Meet the 2024 Snowflake Startup Challenge Finalists

The 2024 Snowflake Startup Challenge began with over 900 applications from startups Powered by Snowflake in more than 100 countries. Our judges narrowed that long list of contenders down to 10, and after much deliberation, they’ve now pared it down to the final three. 

We are pleased to announce that BigGeo, Scientific Financial Systems and SignalFlare.ai by Extropy360 will advance to the Snowflake Startup Challenge finale and compete for the opportunity to receive a share of up to $1 million in investments from Snowflake Ventures, plus exclusive mentorship and visibility opportunities from NYSE. 

Many thanks to the other semifinalists for their dedication and the effort they put into their presentations during the previous round of competition. 

Let’s get to know the 2024 finalists.

Crunching vast amounts of geospatial data is an intimidating, resource-consuming task. But the potential rewards are so rich — whether it is mapping the spread of diseases; determining optimum places for new housing developments; creating more efficient travel and shipping routes; and yes, providing more accurate and timely weather and traffic reports. 

BigGeo is looking to remove the intimidation factor and give companies immediate, interactive geospatial insights. 

“With our technology, clients can execute fast and effective geospatial queries, integrate seamlessly with Snowpark Container Services and significantly improve data visualization,” says Brent Lane, Co-Founder and CEO of BigGeo. “This makes geospatial insights more accessible and actionable than ever before, empowering organizations to make informed decisions quickly.”

BigGeo’s mission is to convert the theoretical advancements uncovered during the founders’ 15 years of research into practical, market-ready solutions. Its Volumetric and Surface-Level Discrete Global Grid System (DGGS), which manages surface-level, subsurface and aerial data, supports the integration of diverse data forms, including 2D and 3D data, and facilitates dynamic interactions with spatial data. The containerized deployment within Snowflake Native Apps allows interoperability across various data sets and enables secure, governed geospatial AI.

The ability to handle large volumes of real-time geospatial data and meet customers’ complex analysis demands is a particular point of pride for the BigGeo team. One of their customers, a major data supplier, used the solution to stream near real-time visualizations of a massive 150 million polygon data set at sub-second speeds, surpassing the capabilities of competing solutions. By directly connecting the visualization layer to the data supplier’s data warehouse, BigGeo enabled informed decision-making directly through the map. 

“This accolade has definitely energized the entire BigGeo team to continue developing solutions that address real-world challenges, drive significant industry change, and build environmentally conscious solutions that align with our vision for a greener future,” says Brett Jones, Co-Founder and President of BigGeo.

The team is excited to present at the Startup Challenge finale. Not only is it an opportunity to expand BigGeo’s network and sharpen its competitive edge, but the team hopes to gain valuable insights from top-tier leaders.

“We are very excited to meet Lynn Martin, President of NYSE Group. She has a profound understanding of technology’s crucial role in data integration, transformation and management — areas central to our work,” says Lane. “Her passion for AI and its role in enhancing data services also aligns with what we do at BigGeo.” 

Beating the market is the driving force for investment management firms. In today's markets, that often means making quick calculations over vast volumes of data to locate those scarce alpha opportunities. This is a difficult and time-consuming task, one that spurred Scientific Financial Systems (SFS) to develop a new solution: Quotient.

Quotient enables financial institutions to rapidly analyze large amounts of data and provide relevant recommendations quickly. Running natively on Snowflake, Quotient uses a novel semantic layer that integrates Python and SQL technologies. For SFS, Snowflake was in the right place at the right time: Quotient embodies the concept of “localized compute” and was an ideal candidate for the Snowflake Native App model, which helps SFS address scalability. 

“We are thrilled to share our story about building our applications on top of Snowflake and leveraging the power that Snowflake offers in security, performance and scalability,” says Anne Millington, Co-Founder and CEO. “It is very rewarding to be recognized for the innovations we offer.” 

The structure and power of Quotient give investment managers the tools they need to find increased alpha outperformance. For small investment managers, Quotient data science and ML techniques provide an immediate and incredibly robust quant bench. For large investment firms, SFS provides a framework to streamline and improve data analytics so their teams can spend more time on research. 

StarMine’s quantitative analytics research team has seen the benefits for itself. The team focuses on developing financial models based on the evaluation of factors that may impact equity performance. The Snowflake Data Cloud provides an ideal environment to evaluate factors by running them against vast amounts of historical data. With the colocation of Quotient compute and StarMine’s data, research that previously took two to three weeks can be completed in one to two days. Plus, StarMine was able to run the factor based on a broad global universe without restriction and see the results before making further customizations to drill into specific equity criteria. With full transparency into the Quotient engine’s calculations, StarMine has confidence in the results.

As for the SFS leadership team, they are honing their presentation for the Snowflake Startup Challenge finale and looking forward to making their pitch. Given their focus on investment firms, winning mentorship from NYSE companies would be “a tremendous honor,” says Millington.

“Gaining insight into the needs and perspectives of these NYSE-listed companies would offer great value to the SFS team on multiple levels, from product roadmap considerations, technology implications for AI and NLP, operational implications and more. The expertise and experience of learning from real-world examples at the highest echelon of success would be invaluable,” she explains.

“We are beyond excited, and frankly a bit shocked” to be a Snowflake Startup Challenge finalist, says Michael Lukianoff, Founder and CEO of SignalFlare.ai. “The team and I come from years of experience in brick-and-mortar restaurant tech and data — which has never been held in the same regard as e-commerce or social media tech. We feel like this honor is not just a recognition of SignalFlare.ai, but of the industry we represent, where the opportunities are boundless.”

Those opportunities are the reason why SignalFlare.ai’s founders created a decision intelligence platform for chain restaurants. Devastated by the impact of COVID-19, the restaurant industry needed to reinvent how it analyzed demand and use data to make better decisions in high-impact areas, like pricing, promotion, menus and new market opportunities.

SignalFlare.ai built new methods, tapped into new data and created a different tech stack, developing a solution, with Snowflake at its core, that incorporates geospatial data for targeting, along with ML models for pricing optimization and risk simulation. Snowflake’s architecture allows visibility into data transformation strategies and performant cross-customer analytics. The team implements Dynamic Tables to ensure the timeliness of changing source data and filters results specific to target analytics. Streamlit apps assist in monitoring incoming data quality and Snowpark integrating ML models for training and returning inferences to Snowflake for downstream analytics. 

Authentic Restaurant Brands, a restaurant acquisition fund that is part of SignalFlare’s “innovation circle” of customers — essentially a test group and sounding board for new ideas and products — has become an avid user of SignalFlare. The company started by validating the SignalFlare solution and pricing method on one brand; after seeing the benefits, it added two more and recently added a fourth after an acquisition. “I have worked with many pricing vendors in my career. SignalFlare’s approach is the most thorough and cost-effective I have encountered,” says Jorge Zaidan, Chief Strategy Officer of Authentic Restaurant Brands. 

Looking ahead to the finale, the SignalFlare team is eager to present and to meet Benoit Dageville, Co-Founder and President of Product at Snowflake, who is part of the Startup Challenge judging panel.  

“The vision that Benoit brought to life made our vision possible,” says Lukianoff, noting that Snowflake was “life-altering” for people like himself, who are obsessed with data accuracy and usability. “The features being released are constantly making our job easier and more efficient, and creating more opportunities. That is a different experience from any technology partner I’ve experienced.”

Want to see which of these three startups will take the top prize? Register for Dev Day now to see the live finale and experience all of the developer-centric demos and sessions, discussions, expert Q&As and hands-on labs designed to set you up for AI/ML and app dev success.

It’s also never too soon to start thinking about the next Snowflake Startup Challenge: Complete this form to get notified when the 2025 contest opens later this year.