Retool Awarded 2025 Databricks Emerging Partner of the Year
SAN FRANCISCO--(BUSINESS WIRE)--Retool, the leading application layer for AI, is honored to announce its recognition as the 2025 Databricks Emerging Partner of the Year. Presented at the annual Data + AI Summit, the award celebrates Retool's deep integration with the Databricks Data Intelligence Platform, which has helped shared customers accelerate development, securely scale internal applications, and realize tangible business outcomes from AI investments.
'Databricks has redefined the modern data stack, and Retool is proud to be the app layer that helps enterprises turn that data into action,' said David Hsu, co-founder and CEO of Retool. 'This award is a testament to our shared vision where, with our help, developers drive the future of work, powered by AI.'
"These awards are always one of my favorite moments of the year, and we are thrilled to name Retool the 2025 Databricks Emerging Partner of the Year," said Roger Murff, VP of Technology Partners at Databricks. "As more enterprises seek to build domain-specific AI applications, Retool's partnership with Databricks is essential to helping these organizations leverage the right data to build AI agents that drive real business outcomes."
This award reflects growing recognition of how organizations are transforming operations through AI-powered automation. Our Databricks integration addresses this need by giving teams a direct path from raw data to business outcomes—whether that's operational dashboards for real-time insights or autonomous agents handling complex workflows. The platform handles the technical complexity of security and scale, so developers can focus on solving business problems rather than infrastructure challenges.
About Retool
Retool is the application layer for AI and leading platform for internal software development, trusted by over 10,000 companies worldwide, including Amazon, Stripe, Brex, and Orangetheory Fitness. Using Retool, developers deploy sophisticated apps and agents dramatically faster without sacrificing quality or control, combining powerful building blocks with the flexibility of custom code. The company's model-agnostic approach lets teams select the optimal AI capabilities for each use case while maintaining enterprise-grade security and scalability. To learn more, visit https://retool.com.
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As their heads were measured and fitted for custom-made, felt cowboy hats, the 100 or so guests assembled in Utah's scenic Wasatch Mountains in November 2023 had ample reason to feel special. The group of AI executives, venture capitalists, government officials, and policy folks, had been handpicked to attend a secretive, three-day retreat focused on the national security implications of artificial intelligence. Presiding over the confab was Alexandr Wang, the young CEO of data labeling AI startup Scale. Wang's company was eight years old and already worth $13 billion at the time, but the event at the Montage Deer Valley, co-hosted with longtime Scale angel investor Nat Friedman, was clearly intended to signal Wang's status as more than just the latest Silicon Valley wunderkind. Sitting alongside gray-haired Pentagon top brass, the 26-year-old Wang held forth on the U.S.-China AI arms race and other weighty topics. 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But when news emerged this month that Wang was joining Meta to be part of a new 'superintelligence' team reporting directly to Mark Zuckerberg, part of a $14.3 billion acqui-hire, industry watchers were still stunned. The deal, which values Scale at $29 billion and Wang's personal stake at a reported $5 billion, is Meta's largest outside investment ever. The stakes couldn't be higher for Meta, as it transitions its business into the rapidly-evolving AI era and races against giants like Google and OpenAI to develop all-powerful AGI and 'superintelligence' capabilities. In Wang, and in the 49% stake in Scale that Meta is acquiring, Zuckerberg appears to see a secret weapon. There are even rumors that Wang could be crowned the head of Meta's entire AI operations—rumors that have only added to the consternation among those wondering how a young entrepreneur whose business relies more on manual labor than large language models fits into Meta's quest for AI supremacy. Fortune spoke to more than more than a dozen people close to Wang, including current and former Scale employees, investors, acquaintances, and competitors, to trace how the 28-year-old MIT dropout built the business at the center of one of the richest deals in the AI boom, and to understand why Meta is betting so much on it. Meta declined to comment or to make Wang available. 'Alex is a great recruiter, a really savvy commercial person,' said one former Scale manager. 'Who knows if it'll work out? Maybe he builds a better AI team into something Herculean, maybe not, but you're gonna bet on someone to do it. There's probably a handful of people in the world that you bet on to do it. I think he's probably one of them.' Meta's relationship with Scale dates back to 2019, when the social media company began using Scale as a data provider for its AI efforts. 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'I never thought about the idea of Alex leaving Scale, especially when we'd just announced the tender offer at a $25 billion valuation. I think about how fast it all happened.' Scale responded to Fortune's request for comment by pointing to a blog post from new CEO Jason Droege that affirms the company's continued independence, its commitment to not favoring any specific AI models, and hinting at upcoming announcements. After the deal was publicly announced, Wang addressed Scale employees at the company's South of Market office in San Francisco. He got a standing ovation as he walked down a winding staircase to the office building's atrium. He cried at times as he spoke to employees about his time at Scale and starting the company when he was a freshman at college. 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Wang, who competed in national math competitions in high school, dropped out of MIT after one year to pursue the startup dream, teaming up with Lucy Guo, a product designer at Quora, who had herself dropped out of Carnegie Mellon to pursue a Thiel Fellowship and had been Snap's first female designer. The duo originally planned to create tech for a doctors' concierge service, but after joining startup accelerator Y Combinator in summer of 2016, they eventually pivoted to data labelling. 'They were originally working on completely different ideas and spent most of the first summer just figuring out what to build,' recalls Friedman, who served as Wang and Guo's partner at Y Combinator in 2016. Once it pivoted to data labelling, Scale found the perfect customer base in the rapidly-expanding, and well-funded, group of startups working to build self-driving vehicles. Wang embodied the company credo of 'Ambition shapes reality,' said an early employee who left in 2022. 'Alex himself would get deeply involved in customer engagements when needed, including deep-diving into both technical challenges and helping to network and negotiate with higher-ups on the business side to help us win customers and new business.' Another source, the former Scale manager, described two-hour-long daily meetings 'where we would review every single account.' The routine was not universally enjoyed inside the ranks, the person said, 'but honestly, in retrospect it forced everyone to be very rigorous.' Tensions between the two cofounders soon surfaced as the company grew. Guo had recruited Wang, and she was the original CEO. But the two founders could not get along, clashing over how they each deemed their counterpart was handling their duties, according to a source familiar with the matter. The board sided with Wang, who became CEO, and Guo left the company in 2018, according to this person's account. Guo told Fortune that when Wang proposed taking on the CEO role after the Series A, believing 'he'd be better as the face of an API company,' she agreed. 'I wasn't title centric and was fine with it,' she said in an email. Guo told Fortune she had received $750 million as a result of the Meta deal, but did not comment on the current state of her relationship with Wang. In recent years, Scale has run into concerns about its labor practices with the estimated tens of thousands of contractors it employs around the world to manually label data and review images. A Department of Labor investigation was opened in 2024 and closed in May around the company's adherence to the Fair Labor Standards Act, particularly around fair pay and worker classification. There are currently two labor lawsuits against Scale that are ongoing. Glenn Danas, partner at Clarkson Law Firm—which brought those cases against the company—estimates the company's contractor workforce could be roughly 60,000 people. One thing that makes the Meta/Scale deal notable is that Wang had always emphasized that he is not a researcher, and that Scale AI was not building AI models. Instead, it wanted to provide the entire generative AI ecosystem with high-quality data to train its models. 'We're not out there developing a leading large language model,' Wang told Fortune last year. 'But we do serve the entire ecosystem. Nearly every major large language model is built on top of our data foundry.' Scale was also agnostic when it came to working with customers. It did some of the earliest experiments on reinforcement learning human feedback (RLHF), with OpenAI in 2019, with the team that later became Anthropic. Scale continued collaborating with both AI startups. 'We're both neutral across the entire ecosystem and we're able to have very strong relations with every relevant company in the AI ecosystem across the stack,' Wang said. Scale has said it will continue to operate as an independent company (with Wang as chairman), serving other customers. Under the terms of the deal, Meta will spend a minimum of $500 million a year for Scale data over the next five years, according to the source familiar with the negotiations. But the perception of neutrality now looks all but dead. Following the announcement, news reports said that both Google and OpenAI were planning to end their relationship with Scale. Some industry observers speculate that Meta's real strategy all along was a data landgrab, a move to secure a major source of one of the vital components for building AI models—and to deprive others of it. But several industry insiders that Fortune spoke to were skeptical of the theory. Ryan Kolln, CEO of Scale AI competitor Appen, said he doubts the Meta/Scale deal is about consolidating data vendors or starving competitors seeking data. There's a risk in doing that because there's a strong benefit to having diversity in data, with vendors that have different specialties and expertise, he said. So what is the key thing Zuckerberg is getting for his $14.3 billion? Several sources close to the companies said they'd heard that Wang is being considered as a potential leader for Meta's entire AI organization—a far more powerful remit than the 'superintelligence' team, which some news reports have said will consist of 50 people and be helmed by Wang. Erik Meijer, a former engineering leader at Meta, said he would not be surprised by a move to make Wang a 'chief AI officer' of all of Meta. 'Heaping everything into a single org makes sense,' he said. 'In fact I would be surprised if Mark [Zuckerberg] would make such a big investment and then not do a full on reorg putting all AI efforts in one place.' Meta's sprawling AI kingdom includes the product-focused generative AI team, the AGI Foundations unit focused on further Llama development, as well as FAIR (Fundamental AI Research), which was founded by Yann LeCun, the AI 'godfather' who remains Meta's chief scientist. There is also a standalone Business AI product team helmed by former Salesforce AI head Clara Shih. That said, Wang would be an unorthodox choice for the top role, given that he is not a computer scientist. Whether the PhDs and other AI researchers working at Meta would accept Wang as their leader is hardly a sure thing. 'Nope,' one current Meta AI researcher said flatly. Wang is a businessman without a strong record working with AI models, the Meta researcher said. A former Meta AI researcher who worked in Meta's FAIR group concurred: 'He is not going to be accepted easily.' It's a warning Zuckerberg may well heed, given that Meta's research lab is already bleeding talent—11 of the original 14 Llama authors have left to join competitors. On the other hand, Wang's defenders point out that while not a computer scientist, he is more than capable of getting in the AI weeds. 'We forget sometimes he is a very technically-capable guy,' said the former Scale manager. 'He's not just a salesperson. He's incredible at it, but he's not just a salesperson.' With more than $164 billion in annual revenue, Meta knows all too well that it takes something truly unique to move the needle in its business. And in paying up for Wang and Scale, Zuckerberg is likely betting that the real value transcends any simple categories. One source close to Wang told Fortune that the surprise and confusion stems from the fact that Wang does not fit into the typical tech world archetypes. 'Silicon Valley is good at putting people into boxes, they like to say, this person is a technical person, this person is a business person,' he said. 'Alex is truly a man of one.' And that kind of asset is especially valuable as the landscape changes. If AI proves as game-changing as some expect, it will be far more consequential than any of the previous platform shifts that have rocked the tech industry, bringing hard-to-predict risks and opportunities. The advent of AI is already thawing the massive military contract market that Silicon Valley companies were once largely frozen out of, with Meta forging ties with drone-maker Anduril while rivals like OpenAI clinch Pentagon deals of their own. 'My very, very deep sense is that Meta is actively exploring, maybe even advanced in terms of actively exploring, wanting to play a significant leadership role in the national security of the United States,' said one former Department of Defense official. As Wang demonstrated at the Utah confab in 2023, and in Scale's full-page Washington Post ad in January ('Dear President Trump, America must win the AI War'), Meta's new investment is well-positioned on that front. It's also not gone unnoticed that Michael Kratsios, the Trump Administration's director of the Office of Science and Technology Policy and the Science Advisor to the President, worked at Scale in between the two Trump terms. 'He's been very smart and extremely clever,' the former DoD official said about Wang. 'He seemed to lean into something that a lot of people have a little bit of a revulsion against, which is, engage in the politics of Washington. I think Alex, whether it's by generation or personality, or whatever, he really got that that needed to happen – he seemed to have a real sixth sense.' But while Meta could use all the help it can get in D.C. ('No one loves to hate anyone more than Washington loves to hate Zuckerberg; it's kind of a pastime for some people,' a beltway insider said), Wang will ultimately need to deliver more than the services of a lobbyist for the partnership with Zuckerberg to be successful. To Wang's fans, including one source who has known Wang for about a decade, his versatility will help Meta open doors and navigate whatever challenges lie ahead: 'He's better at the point of contact on any problem more than anyone I've ever seen.' Zoom out for a longer term view, and this person echoed a common sentiment, that Wang is just getting started. 'He may start another company, or maybe a bunch of companies,' the person said. 'Over time, he has a chance of being a main character in Silicon Valley at the highest levels for the next 30 years.' This story was originally featured on
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A successful approach includes real time, AI power decisioning, with AI driven models that continuously learn and adapt to new fraud patterns. 'It is a critical moment for a shift from a very reactive risk management approach to something much more intelligence driven, proactive and dynamic so that that credit risk is managed dynamically,' says Hamilton. Fraud and credit risk are often managed in separate silos, says Hamilton. The result is inefficiencies and missed insights. A unified decisioning approach enables better risk assessment, faster response times and enhanced customer experiences. Accordingly, financial institutions need to invest in unified decisioning platforms to eliminate silos, reduce inefficiencies and improve risk assessment accuracy. While financial service providers increasingly recognise that AI can enhance credit risk assessments, strengthen fraud detection and improve operational efficiency, that is only part of the equation. It is true that AI adoption is accelerating but poor data integration remains a significant barrier. The financial institutions that embrace this transformation will be better positioned to mitigate risks, drive growth and deliver superior customer experiences. The extent of the challenge facing the sector was highlighted by a global survey conducted by Provenir earlier this year. Key decision makers at financial services providers globally were surveyed to understand their risk decisioning and fraud challenges across the customer lifecycle, decisioning investment priorities, and AI opportunities. It revealed that nearly half of all financial services executives are struggling with managing credit risk and detecting and preventing fraud. The survey also disclosed that many are revamping their credit risk decisioning and fraud prevention strategies in 2025, with AI playing a prominent role. Nearly 60% say they find it difficult to deploy and maintain risk decisioning models. 55% of executives recognise the value of AI to make streamlined strategy decisions, and in its ability to provide AI-powered performance improvement recommendations. 37% say they struggle with effective data orchestration for application fraud prevention, specifically in not being able to easily ingest and integrate new data sources. 36% are challenged in using AI and machine learning for fraud prevention. Key priorities for customer and account management are real-time, event-driven decisioning (65%), eliminating friction across the customer lifecycle (44%), and increasing customer lifetime value (44%). Over half of respondents agree the biggest data challenge they face is being able to easily integrate data sources into decisioning processes. 'I would say that investment is definitely happening, and there are many more projects that are trying to get off the ground and start as well. It is the execution, though that remains the challenge. So we are seeing the investment, but I feel that AI is still going through a transition of organisations figuring that how they can adopt it in their business and make it effective.' Hamilton suggests that organisations should consider starting small and scaling smartly to mitigate risk and ensure measurable impact. That would mean starting with AI projects that offer a quick return on investment, such as credit scoring and automated customer decisioning, or maybe slightly less regulated areas such as fraud detection. A phased approach, focused on early wins, will build confidence in AI driven strategies while demonstrating tangible business values. 'US and Canadian banks are leading the charge in AI adoption, with nearly two thirds of them investing in AI and embedded intelligence now, higher than any other region. So that's a really positive sign, but integration does remain a challenge for the North American banks. 'Compliance and security concerns we do see higher in EMEA than other regions, with many of them calling that out as a barrier to AI adoption. The challenge for European banks is that while they're data rich they often struggle to orchestrate that together, to unlock the power of it. 'It is a critical moment for companies to act but I do think that it's a very positive sign that there's so much energy going into getting these projects off the ground to unify the decisioning, bring in AI and optimise data integration. 'The final point is that the discussion is often based on the premise of reducing risk and stopping the bad but we haven't really talked half as much as we could around the power to actually unlock new opportunities for innovation and growth as well for these organisations. 'Because if you really understand who you're doing business with and the threat or risk that they pose, you will find that where that's a small threat and a small risk, they could be a fantastic customer for you, that you want to put that time and energy into engaging with in the right way to drive value for them and your business.' That then is the challenge and the huge potential prize. AI enabling proactive engagement and tailored offers that drive loyalty and maximise customer value with AI powered decisioning models ensuring a more customer centric approach which can adapt dynamically to customer behaviour in real time. Eliminating unnecessary friction while maintaining strong risk controls is easy to summarise-harder to execute. Banks that can deliver smarter, faster and more customer centric experiences with AI and real time data and insights and leverage hyper personalisation to increase engagement and lifetime value, will be the winners. "Provenir's Carol Hamilton on credit risk decisioning, fraud prevention and reward" was originally created and published by Retail Banker International, a GlobalData owned brand. The information on this site has been included in good faith for general informational purposes only. It is not intended to amount to advice on which you should rely, and we give no representation, warranty or guarantee, whether express or implied as to its accuracy or completeness. You must obtain professional or specialist advice before taking, or refraining from, any action on the basis of the content on our site. Error in retrieving data Sign in to access your portfolio Error in retrieving data Error in retrieving data Error in retrieving data Error in retrieving data
