Large language models, generative AI, and foundation models are driving a dramatic paradigm shift in how AI is being applied today. Numbers Station is pioneering new technology to disrupt the data automation market. And today, we’re excited to announce our investment in Numbers Station’s Series A.
Using AI to Disrupt the Massive Enterprise Data Market
By now, it’s safe to say that GPT and BERT foundation models’ abilities are well-known to all. Organizations are increasingly moving away from expensive models trained on massive amounts of data to a world of pre-trained models that can be used to augment humans as “co-pilots” in a variety of tasks. Examples include sales coaching (e.g. Gong), sales emails (e.g. Lavender), copywriting (e.g. Jasper, Copy.ai), creative/design (e.g. Stability.ai, Runway), and document extraction (e.g. AirPaper).
Leveraging its proprietary foundation model, Numbers Station is building an AI co-pilot with which data analysts can collaborate. Using natural language, users can offload repetitive data tasks such as data cleaning, data preparation, deduplication, classification, and normalization.
Addressing the Growing Market of Enterprise Data
Repetitive tasks can take up 80 percent of a data worker’s time, according to Harvard Business Review. Organizations are forced to spend heavily on data analysts and scientists in the loop to ensure high data quality. Today, the data preparation tooling market is a sizable multi-billion-dollar market that’s growing quickly. After speaking with countless data quality and analytics leaders, there is unanimous feedback on how ineffective and manual today’s data workflows still are, even with the many tools in the market.
Repetitive tasks can take up 80 percent of a data worker’s time. Numbers Station’s AI co-pilot offloads those repetitive tasks, including data cleaning, classification preparation and more.
By empowering its users to automate data-intensive workflows, information workers are freed from the most basic of data tasks to the most complex.
Built by a Strong Team That Wrote the Seminal AI Research in This Space
Chris Aberger, Ines Chami, Sen Wu, and Chris Re spent years in Stanford’s AI Lab building data task automation technology and even pioneered the use of foundation models on these data automation tasks (research paper here). Before Numbers Station, Chris Aberger served as the Senior Director of ML at SambaNova Systems, where he built the AI team from the ground up. This experience revealed a lot of the challenges that customers faced firsthand.
With the launch of the platform today, we can’t wait to see what’s yet to come for Numbers Station and its customers. We’re excited to partner with Madrona and Factory and extend the Numbers Station team a warm welcome to Norwest’s portfolio!
