Expected to read upwards of 200,000 words daily from hundreds, if not thousands, of documents, financial analysts are asked to perform the impossible.
Primer is using AI to apply the equivalent of compression technology to this mountain of data to help make work easier for them as well as analysts across a range of other industries.
The five-year-old company, based in San Francisco, has built a natural language processing and machine learning platform that essentially does all the reading and collating for analysts in a tiny fraction of the time it would normally take them.
Whatever a given analyst might be monitoring, whether it’s a natural disaster, credit default or geo-political event, Primer slashes hours of human research into a few seconds of analysis.
The software combs through massive amounts of content, highlights pertinent information such as quotes and facts, and assembles them into related lists. It distills vast topics into the essentials in seconds.
“We train the models to mimic that human behavior,” said Barry Dauber, vice president of commercial sales at Primer. “It’s really a powerful analyst platform that uses natural language processing and machine learning to surface and summarize information at scale.”
The Power of 1,000 Analysts
Using Primer’s platform running on NVIDIA GPUs is akin to giving an analyst a virtual staff that delivers near-instantaneous results. The software can analyze and report on tens of thousands of documents from financial reports, internal proprietary content, social media, 30,000-40,000 news sources and elsewhere.
“Every time an analyst wants to know something about Syria, we cluster together documents about Syria, in real time,” said Ethan Chan, engineering manager and staff machine learning engineer at Primer. “The goal is to reduce the amount of effort an analyst has to expend to process more information.”
Primer has done just that to the relief of its customers, which includes financial services firms, government agencies and an array of Fortune 500 companies.
As powerful as Primer’s natural language processing algorithms are, up until two years ago they required 20 minutes to deliver results because of the complexity of the document clustering they were asking CPUs to support.
“The clustering was the bottleneck,” said Chan. “Because we have to compare every document with every other document, we’re looking at nearly a trillion flops for a million documents.”
GPUs Slash Analysis Times
Primer’s team added GPUs to the clustering process in 2018 after joining NVIDIA Inception — an accelerator program for AI startups — and quickly slashed those analysis times to mere seconds.
Primer’s GPU work unfolds in the cloud, where it makes equally generous use of AWS, Google Cloud and Microsoft Azure. For prototyping and training of its NLP algorithms such as Named Entity Recognition and Headline Generation (on public, open-source news datasets), Primer uses instances with NVIDIA V100 Tensor Core GPUs.
Model serving and clustering happens on instances with NVIDIA T4 GPUs, which can be dialed up and down based on clustering needs. The company also uses a wrapper called CuPy, which allows for CUDA-powered acceleration of GPUs on Python.
But what Chan believes is Primer’s most innovative use of GPUs is in acceleration of its clustering algorithms.
“Grouping documents together is not something anyone else is doing,” he said, adding that Primer’s success in this area further establishes that “you can use NVIDIA for new use cases and new markets.”
Flexible Delivery Model
With the cloud-based SaaS model, customers can increase or decrease their analysis speed, depending on how much they want to spend on GPUs.
Primer’s offering can also be deployed in a customer’s data center. There, the models can be trained on a customer’s IP and clustering can be performed on premises. This is an important consideration for those working in highly regulated or sensitive markets.
Analysts in finance and national security are currently Primer’s primary users, however, the company could help anyone tasked with combing through mounds of data actually make decisions instead of preparing to make decisions.