Customer Stories / Life Sciences / United States

Alnylam Logo

Alnylam Transforms Product Complaint Management with Generative AI Using Amazon Bedrock

Learn how Alnylam Pharmaceuticals uses generative AI to enhance employee productivity and streamline product complaint management.

From 3 days to hours

decreased time to triage product complaints

From 15 minutes to 30 seconds

decrease in time for information searching


LLM accuracy

Maintained GxP

standards for handling patient data

Built proof-of-concept

capabilities that can be repurposed


Alnylam Pharmaceuticals (Alnylam) is a global pharmaceutical company known for its groundbreaking therapeutics based on RNA interference (RNAi), which is a way to silence disease-causing genes. The company drives innovation not only in its RNAi-based medicines but also in its use of generative artificial intelligence (AI) for operational efficiencies.

The company became an early adopter of Amazon Bedrock—the simplest way to build and scale generative AI applications with foundation models. Alnylam’s business and IT teams closely collaborated to enhance employee productivity and streamline business workflows with automation for improved efficiency and cost savings using generative AI.

Using Amazon Bedrock, Alnylam took on two novel generative AI use cases: a solution for managing and triaging product complaints, and an internal generative AI–powered digital assistant that is a force multiplier for helping employees efficiently access company information. Alnylam brought these use cases to life while meeting stringent data security and privacy standards and good practice guidelines (GxP) for the life sciences.

Factory for the production of medicines, glass bottles on the conveyor

Opportunity | Using Amazon Bedrock to Drive Innovation across Use Cases for Alnylam

Founded in 2002 and based in Cambridge, Massachusetts, Alnylam operates in compliance with regulatory mandates, with an obligation to triage and manage product complaints related to clinical and commercial drug products to regulatory bodies such as the Food and Drug Administration. Previously, teams relied on manual processes to understand, analyze, categorize, and investigate hundreds of drug product quality complaint reports. “We were taking 3–4 days to complete the steps for customers across different time zones, languages, and reporting channels,” says Kristen Dvorscak, Alnylam’s head of global quality systems. “With the new process powered by generative AI, we are reinventing existing workflows, making them near real time.”

In June 2023, Alnylam and teams from Amazon Web Services (AWS) began work on a solution that uses generative AI to significantly cut down reporting and initial evaluation and response time. “When we started on this journey, we knew we needed a solid partner like AWS with strong technical expertise,” says Murtaza Cherawala, head of data management and AI at Alnylam. “The AWS team describes technical complexities in a way that is understood by developers as well as business leaders.” Together, Alnylam and AWS developed a product complaints intake and triage prototype in 3 months, in alignment with company requirements for legal compliance, data privacy, security, validation, and quality.

In parallel, Alnylam and AWS teams also sought to enhance employee productivity with generative AI. They used Amazon Bedrock to create a Slack-integrated chatbot called AskALNY that is used by more than 2,000 employees and an additional 1,000 contractors to find information easily, summarize insights from complex documents, and generate content for a range of use cases. “Using AskALNY, our teams accurately find information hidden in different policy documents along with the source link,” says Cherawala. For example, teams use AskALNY to create job descriptions, write meeting agendas, summarize medical literature and other documents into plain language, search compliance and legal policies, access guidance around finance and procurement, identify guardrails around travel or timecard submissions, and even find facilities information.


Using generative AI on AWS, we are transforming the way we work across the enterprise and resetting our company’s mindset.”

Kristen Dvorscak
Head of Global Quality Systems, Alnylam Pharmaceuticals

Solution | Improving Employee Productivity by Using Generative AI to Deliver Answers in 30 Seconds Instead of 15 Minutes

While respecting GxP compliance, the Alnylam and AWS teams defined a range of parameters for the use cases, including model selection. They ran iterations on several large language models (LLMs) accessible through Amazon Bedrock, using humans to determine which was the most accurate, in addition to scoring comprehensiveness, relevance, and clarity. To further assess accuracy, AWS engineers used cosine similarity, a method of using mathematics to measure the similarity of two sets of information. After extensive evaluation using human and mathematical methods, Alnylam chose Anthropic’s Claude, which summarizes information, maps it to the database, and categorizes it based on rules on which the LLM has trained. The solution frames real-time responses to the adverse-event reporter, providing critical additional information about the complaint faster, while improving decision-making internally, and cutting down time for training new adverse-event investigators joining the company.

The data automatically gets stored in Amazon Simple Storage Service (Amazon S3), an object storage service offering scalability, data availability, security, and performance. The data from the complaint is extracted, converted into a readable format, and sent to the LLM to interpret, infer, and generate the output. The system checks that all fields populate correctly—through a confidence score that checks for comprehensiveness, accuracy, relevancy, and clarity—and automatically creates a record in Alnylam’s quality system.

The model also generates relevant follow-up questions so that Alnylam can better understand patient concerns and the criticality of the defect, streamlining feedback and removing the delay across time zones. “It’s likely that when we come in to work the next day, we have already generated a record that includes important answers to follow-up questions,” says Dvorscak. “This has changed us from operating in a reactive mode to a proactive mode and is designed with patient data privacy and security in mind. As we scale, the number of incoming reports will grow exponentially, and this capability will save us significant time and resources.” As the solution moves out of its pilot phase, it will require human attention only for exceptions and rely on cosine similarity to score model accuracy, saving additional time.

For AskALNY – the internal employee-productivity solution – the platform resulted in gains in productivity, providing answers to employees in 30 seconds when they previously took more than 15 minutes. At the suggestion of the AWS team, Alnylam built further efficiencies into AskALNY using retrieval-augmented generation, a process that generates more accurate responses by prioritizing the LLM’s use of new information over training data.

Outcome | Transforming the Company Mindset through Generative AI on AWS

Alnylam’s strong, secure foundation for generative AI will drive future applications that ultimately will improve drug safety and advance patient care. The company has identified more than 250 use cases for AI, with plans for up to nine projects in the coming year. It plans to apply generative AI to larger-scale intake models, such as workflows concerning clinical development, and medical information. “We will be able to respond quicker to any potential issues in the field, thus protecting our patients,” says Dvorscak. “Using generative AI capabilities on AWS, we are transforming the way we work across the enterprise and resetting our company’s mindset.

Alnylam is sharing its learnings from AskALNY with AWS, providing feedback for Amazon Q for Business, a generative AI–powered assistant designed for work that can be tailored to an organization’s business.

“We are ready to use generative AI, not just within general domains like finance and human resources but also in very strictly regulated GxP areas, like clinical development and regulatory affairs,” Cherawala says. “Instead of months of work to understand how to process generative AI projects, we now have a playbook.”

About Alnylam

Founded in 2002, Alnylam is pioneering the use of RNA interference (RNAi) therapeutics to help people living with diseases for which there are limited or inadequate treatment options.

AWS Services Used

Amazon Bedrock

Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies through a single API, along with a broad set of capabilities you need to build generative AI applications with security, privacy, and responsible AI. 

Learn more »

Amazon Q

Amazon Q Business is a generative AI–powered assistant that can answer questions, provide summaries, generate content, and securely complete tasks based on data and information in your enterprise systems. 

Learn more »

Amazon S3

Amazon SageMaker Studio provides a single, web-based visual interface where you can perform all ML development steps, improving data science team productivity by up to 10x..

Learn more »

More Life Sciences Customer Stories

no items found 


Get Started

Organizations of all sizes across all industries are transforming their businesses and delivering on their missions every day using AWS. Contact our experts and start your own AWS journey today.