When TS Imagine first started using Snowflake, it was simply seeking a means to manage its data. Three years later, Snowflake is providing the fuel for the fintech company’s transformation into an enterprise powered by AI.
Based in Bozeman, Mont., but with no central headquarters, Snowflake is a data cloud vendor whose platform enables customers to store and analyze data. In addition, over the past couple of years, the vendor has made AI a focal point, developing an environment where customers can develop, deploy and manage AI, machine learning models and applications.
TS Imagine, meanwhile, is a SaaS-based financial services vendor headquartered in New York City that provides front-office trading, portfolio management and financial risk assessment capabilities. The company was formed in 2021 after the merger of TradingScreen and Imagine Software.
Following the merger, TS Imagine needed a way to integrate and organize years of data from TradingScreen, founded in 1999, and Imagine Software, founded in 1993.
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Snowflake was that way. Now, however, as Snowflake has evolved beyond a platform for data management into an environment for AI, TS Imagine has evolved with it and is using Snowflake’s platform to power its metamorphosis.
“We are a cloud-first company and a Snowflake-first company,” said Thomas Bodenski, TS Imagine’s COO and chief data and analytics officer. “Now there’s a third one: AI-first.”
Using Snowflake, TS Imagine is accessing data previously unavailable to inform decisions. It’s using AI to manage certain processes, and it’s reaping financial benefits.
First, however, it just needed to get organized.
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Starting with Snowflake
When TradingScreen and Imagine Software merged in May 2021, the newly formed company faced challenges. Both TradingScreen and Imagine Software brought with them well over two decades of data. In addition, the newly formed company had two data teams; two technology stacks; and plans to expand into new areas, such as fixed-income securities trading.
TS Imagine needed a way to unify that data, and it needed to do so in a single system that would help its expansion.
“We very quickly identified the data as the area where we needed to focus,” Bodenski said. “We knew, strategically, that we had to do something. We have to have data ready at any time because it’s used for trading and for risk management. We need to solve problems that our clients are never meant to see.”
TS Imagine manages over 20 million financial instruments — assets such as stocks, bonds, loans, funds and certificates of deposit that can be traded or exchanged. Each, including the client that owns the instrument, generates data, meaning that TS Imagine needs to manage massive amounts of data to serve the needs of its customers.
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It, therefore, needed a data management platform that was simple enough to enable users to easily access data when needed and could also handle scale.
One option included the platforms previously used by TradingScreen and Imagine Technologies. Others included platforms such as Markit EDM and GoldenSource geared specifically for reference data used to categorize financial transactions and for enabling semantic modeling for financial instruments.
Ultimately, TS Imagine chose Snowflake.
Timeliness was a key factor in the TS Imagine’s decision given that it needs to access data in near real time to inform and execute trades, according to Bodenski. So were the breadth and depth — the scale — of Snowflake.
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Finally, simplicity played a significant role.
Snowflake understands Python and SQL code. If TS Imagine had chosen a platform that required Java or C++, for example, few of its developers would have had the requisite skills to use the platform. But because Python and SQL can be used in Snowflake, 54 data scientists, engineers and other data experts already had the needed skills.
“We felt that with Snowflake, we had a platform that could empower us,” Bodenski said. “We were able to grow and scale overnight from a small team to a large organization.”
Now, TS Imagine stores all its data in Snowflake and runs all its data management processes, such as data quality monitoring, pipeline monitoring and automated regression testing, in Snowflake.
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Enterprises might receive these seven benefits when using generative AI.
Adding AI
About a year after TS Imagine got its data in order on Snowflake, OpenAI launched ChatGPT.
Released in November 2022, ChatGPT was a significant improvement in generative AI capabilities over what was previously available. Of particular interest to many organizations were its natural language processing (NLP) and automation capabilities.
Enterprises quickly recognized that if they could combine those capabilities with proprietary data to understand an organization’s operations, they could reap significant benefits such as more widespread use of analytics due to NLP and efficiency gains due to process automation.
Among the organizations that saw the possibilities of generative AI in the enterprise was TS Imagine.
“When the hype around ChatGPT started, we got very excited,” Bodenski said. “All of my executive peers are into data as well, and ChatGPT was part of every meeting.”
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TS Imagine had already experimented with NLP and machine learning to automate tasks, such as data classification and cataloging. However, converting unstructured data to structured data to inform models and applications had proven to be difficult.
Unstructured data, such as text, images and audio files, is estimated to make up well over three-quarters of all data. Tapping into unstructured data is critical to gaining a full understanding of an organization.
With Snowflake still focused largely on data management at the time, TS Imagine viewed ChatGPT’s generative AI capabilities as a way to finally gain access to its unstructured data, particularly text in emails and PDF documents.
“We needed to make it more actionable by converting it into structured content,” Bodenski said.
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TS Imagine developed an AI engineering team that worked with its data experts to use the data it had stored in Snowflake to train ChatGPT to analyze text.
It created an AI pipeline using open source database ChromaDB to vectorize unstructured data to give it structure, LangChain to develop a retrieval-augmented generation (RAG) pipeline to discover the relevant data required to train its models, and containers from Google Cloud to run its generative AI workloads.
The result was models that delivered precise, accurate outputs when analyzing text from over 500 clients, according to Bodenski.
“It had an unbelievably high provision rate to the point where we could rely on it,” he said.
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Nevertheless, TS Imagine did not automate final decisions based on generative AI outputs. It still put a human in place to check the outputs for accuracy and make any final decisions.
For the next year, TS Imagine continued to use ChatGPT to underpin its generative AI development and analysis. That was until Snowflake began developing its own environment for generative AI.
Snowflake for everything AI
Enterprises like TS Imagine weren’t the only ones who recognized the potential value of generative AI following the release of ChatGPT.
With data serving as the underlying engine for AI — the information used to train and inform AI models and applications — analytics and data management vendors, from specialists such as MicroStrategy and Monte Carlo to tech giants such as AWS, Google Cloud and Microsoft, all made generative AI a focal point of their product development plans.
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Snowflake rival Databricks was especially aggressive in building up an environment for customers to create AI models and applications. After a slow start, Snowflake followed suit.
We are a cloud-first company and a Snowflake-first company. Now there’s a third one: AI-first. Thomas BodenskiCOO and chief data and analytics officer, TS Imagine
In May 2023, Snowflake acquired Neeva, a search engine specialist, to procure generative capabilities. Five months later, the vendor introduced Cortex, an environment for AI development that includes access to LLMs and vector search capabilities, among other capabilities. Since then, Snowflake has continued to add tools aimed at enabling AI and machine learning development, including its own LLM and a chatbot development framework.
With Snowflake — via Cortex — providing the same capabilities TS Imagine was piecing together with ChatGPT, ChromaDB, LangChain and Google Cloud, the financial services specialist decided to migrate its AI operations to Snowflake.
The process was simple, taking one engineer one week to complete the entire undertaking, according to Bodenski.
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“Everything AI runs exclusively on Snowflake now,” he said.
Immediately following the migration, simply by eliminating the cost of using various platforms to create an AI pipeline and instead using the tools provided by Snowflake, TS Imagine saw a 30% reduction in spending related to training and managing its generative AI capabilities.
“That was significant for us,” Bodenski said. “It’s a one-stop shop for us. We can build the entire AI pipeline on the technology we are all familiar with.”
With its data already residing in Snowflake, TS Imagine simply builds an AI pipeline on top of that data without needing to move the data to another system where it might get accidentally exposed. In addition, with all the required pieces of an AI pipeline in one environment, it takes just a few days to develop a new model or application.
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Results
In the year since the migration, after developing text analysis capabilities using ChatGPT for generative AI capabilities and moving that to Snowflake, TS Imagine has developed five other generative AI pipelines for different applications.
Beyond analyzing emails and PDFs, one of the key applications of generative AI is to monitor customer service. TS Imagine receives an average of 5,000 inquiries per month. Fully understanding everything related to customer service is challenging.
“If you are the global head of customer service, it’s not easy to get that overview,” Bodenski said. “And if you are a regional manager, it’s hard to know everything that is going on.”
With its customer service application, TS Imagine can now classify each customer service incident, automatically assigning sensitivity ratings as well as understanding the sentiment, urgency and complexity of the request.
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“Those are all steps that would have had to have been done manually,” Bodenski said.
Tangibly, by developing and deploying generative AI tools using Snowflake, TS Imagine has saved thousands of hours of work — including 4,000 that would have been devoted just to analyzing emails –that otherwise would have been done manually, he continued.
“It allows us to utilize people to do work that is more analytical, more knowledge-oriented,” Bodenski said. “We can use people to be more productive on other tasks.”
Despite all its benefits, like most enterprises using generative AI to improve operations, TS Imagine is working through some problems.
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While using Snowflake to develop generative AI tools has been a smooth process, getting models and applications to consistently deliver outputs that can be trusted remains a concern, according to Bodenski.
“There is still a challenge with what large language models produce,” he said.
Accuracy has been a problem for generative AI. Even when trained using high-quality data, models and applications sometimes still deliver incorrect and even bizarre outputs called hallucinations.
To combat those inaccuracies, TS Imagine runs its RAG pipelines multiple times for each query to try to weed out any outliers. Still, however, the company makes sure there is always a person in place to take any action rather than trust the model or application to automatically go from output to action on its own.
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“We need to constantly look at the results,” Bodenski said. “And you really need to find the right use cases. This stuff doesn’t solve everything. You need to find the right use cases, and that’s when you get high precision rates. Even still, the outputs are sometimes very strange.”
Future plans
With six RAG pipelines running after one year using Snowflake for its AI development and deployment, TS Imagine has plans to add more AI applications, according to Bodenski.
To date, what the company has done with generative AI is to automate processes to make workers more efficient. It hasn’t yet developed AI assistants that enable business users to query and analyze data using natural language.
TS Imagine has used Snowflake to develop applications its customers can use to analyze data. But those applications are traditional analytics applications rather than AI-powered applications.
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The next step is to add generative AI to those applications to enable clients to broaden their use of BI beyond data experts as they analyze financial transactions and strategies.
“What we aim for is self-service analytics,” Bodenski said. “There is a lot of data involved in financial transactions, and with this data available, our clients can self-service themselves. We want to bring AI through our products to our clients. That’s the final objective.”
Eric Avidon is a senior news writer for TechTarget Editorial and a journalist with more than 25 years of experience. He covers analytics and data management.
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Retail giant Walmart is no stranger to AI and has begun testing its own large language model (LLM), which it may use for other applications.
Wallaby, a suite of retail-focused LLMs, is trained on decades of Walmart data and understands how Walmart employees and customers talk. It is also trained to respond in a more natural tone to better align with Walmart’s core values of customer service.
Desirée Gosby, vice president of emerging Technology at Walmart Global Tech, told VentureBeat in an interview that the company wants to extensively test Wallaby before releasing it to a wider audience.
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“Wallaby is not being used yet because we are testing it quite heavily internally, in particular with our associates since we have such a large base of associates,” Gosby said. “Over the next year, we’ll start to leverage it.”
While she thinks Wallaby’s first use cases will be more consumer-facing, the new LLM will be part of a stable of models Walmart plans to use when developing new applications.
It’s not surprising that Walmart would choose to train its own series of retail-specific LLMs. Developing internal models is expensive, and even fine-tuning third-party models can get expensive. As one of the largest retailers in the world, Walmart not only has the war chest to experiment with AI models, but it also sits on a ton of customer, employee, logistics and retail data that enriches a model.
Mixture of models
Like many companies, the retailer prefers to use the best model it finds for the use case it wants to address. Sometimes, this means using off-the-shelf or third-party models, or Walmart can use another previously developed algorithm.
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Gosby said Walmart has a multi-layered approach to bringing AI to its technology stack. It uses a platform called Element to plug and play different models, both from third parties or its proprietary LLMs, to direct them to specific applications.
“It’s helping us manage those models, and at the foundation are the different LLMs we use, one of which is the retail-based ones in Wallaby,” Gosby said. “At the end of the day, it’s really going to come down to what problems we’re trying to solve, and we will figure out the best approach, maybe it’s leveraging a mixture of models.”
Gosby said during VentureBeat Transform in July that the retailer has been expanding its technology use and taking a platform approach to integrating AI. Walmart had been using GPT-4 for many of its AI applications, especially as more customers were turning to its mobile and web storefronts.
Of course, Walmart is not the only retailer with AI in its applications. Amazon released Rufus, a chatbot powered by AWS models that answers questions about products and references customers’ reviews on the platform.
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AI all over its applications
Employees at Walmart’s Bentonville, Arkansas headquarters have been using AI for many years. The company rolled out a chatbot for associates to ask questions about Walmart’s policies and employee handbooks. It has also brought in AI to streamline its operations, including managing its supply chain.
Its physical storefronts —Walmart and Sam’s Club — also feature AI technology to help floor associates assess inventory and manage check-out procedures.
Walmart also expanded generative AI tools on its digital platforms. Gosby said Walmart has had a chat feature for years, but it enhanced its Customer Support Assistant this month. The chatbot will now recognize customers from the start and will be able to understand customer intent.
Walmart showed reporters a demo of the feature that lets a customer express in natural language that they accidentally bought some toys. The Customer Support Assistance focuses on the word “toy” and brings up a recent order for toys, ignoring other purchases that do not fit the conversation’s context. Without prompting, the assistant will also ask if the customer wants to return or keep the product.
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The retailer plans to roll out more personalized recommendations and homepages to customers.
Gosby said Walmart will continue to explore how AI can help smooth customer and employee experiences. And in the next year, these experiences may include applications powered by Wallaby.
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Today on Equity, Rebecca Bellan sat down with Zander Brumbaugh, the 22-year-old founder of Gamebeast, a startup that offers Roblox developers tools like A/B testing and LiveOps to modify games without needing to release a new version or interrupt a user’s ongoing game.
Gamebeast recently raised a $3.7 million pre-seed round, led by J2 Ventures with participation from a16z’s Speedrun accelerator, which Brumbaugh graduated from in March. Brumbaugh said everything from his experience consulting for entertainment studios like Netflix, and writing a best-selling book on Roblox development, to chats with investors tell him that the future of video gaming is in user-generated content, or UGC.
The two discussed the rise in popularity of UGC games, a sector that investors are increasingly looking towards as drivers of growth in the video game industry. Brumbaugh noted that a large majority of kids under 16-years-old asked their parents for virtual currency, like Roblox’s Robux or Fortnite’s V-Bucks, during the last holiday season.
Most of the games, experiences, and virtual worlds on Roblox are user-generated, and that’s by design. Fortnite, which is well known for its Battle Royale player-vs-player game, also has a sandbox mode that allows players to build and create their own worlds and experiences. Creative mode is fast gaining popularity.
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“These platforms make it extremely easy to make content from a young age,” Brumbaugh said. “I started when I was 12, and taught myself how to program in Lua and kind of learn my way around the Roblox engine. I think people being able to creatively express themselves and make games that they themselves enjoy, and then also turn that into a profitable hobby, or in many cases, a career, that’s a really exciting thing.”
On the show, Brumbaugh also shared his thoughts on how generative AI is changing the gaming landscape.
“You can’t really discuss AI, especially in creative spaces, without there being an immediate knee-jerk negative reaction from a lot of people in the community,” Brumbaugh said. “But ultimately, I think as models get better, the best thing is always going to be integrating these types of models into a workflow, making something easier for developers.”
Brumbaugh didn’t only get investment for his startup because investors see the value in enabling UGC game development. J2 Ventures’ thesis focuses on dual-use technology, and Gamebeast was able to demonstrate how its tech has applications both in commercial and defense. And indeed, we’re seeing a trend of companies going for that sweet military money to stay afloat.
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There are more insights about the future of the video game market and investor insights aplenty, so have a listen, and enjoy!
Equity is TechCrunch’s flagship podcast, produced by Theresa Loconsolo, and posts every Wednesday and Friday.
Subscribe to us on Apple Podcasts, Overcast, Spotify and all the casts. You also can follow Equity on X and Threads, at @EquityPod. For the full episode transcript, for those who prefer reading over listening, check out our full archive of episodes over at Simplecast.
That’s just a glimpse of the best Apple deals we’re currently seeing, though. Below, we’ve compiled the best across a range of categories — including tablets, headphones, styluses, and more — so you can sift through Apple’s various wears more easily. We’ve also put together a larger guide to the best Prime Day deals overall, just in case you also want to take a look at what’s on sale outside of the Apple ecosystem.
Update, October 9th: Adjusted pricing and availability.
We’ve recently been working with a set of ASRock Rack servers that are ideal for SMBs and system integrators looking for flexible, high-density storage systems. Because they both support NVMe flash though, the story gets a little better in terms of what these systems can deliver when it comes to performance. Whether it’s virtualization use cases, software-defined storage or a simple backup target, both of these servers have compelling stories. We have a deep dive on both here –
You’ve probably never heard of Foxconn Technology Group, but it is a Taiwanese consumer electronics manufacturer that is perhaps best known for supplying the world with Apple iPhones – it also makes most of Amazon’s Kindles and Nintendo’s gaming consoles.
Stopping short of actually producing the folklore legend Apple Car, the company has branched out into developing electric vehicles of its own under the Foxtron moniker, with its existing Model C five-or seven-seater SUV already licensed by the rapidly expanding Taiwanese automotive brand Luxgen and badged the n7.
While Foxtron isn’t exactly interested in becoming a recognized automotive name in its own right, it is keen to sell its designs and platforms to other, perhaps more established players in the EV market.
As such, it used its most recent Apple-esque Technology Day event to reveal two new models in the Model D and the Model U – a naming convention that might have folk at Tesla pulling a confused face.
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The Model D is an absolutely massive, angular Lifestyle Multipurpose Utility Vehicle (LMUV), which measures 5.1m in length (think Ford Transit van size) and almost 2m wide. Inside, there’s enough space for seven people to ride, although Foxtron is keen to point out that it can configure its cars to a B2B customer’s demands.
(Image credit: Foxconn Technology Group)
Powering the LMUV is a range of 100-120kWh lithium-iron-phosphate (LFP) battery packs, which would see an effective range of around 310 miles to almost 400-miles in the range-topping versions.
Designed with aerodynamics at the forefront, it features all manner of creases and ducts to improve airflow. Despite its blocky look, it was actually penned by Pininfarina, the designers behind numerous classic Italian sports cars.
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Naturally, Foxtron has also ensured the interior of its Model D plays nicely with the technology its sister company manufacturers. There are specific charging spots for iPhones in the rear, as well slots for passengers to attach their own iPads.
At the opposite end of the spectrum, the Model U is a mid-sized electric bus that features the latest in advanced driver assist systems. Perhaps Tesla needn’t be too worried, after all.
Analysis: A Chinese ban could open the door for Taiwan
(Image credit: Foxconn Technology Group)
The purpose of the Foxconn Technology Group’s Technology Day event was to reveal a series of vehicles that the company says is destined for the North American market.
The Model C, which is already on sale in Taiwan, has seen its design tweaked slightly for a US audience and could be heading there soon, while the aforementioned Model D is set for production some time in 2026.
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However, there’s no word on exactly what badge these cars will carry when they eventually go on sale to the general public, with Foxconn’s Contract Design and Manufacturing Service (CDMS) business model essentially providing the platform, hardware and updatable software to the highest bidder.
But with the US and much of Europe clamping down on imported Chinese EVs, it could be the perfect opportunity for Taiwan to offer the cheaper but more technologically advanced electric vehicle.
Foxtron already has models ready to roll that cover everything from the small B-segment hatchback to a much larger pick-up truck. It has also made some serious advances in electric vehicle technology.
Battery partnership programs with Pininfarina, ZF, Our Next Energy (ONE) and BMW have spawned ultra high-range LFP packs, with the resulting Gemini Dual-Chemistry Battery able to power a BMW iX for 608 miles on a single charge.
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Plus, a relationship with Nvidia, albeit one that provides servers for AI applications, will likely put it in a favorable position to receive the latest single-chip architecture innovations required for Software Defined Vehicles and those with high levels of autonomous driving capabilities.
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