Even in today’s highly digital workplace, documents are often manually processed in many enterprise workflows, including workflows in financial services. Alkymi, founded by a team from Bloomberg and x.ai, enlists automation to streamline this laborious and error-prone work. Using deep learning models hosted on Amazon SageMaker, Alkymi identifies patterns and relationships in unstructured data and synthesizes documents into actionable data. This gives enterprises the potential to save billions in the process by removing a stubborn barrier to automation.
Alkymi uses AWS as their primary AI/ML platform. The CEO of Alkymi, Harald Collet, notes, “We apply AI to help automate tasks on documents that require human comprehension, and AWS has enabled us to quickly launch new functionality with the security and scalability that financial services customers require.” As Alkymi ingests documents, emails, and images, the platform automates data extraction and data entry tasks by using various AWS services. “AWS allows us to scale our platform to handle customers of all sizes. Amazon SageMaker has improved our development process by providing our data scientists with a way to train and deploy models to production,” remarks Alkymi CTO Steven She.
Alkymi’s data pipeline begins with ingesting documents and images through their REST API hosted on Amazon Elastic Container Service (ECS) or as email received through Amazon Simple Email Service (SES). The data are saved into encrypted Amazon S3 buckets based in geo-regions that adhere to the compliance policies of our customers.
Documents are placed into messaging queues, then processed by pipelines of Amazon SageMaker machine learning and natural language processing models. The data science team loves Amazon SageMaker’s streamlined UI and workflow, which make it possible for the data scientists to train and deploy the models themselves. Alkymi’s sophisticated ML models are both trained and hosted on Amazon SageMaker. With just a few clicks, the team can identify the context of the information on each page, such as tables, paragraphs, info boxes, and charts. This ensures that the natural language processing can be maximally effective as it operates within context. All model predictions come with a confidence score. Documents where the models have a low confidence score are flagged and routed for human review.
After clients deploy Alkymi in production, end users, such as business or ops analysts, no longer need to use a manual copy-and-paste workflow. Instead, they only need to validate a small amount of exceptions that have been flagged by Alkymi. These corrections fuel a feedback loop that improves model accuracy and performance over time. As a result, the business can move forward quicker, with fewer missed opportunities, less risk, and much less operational overhead. Alkymi’s customers estimate that the platform automates up to 90 percent of manual document processing tasks and cuts errors by 50 percent—all while generating actionable insights in real time rather than days or weeks later.
For Alkymi, the business impact is exciting, and the potential is limitless. As customers are rapidly embracing AI / ML technologies, Alkymi is committed to maintaining its position as a pioneer in a fast-growing market. Harald Collet comments, “We’re tackling a massive opportunity to help financial services companies transform how works get done and rapidly innovate to keep pace with the market.” Building on the AWS platform and energized by the support of the AWS Accelerate program, Alkymi is on an unstoppable mission to deliver digital transformation for financial services.
About the Author
Marisa Messina is on the AWS AI marketing team, where her job includes identifying the most innovative AWS-using customers and showcasing their inspiring stories. Prior to AWS, she worked on consumer-facing hardware and then university-facing cloud offerings at Microsoft. Outside of work, she enjoys exploring the Pacific Northwest hiking trails, cooking without recipes, and dancing in the rain.