. Increasing the ability for citizens to access pertinent information in a self-service manner saves the department time and money, lessening the need for call center agent interaction. Key features of the solution include:

  • Hybrid intent approach – Uses generative and pre-trained intents
  • Multi-lingual support – Converses in English and Spanish
  • Conversational analysis – Reports on user needs, sentiment, and concerns
  • Natural conversations – Maintains context with human-like natural language processing (NLP)
  • Transparent citations – Guides users to the source information

Accenture’s generative AI solution provides the following advantages over existing or traditional chatbot frameworks:

  • Generates accurate, relevant, and natural-sounding responses to user queries quickly
  • Remembers the context and answers follow-up questions
  • Handles queries and generates responses in multiple languages (such as English and Spanish)
  • Continuously learns and improves responses based on user feedback
  • Is easily integrable with your existing web platform
  • Ingests a vast repository of enterprise knowledge base
  • Responds in a human-like manner
  • The evolution of the knowledge is continuously available with minimal to no effort
  • Uses a pay-as-you-use model with no upfront costs

The high-level workflow of this solution involves the following steps:

  1. Users create a simple integration with existing web platforms.​
  2. Data is ingested into the platform as a bulk upload on day 0 and then incremental uploads day 1+. ​
  3. User queries are processed in real time with the system scaling as required to meet user demand.
  4. Conversations are saved in application databases (Amazon Dynamo DB) to support multi-round conversations.​
  5. The Anthropic Claude foundation model is invoked via Amazon Bedrock, which is used to generate query responses based on the most relevant content.
  6. The Anthropic Claude foundation model is used to translate queries as well as responses from English to other desired languages to support multi-language conversations.
  7. The Amazon Titan foundation model is invoked via Amazon Bedrock to generate vector embeddings​.
  8. Content relevance is determined through similarity of raw content embeddings and the user query embedding by using Pinecone vector database embeddings.​
  9. The context along with the user’s question is appended to create a prompt, which is provided as input to the Anthropic Claude model. The generated response is provided back to the user via the web platform.

The following diagram illustrates the solution architecture.

The architecture flow can be understood in two parts:

In the following sections, we discuss different aspects of the solution and its development in more detail.

Model selection

The process for model selection included regress testing of various models available in Amazon Bedrock, which included AI21 Labs, Cohere, Anthropic, and Amazon foundation models. We checked for supported use cases, model attributes, maximum tokens, cost, accuracy, performance, and languages. Based on this, we selected Claude-2 as best suited for this use case.

Data source

We created an Amazon Kendra index and added a data source using web crawler connectors with a root web URL and directory depth of two levels. Several webpages were ingested into the Amazon Kendra index and used as the data source.

GenAI chatbot request and response process

Steps in this process consist of an end-to-end interaction with a request from Amazon Lex and a response from a large language model (LLM):

  1. The user submits the request to the conversational front-end application hosted in an Amazon Simple Storage Service (Amazon S3) bucket through Amazon Route 53 and Amazon CloudFront.
  2. Amazon Lex understands the intent and directs the request to the orchestrator hosted in an AWS Lambda function.
  3. The orchestrator Lambda function performs the following steps:
    1. The function interacts with the application database, which is hosted in a DynamoDB-managed database. The database stores the session ID and user ID for conversation history.
    2. Another request is sent to the Amazon Kendra index to get the top five relevant search results to build the relevant context. Using this context, modified prompt is constructed required for the LLM model.
    3. The connection is established between Amazon Bedrock and the orchestrator. A request is posted to the Amazon Bedrock Claude-2 model to get the response from the LLM model selected.
  4. The data is post-processed from the LLM response and a response is sent to the user.

Online reporting

The online reporting process consists of the following steps:

  1. End-users interact with the chatbot via a CloudFront CDN front-end layer.
  2. Each request/response interaction is facilitated by the AWS SDK and sends network traffic to Amazon Lex (the NLP component of the bot).
  3. Metadata about the request/response pairings are logged to Amazon CloudWatch.
  4. The CloudWatch log group is configured with a subscription filter that sends logs into Amazon OpenSearch Service.
  5. Once available in OpenSearch Service, logs can be used to generate reports and dashboards using Kibana.


In this post, we showcased how Accenture is using AWS generative AI services to implement an end-to-end approach towards digital transformation. We identified the gaps in traditional question answering platforms and augmented generative intelligence within its framework for faster response times and continuously improving the system while engaging with the users across the globe. Reach out to the Accenture Center of Excellence team to dive deeper into the solution and deploying this solution for your clients.

This Knowledge Assist platform can be applied to different industries, including but not limited to health sciences, financial services, manufacturing, and more. This platform provides natural, human-like responses to questions using knowledge that is secured. This platform enables efficiency, productivity, and more accurate actions for its users can take.

The joint effort builds on the 15-year strategic relationship between the companies and uses the same proven mechanisms and accelerators built by the Accenture AWS Business Group (AABG).

Connect with the AABG team at to drive business outcomes by transforming to an intelligent data enterprise on AWS.

For further information about generative AI on AWS using Amazon Bedrock or Amazon SageMaker, we recommend the following resources:

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About the Authors

Ilan Geller is a Managing Director at Accenture with a focus on Artificial Intelligence, helping clients scale Artificial Intelligence applications, and is the Global GenAI COE Partner Lead for AWS.

Shuyu Yang is Generative AI and Large Language Model Delivery Lead and also leads CoE (Center of Excellence) Accenture AI (AWS DevOps professional) teams.

Shikhar Kwatra is an AI/ML specialist solutions architect at Amazon Web Services, working with a leading Global System Integrator. He has earned the title of one of the Youngest Indian Master Inventors with over 500 patents in the AI/ML and IoT domains. Shikhar aids in architecting, building, and maintaining cost-efficient, scalable cloud environments for the organization, and supports the GSI partner in building strategic industry solutions on AWS.

Jay Pillai is a Principal Solution Architect at Amazon Web Services. In this role, he functions as the Global Generative AI Lead Architect and also the Lead Architect for Supply Chain Solutions with AABG. As an Information Technology Leader, Jay specializes in artificial intelligence, data integration, business intelligence, and user interface domains. He holds 23 years of extensive experience working with several clients across supply chain, legal technologies, real estate, financial services, insurance, payments, and market research business domains.

Karthik Sonti leads a global team of Solutions Architects focused on conceptualizing, building, and launching horizontal, functional, and vertical solutions with Accenture to help our joint customers transform their business in a differentiated manner on AWS.