Category: Customer Solutions

In 2020, Boosted.ai expanded their artificial intelligence (AI)-powered financial analysis platform—Boosted Insights. But using an LLM came with some significant drawbacks—a high annual cost to operate and GPU capacity limitations that limited their ability to scale.

With its AI-driven automated evaluation and security platform, Patronus helps its customers use large language models (LLMs) confidently and responsibly while minimizing the risk of errors. The startup’s aim is to make AI models more trustworthy and more usable.

We recently sat down with the Timehop and Nimbus CEO, Matt Raoul, as well as two talented team members, David Leviev, VP of Programmatic Product Development, and Mark Laczynski, Senior Cloud Architect to discuss the obstacles the company has faced using third-party ad serving platforms that led to the in-house creation of Nimbus. They shared their challenges and revealed how they leveraged AWS solutions to optimize the development of Nimbus within Timehop, and not only improved the quality of ad servicing to their users, but also increased overall ad revenue.

How does “sustainability” of a company translate into numbers? With millions of potential data points on any given company, compiling and analyzing the metrics that are most significant can be a daunting task. Clarity AI uses artificial intelligence (including machine learning) and AWS to radically streamline and improve the process of measuring the impact of investments and organizations.

Sign-Speak is an innovative startup whose language software recognizes American Sign Language (ASL) and translates it into spoken words (and vice versa) with machine learning. Their platform offers real-time ASL recognition, avatar, and transcription to facilitate communication with Deaf and Hard of Hearing individuals. Sign-Speak leveraged the AWS Impact Accelerator Latino Founders cohort to gain technical and business support as they make the world a more accessible and inclusive place.

When choosing the infrastructure for their ML workloads, startups should consider how to best approach training and inference. Training is process by which a model is built and tuned for a specific task by learning from existing data. Inference is the process of using that model to make predictions based on new input data. Over the last five years, AWS has been investing in our own purpose-built accelerators to push the envelope on performance and compute cost for ML workloads. AWS Trainium and AWS Inferentia accelerators enable the lowest cost for training models and running inference in the cloud.

Originally, Fyle’s Data-Extractor service relied on an external service provider for optical character recognition (OCR) and Fyle’s internal machine learning algorithm to detect amount, category, date, currency, and vendor information. Unfortunately, they were receiving some feedback from customers that their tool wasn’t very accurate. As you can imagine, this isn’t the best place to be, so they rewrote their Data-Extractor service to use Amazon Textract because of its intuitive web console for APIs, which allowed them to test APIs in real-time with personalized input. This let them quickly try out an Amazon Textract API, which helped them achieve their goal of turning around a solution in two months. After implementing their new solution, Fyle saw 51.7% improvement in accuracy for the Data-Extractor service.

MoneySmart lives and breathes efficiency and believes in intelligent use of AWS services to deliver high ROI and keep costs low. Here’s how they’re doing it.