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AWS launches GenAI Loft Tour to accelerate AI innovation in startup and developer communities worldwide
Jul 11, 2024Tap to summarizeStartups have always been the lifeblood of innovation. They are the frontrunners in new technologies adoption, and when it comes to generative artificial intelligence (AI), they are poised to transform industries and shape the future. That’s why we recently announced a commitment of $230 million to accelerate the creation of generative AI applications by startups around the world as well as the second-annual AWS Generative AI Accelerator. With these efforts, we are doubling down on our commitment to support startup founders to innovate faster and reinvent customer experiences and applications with generative AI. Building on this news, today we are announcing the AWS GenAI Lofts, a global tour of pop-up collaborative spaces and immersive experiences for startups and developers that will take residence in innovation and AI hubs around the globe. This initiative also represents our commitment to make it easy for developers of all skill levels to build and scale generative AI applications. Similar to the AWS Startup Lofts that started in 2014, the AWS GenAI Lofts provide a one-stop destination for in-person engagement for startups and developers to learn how to use and implement generative AI technology, get up to speed on the latest trends, and connect with a wider community of technology and business experts. With AWS GenAI Lofts, startups, developers, and AI enthusiasts can get hands-on AI products and services from AWS Partners and AWS, including Amazon Bedrock and Amazon Q. Developers will have the opportunity to gain greater understanding of advanced techniques, such as building agentic workflows and tuning foundation models, and dig deeper into generative AI use cases and demos. Visitors can experience exclusive sessions led by industry luminaries, make connections with generative AI investors and leaders, and get their questions answered in-person by generative AI experts. Pop-ups will open for up to 12 weeks in global innovation hubs, including: AWS GenAI Loft | Bengaluru: July 29, 2024 AWS GenAI Loft | San Francisco: August 12, 2024 AWS GenAI Loft | São Paulo: September 2, 2024 AWS GenAI Loft | London: September 30, 2024 AWS GenAI Loft | Paris: October 8, 2024 Visitors will benefit from immersive experiences showcasing cutting-edge generative AI projects, workshops, fireside chats, and hands-on programming from AI experts and AWS partners Anthropic, Cerebral Valley, and Weights & Biases, among others. Each city will host a roster of esteemed AI experts and thought leaders, including machine learning data scientists. These sessions will provide access to some of the brightest minds in the field with daily events. Visitors can also leverage the Ask an Expert Bar to get their questions answered by AWS Solutions Architects. While each pop-up GenAI Loft is open, startups and developers can take advantage of programs, workshops, and tools, including: Experiential Series This series highlights not just luminary speakers, but incorporates seeing, feeling, and even touching generative AI through real-life applications. Experiences include seeing immersive robotics art-making in motion, spotlighting cultural influencers who are using generative AI to enrich experiences. These “Artist-in-Residence” sessions are interactive and experiential, bringing generative AI technology to life. Bootcamps Bootcamps are immersive three-day programs designed to equip founders and developers with new skills, frameworks, and resources to propel their business forward. The programs are tailored to the needs of both business and technical founders, providing a combination of hands-on workshops and interactive sessions. Immersion Days AWS Solution-Focused Immersion Days are a series of events designed to provide startups employees and developers with hands-on experience using generative AI services and discover efficient methodologies to help problem-solve through generative AI. Startup Talks Startup Talks provide a comprehensive learning experience for startup founders, covering technical, business, and personal aspects of the entrepreneurial journey, while also incorporating the perspectives of investors, industry partners, and startup experts. These talks will feature a mix of content led by leaders and founders from some of the top startups in the world. To find out when the AWS GenAI Loft tour is coming to a city near you, get more details on programming, and register, visit aws.amazon.com/startups/lp/aws-gen-ai-lofts.
Transcribe →Accelerating the next wave of generative AI startups
Jun 13, 2024Tap to summarizeSince day one, AWS has helped startups bring their ideas to life by democratizing access to the technology powering some of the largest enterprises around the world including Amazon. Each year since 2020, we have provided startups nearly $1 billion in AWS Promotional Credits. It’s no coincidence then that 80% of the world’s unicorns use AWS. I am lucky to have had a front row seat to the development of so many of these startups over my time at AWS—companies like Netflix, Wiz, and Airtasker. And I’m enthusiastic about the rapid pace at which startups are adopting generative artificial intelligence (AI) and how this technology is creating an entirely new generation of startups. These generative AI startups have the ability to transform industries and shape the future, which is why today we announced a commitment of $230 million to accelerate the creation of generative AI applications by startups around the world. We are excited to collaborate with visionary startups, nurture their growth, and unlock new possibilities. In addition to this monetary investment, today we’re also announcing the second-annual AWS Generative AI Accelerator in partnership with NVIDIA. This global 10-week hybrid program is designed to propel the next wave of generative AI startups. This year, we’re expanding the program 4x to serve 80 startups globally. Selected participants will each receive up to $1 million in AWS Promotional Credits to fuel their development and scaling needs. The program also provides go-to-market support as well as business and technical mentorship. Participants will tap into a network that includes domain experts from AWS as well as key AWS partners such as NVIDIA, Meta, Mistral AI, and venture capital firms investing in generative AI. Building in the cloud with generative AI In addition to these programs, AWS is committed to making it possible for startups of all sizes and developers of all skill levels to build and scale generative AI applications with the most comprehensive set of capabilities across the three layers of the generative AI stack. At the bottom layer of the stack, we provide infrastructure to train large language models (LLMs) and foundation models (FMs) and produce inferences or predictions. This includes the best NVIDIA GPUs and GPU-optimized software, custom, machine learning (ML) chips including AWS Trainium and AWS Inferentia, as well as Amazon SageMaker, which greatly simplifies the ML development process. In the middle layer, Amazon Bedrock makes it easier for startups to build secure, customized, and responsible generative AI applications using LLMs and other FMs from leading AI companies. And at the top layer of the stack, we have Amazon Q, the most capable generative AI-powered assistant for accelerating software development and leveraging companies’ internal data. Customers are innovating using technologies across the stack. For instance, during my time at the VivaTech conference in Paris last month, I sat down Michael Chen, VP of Strategic Alliances at PolyAI, which offers customized voice AI solutions for enterprises. PolyAI develops natural-sounding text-to-speech models using Amazon SageMaker. And they build on Amazon Bedrock to ensure responsible and ethical AI practices. They use Amazon Connect to integrate their voice AI into customer service operations. At the bottom layer of the stack, NinjaTech uses Trainium and Inferentia2 chips, along with Amazon SageMaker, to build, train, and scale custom AI agents. From conducting research to scheduling meetings, these AI agents save time and money for NinjaTech’s users by bringing the power of generative AI into their everyday workflows. I recently sat down with Sam Naghshineh, Co-founder and CTO, to discuss how this approach enables them to save time and resources for their users. Leonardo.AI, a startup from the 2023 AWS Generative AI Accelerator cohort, is also harnessing the capabilities of AWS Inferentia2 to enable artists and professionals to produce high-quality visual assets with unmatched speed and consistency. By reducing their inference costs without sacrificing performance, Leonardo.AI can offer their most advanced generative AI features at a more accessible price point. Leading generative AI startups, including Perplexity, Hugging Face, AI21 Labs, Articul8, Luma AI, Hippocratic AI, Recursal AI, and DatologyAI are building, training, and deploying their models on Amazon SageMaker. For instance, Hugging Face used Amazon SageMaker HyperPod, a feature that accelerates training by up to 40%, to create new open-source FMs. The automated job recovery feature helps minimize disruptions during the FM training process, saving them hundreds of hours of training time a year. At the middle layer, Perplexity leverages Amazon Bedrock with Anthropic Claude 3 to build their AI-powered search engine. Bedrock ensures robust data protection, ethical alignment through content filtering, and scalable deployment of Claude 3. While Nexxiot, an innovator in transportation and supply chain solutions, quickly moved its Scope AI assistant solution to Amazon Bedrock with Anthropic Claude in order to give their customers the best real-time, conversational insights into their transport assets. At the top layer, Amazon Q Developer helps developers at startups build, test, and deploy applications faster and more efficiently, allowing them to focus their valuable energy on driving innovation. Ancileo, an insurance SaaS provider for insurers, re-insurers, brokers, and affinity partners, uses Amazon Q Developer to reduce the time to resolve coding-related issues by 30%, and is integrating ticketing and documentation with Amazon Q to speed up onboarding and allow anyone in the company to quickly find their answers. Amazon Q Business enables everyone at a startup to be more data-driven and make better, faster decisions using the organization’s collective knowledge. Brightcove, a leading provider of cloud video services, deployed Amazon Q Business to streamline their customer support workflow, allowing the team to expedite responses, provide more personalized service, and ultimately enhance the customer experience. Resources for generative AI startups The future of generative AI belongs to those who act now. The application window for the AWS Generative AI Accelerator program is open from June 13 to July 19, 2024, and we’ll be selecting a global cohort of the most promising generative AI startups. Don’t miss this unique chance to redefine what’s possible with generative AI, and <a href="https://aws.amazon.com/startups/accelerators/generative-ai...
Transcribe →AWS and CrowdStrike announce the winner of the AWS & CrowdStrike Cybersecurity Accelerator
May 14, 2024Tap to summarizeLeft to Right: CJ Moses, CISO, Amazon, Mike Sentonas, President, CrowdStrike, Galina Antova, Co-founder, Claroty, Tal Kollender, Co-founder & CEO, Gytpol, Ilan Leiferman, Head BD Cybersecurity, AWS, Daniel Bernard, CBO, CrowdStrike, Dona Haj, VCs & Startups BD, AWS In a groundbreaking initiative aimed at nurturing the next wave of cybersecurity disruptors, AWS and CrowdStrike announced security remediation platform GYTPOL as the winner’s of the 2024 AWS and CrowdStrike Cybersecurity Startup Accelerator. The top prize was awarded for their innovative technology, which specializes in continuous detection and automated remediation of device misconfigurations, ensuring zero impact on business continuity. The winner was announced after competing against 8 other startups at the final stages of the program. Handpicked from a pool of hundreds of applicants, 23 startups embarked on an exciting journey through the AWS and Crowdstrike Cybersecurity Accelerator, a 10-week, equity-free program that provided unparalleled access to industry experts, masterclasses, global investors, and up to $25,000 in AWS Activate Credits, empowering these startups to scale and innovate in the cybersecurity realm. Participating in the AWS and Crowdstrike Accelerator Program has truly been a game-changer for GYTPOL. The invaluable content and strategy sessions have significantly boosted our company’s growth. What’s been most rewarding on a personal level is the exceptional mentorship and the opportunity to connect with industry leaders I’ve long admired. It’s surreal to witness their genuine interest and eagerness to help. Equally exciting is meeting the rising stars, individuals whose potential is palpable. This program has a knack for cultivating talent and fostering a supportive community. I feel privileged to be part of this cohort, surrounded by such incredible tech and talent, in a program that’s truly one-of-a-kind. Tal Kollender, CEO of Gytpol The winner was unveiled at an exclusive startup showcase at the San Francisco Mint during RSA Conference 2024, attended by cybersecurity executives, technology buyers, CISOs, and investors. Mike Sentonas, CrowdStrike President, CJ Moses, AWS Chief Information Security Officer, and entrepreneur and investor, Galina Antova. “CrowdStrike took cybersecurity to the cloud by building on AWS – together we’ve propelled how companies build and secure their businesses in the cloud, from code to runtime,” shared Daniel Bernard, Chief Business Officer at CrowdStrike and showcase moderator. “Partnering with AWS to foster the next-generation of cybersecurity innovators underscores our commitment to driving industry transformation.” “In the midst of every crisis there may be an opportunity. But in technology, the reverse is often true, as well: every opportunity also creates a crisis. AI is undoubtedly a generational opportunity—a force multiplier for productivity that has quickly become essential for teams to compete. But it’s also a force multiplier for malicious actors and represents a new cybersecurity attack vector,” said Bogomil Balkansky, partner at Sequoia Capital. “Our portfolio company, Apex, helps teams resolve the tension between leveraging transformative AI innovations and ensuring robust protection against risks. We are proud to see Apex recognized as a finalist in the AWS & CrowdStrike accelerator.” “Congratulations to GYTPOL for their outstanding achievement in winning the AWS and CrowdStrike Cybersecurity Startup Accelerator. Their innovative approach to continuous detection and automated remediation of device misconfigurations is truly groundbreaking. We are thrilled to see them thrive and look forward to supporting their continued success as they make waves in the cybersecurity industry,” said Kellen O’Connor, EMEA Managing Director for Startups at AWS. Meet the Finalists of the 2024 Cohort: Aim Security Aim Security’s mission is to empower security teams to allow enterprise adoption of generative AI technologies securely and safely. Aim builds the first generative AI security platform and provides protection, risk management and governance for all Large Language Model (LLM) risks. Miggo Miggo is the world’s first Application Detection and Response platform. Uniquely integrating real-time identity-aware application tracing, proactive threat hunting, and anomaly detection at the business logic layer, Miggo reduces risk, ensures compliance, and protects even insecure production applications and APIs. Oligo Security Oligo Security is on a mission to proactively protect software throughout the development lifecycle. With contextual detection of exploitable flaws in all application code, the Oligo platform helps organizations focus on risks that matter – improving transparency and trust for security and engineering teams. Let your developers focus on building features, not fixes. Opus Security Opus Security brings together vulnerabilities from Cloud, Application, and other attack surfaces, consolidating organizational security postures. The platform automates prioritization, streamlines remediation, and promotes collaboration between security, engineering, and IT. With Opus, organizations benefit from an efficient cross-organizational risk reduction process that works. Apono Apono is a cloud privileged access management platform providing visibility and control to privileged permissions. Apono helps companies reduce over-privileges, automate manual access provisioning, and gain control of access with “Just In Time” and “Just Enough” dynamic provisioning. Mindflow Mindflow is an AI-driven automation platform designed for SecOps that empowers enterprise teams to operate at a new level of performance by intuitively automating repetitive, mundane tasks and seamlessly orchestrating all their tools. onum With the market’s largest library of integrated services and revolutionary generative AI automation and onum, empowering organizations with tailored cybersecurity strategies. Find more information on this year’s AWS & CrowdStrike Cybersecurity Accelerator and discover other accelerator program opportunities available to startups by visiting Startups.AWS. <img loading="lazy" class="alignleft wp-image-18428 size-thumbnail" src="https://d2908q01vomqb2.cloudfront.net/cb4e5208b4cd87268b208e49452ed6e89a68e0b8/2024/05/1...
Transcribe →Boosted.ai’s generative AI portfolio manager surfaces near-instant finance insights with AWS
May 7, 2024Tap to summarizeBy switching from a generic LLM that was too expansive and cumbersome for their needs to a model tailored to their domain (capital markets), Boosted.ai reduced costs by 90 percent, vastly improved efficiency, and unlocked the GPU capacity needed to scale their generative AI investment management application. Summary In 2020, Boosted.ai expanded their artificial intelligence (AI)-powered financial analysis platform—Boosted Insights—by building an AI portfolio assistant for asset managers on a large language model (LLM) that processed data from 150,000 sources. The output was macro insights and market trend analysis on over 60,000 stocks across every global equity market (North America, EU & UK, APAC, Middle East, Latin America, and India). But using an LLM came with some significant drawbacks—a high annual cost to operate and GPU capacity limitations that limited their ability to scale. Boosted.ai began domain-optimizing a model running on AWS and: reduced costs by 90 percent without sacrificing quality moved from overnight to near real-time updates, unlocking more value for their investment manager clients acting on hundreds of thousands of data sources improved security and personalization with the ability to run a model in a customer’s private cloud, rather than running workloads through an LLM cloud Introduction 2023 was the year generative AI went mainstream. Enhancing efficiency to do more with less will continue to be on corporate agendas throughout 2024 and beyond. It is critical for teams to have a strategy for how they will incorporate generative AI to create productivity gains. However, even when there’s a clear use case, it’s not always apparent how to implement generative AI in a way that makes sense for a business’s bottom line. Here’s how Boosted.ai incorporated generative AI to automate research tasks for their investment management clients in a way that improved outcomes for both Boosted.ai and their customers. Founded in 2017, Boosted.ai offers an AI and machine learning (ML) platform—Boosted Insights—to help asset managers sort through data to enhance their efficiency, improve their portfolio metrics, and make better, data-driven decisions. When the founders saw the impact of powerful LLMs, they decided to use a closed-source LLM to build an AI-powered portfolio management assistant. Overnight, it would process millions of documents from 150,000 sources, including nontraditional datasets like SEC filings such as 10Ks and 10Qs, earnings calls, trade publications, international news, local news, even fashion. After all, if you’re talking about a company like Shein going public, a Vogue article could become relevant investing information. Boosted Insights summarized and collated all this information into an interactive user interface that their asset manager clients could sort through themselves. With their new generative AI model, Boosted.ai was now pushing critical investment information to all their clients, over 180 of the world’s biggest asset managers. For these teams, time is money. When something impacts a company’s stock price, how fast someone gets and acts on that information can be the difference of thousands, even millions of dollars. Boosted.ai gave these managers an edge. For instance, it flagged that Apple was moving some of its manufacturing capabilities into India before news broke in mainstream media outlets, because Boosted Insights was reading articles in Indian media. Adding a generative AI component to Boosted Insights automated a lot of the research to turn an investing hypothesis into an actual trade. For instance, if an investor was concerned about a trade war with China, they could ask Boosted Insights: “What are the kinds of stocks I should buy or sell?” Before generative AI, answering that question was a 40-hour research process, sifting through hundreds of pages of analyst reports, news articles, and earnings summaries. With an AI-powered portfolio management assistant, 80 percent of that work was now automated. Figure 1. Boosted Insights maps the performance of stocks with exposure to generative AI Solving for scale with domain-specific language models Boosted.ai’s generative AI rollout was extremely well received by clients, but the company wanted to scale it to run up to 5x or 10x more analysis and get from overnight reports to a true real-time system. But there was a problem: running the AI cost nearly $1 million a year in fees, and even if they wanted to buy more GPU capacity, they simply couldn’t. There just wasn’t enough GPU capacity for their AI financial analysis tool to scale into a real-time application. Right-sizing the model for lower costs and greater scale Boosted.ai’s challenges are increasingly common ones for organizations adopting LLMs and generative AI. Since LLMs are trained for general purpose use, the companies that train these models spend a lot of time, testing, and money to get them to work. The larger the model, the more accelerated compute it has to use on every request. As a result, for most organizations, including Boosted.ai, it is just not viable to use an LLM for a specific task. Boosted.ai decided to explore a more targeted and cost-effective approach: fine-tuning a smaller language model to perform a specific task. In the AI/ML world, these models are often referred to as “open source,” but that doesn’t mean they are hacked together by random people sharing a wiki, as you might imagine from the early days of open-source coding. Instead, open-source language models, like Meta’s Llama 2, are trained on trillions of data points and maintained in secure environments like Amazon Bedrock. The difference is an open-source model gives users total access to its parameters and the option to fine-tune them for specific tasks. Closed-source LLMs, by contrast, are a black box that don’t allow for the kind of customization Boosted.ai needed to create. The ability to fine-tune their model would prove to make a difference for Boosted.ai. Through the AWS Partner Network, Boosted.ai connected with Invisible, whose global network of AI training specialists allowed Boosted.ai to stay focused on their core developmental work while Invisible provided high-quality data annotation faster and more cost effectively than staffing an in-house team to the project. Together, AWS, Invisible, and Boosted.ai found and implemented the smallest possible model that could handle their use case, benchmarking against the industry-standard Massive Multitask Language Understanding (MMLU) dataset to evaluate performance. Our goal was to have the smallest possible model with the highest possible IQ for our tasks. We went into the MMLU and looked at subtasks we thought were highly relevant to what Boosted.ai is doing: microeconomics and macroeconomics, math, and a few others. We grabbed the smallest model we thought would work and tuned it to be the best it could be for our tasks. If that didn’t work, we moved to the next size model and the next level of intelligence. — Joshua Pantony, Boosted.ai co-founder and CEO With a more compact and efficient model that performed just as well at financial analysis, Boosted.ai slashed costs by 90 percent. The big benefit they saw from this efficiency was being able to massively upsize the amount of data they pulled—going from overnight updates to near real-time. More importantly, they got the GPUs they needed to scale. Where Boosted.ai once needed A100 and H100 to run their models, this more efficient domain-specific generative AI allowed them to run a layer on smaller and more readily available hardware. <p id="caption-attachment-18385" class="wp-caption...
Transcribe →How Patronus AI helps enterprises boost their confidence in generative AI
May 2, 2024Tap to summarizeOver the past few years, and especially since the launch of ChatGPT in 2022, the transformational potential of generative artificial intelligence (AI) has become undeniable for organizations of all sizes and across a wide range of industries. The next wave of adoption has already begun, with companies rushing to adopt generative AI tools in order to drive efficiency and enhance customer experiences. A 2023 McKinsey report estimated that generative AI could add the equivalent of $2.6 trillion to $4.4 trillion in value to the global economy annually, boosting AI’s overall economic impact by some 15-40 percent, while IBM’s latest CEO survey found that 50 percent of respondents were already integrating generative AI into their products and services. As generative AI goes mainstream, however, customers and businesses are increasingly expressing concern about its trustworthiness and reliability. And it can be unclear why given inputs lead to certain outputs, making it difficult for companies to evaluate the results of their generative AI. Patronus AI, a company founded by machine learning (ML) experts Anand Kannappan and Rebecca Qian, has set out to tackle this problem. With its AI-driven automated evaluation and security platform, Patronus helps its customers use large language models (LLMs) confidently and responsibly while minimizing the risk of errors. The startup’s aim is to make AI models more trustworthy and more usable. “That’s become the big question in the past year. Every enterprise wants to use language models, but they’re concerned about the risks and even just the reliability of how they work, especially for their very specific use cases,” explains Anand. “Our mission is to boost enterprise confidence in generative AI.” Reaping the benefits and managing the risks of generative AI Generative AI is a type of AI that uses ML to generate new data similar to the data it was trained on. By learning the patterns and structure of the input datasets, generative AI produces original content—images, text, and even lines of code. Generative AI applications are powered by ML models that have been pre-trained on vast amounts of data, most notably LLMs trained on trillions of words across a range of natural language tasks. The potential business benefits are sky-high. Firms have shown interest in using LLMs to leverage their own internal data through retrieval, to produce memos and presentations, to improve automated chat assistance, and to auto-complete code generation in software development. Anand also points to the whole range of other use cases that have not yet been realized. “There’s a lot of different industries that generative AI hasn’t disrupted yet. We’re really just at the early innings of everything that we’re seeing so far.” As organizations consider expanding their use of generative AI, the issue of trustworthiness becomes more pressing. Users want to ensure their outputs comply with company regulations and policies while avoiding unsafe or illegal outcomes. “For larger companies and enterprises, especially in regulated industries,” explains Anand, “there are a lot of mission-critical scenarios where they want to use generative AI, but they’re concerned that if a mistake happens, it puts their reputation at risk, or even their own customers at risk.” Patronus helps customers manage these risks and boost confidence in generative AI by improving the ability to measure, analyze, and experiment with the performance of the models in question. “It’s really about making sure that, regardless of the way that your system was developed, the overall testing and evaluation of everything is very robust and standardized,” says Anand. “And that’s really what’s missing right now: everyone wants to use language models, but there’s no really established or standardized framework of how to properly test them in a much more scientific way.” Enhancing trustworthiness and performance The automated Patronus platform allows customers to evaluate and compare the performance of different LLMs in real-world scenarios, thereby reducing the risk of undesired outputs. Patronus uses novel ML techniques to help customers automatically generate adversarial test suites and score and benchmark language model performance based on Patronus’s proprietary taxonomy of criteria. For example, the FinanceBench dataset is the industry’s first benchmark for LLM performance on financial questions. “Everything we do at Patronus is very focused around helping companies be able to catch language model mistakes in a much more scalable and automated way,” says Anand. Many large companies are currently spending vast amounts on internal quality assurance teams and external consultants, who manually create test cases and grade their LLM outputs in spreadsheets, but Patronus’s AI-driven approach saves the need for such a slow and expensive process. “Natural Language Processing (NLP) is quite empirical, so there is a lot of experimentation work that we are doing to ultimately figure out which evaluation techniques work the best,” explains Anand. “How can we enable those kinds of things in our product so that people can leverage the value … from the techniques that we figured out work the best, very easily and quickly? And how can they get performance improvements, not only for their own system, but even for the evaluation against that system that they’ve been able to do now because of Patronus?” What results is a virtuous cycle: the more a company uses the product and gives feedback via the thumbs or thumbs down feature, the better its evaluations become, and the better the company’s own systems become as a result. Boosting confidence through improved results and understandability To unlock the potential of generative AI, improving its reliability and trustworthiness is vital. Potential adopters across a variety of industries and use cases are regularly held back—not just by the fact that mistakes are sometimes made by AI applications—but also by the difficulty of understanding how or why a problem has occurred, and how to avoid that happening in the future. “What everyone is really asking for is a better way to have a lot more confidence in something when you roll it out to production,” says Anand. “And when you put it in front of your own employees, and even end customers, then that’s hundreds, thousands, or tens of thousands of people, so you want to make sure that those kinds of challenges are limited as much as possible. And, for the ones that do happen, you want to know when they happen and why.” One of Patronus’ key goals is enhancing the understandability, or explainability, of generative AI models. This refers to the ability to pinpoint why certain outputs from LLMs are the way they are, and how customers can gain more control over those outputs’ reliability. Patronus incorporates features aimed at explainability, primarily by giving customers direct insight into why a particular test case passed or failed. Per Anand: “That’s something that we do with natural language explanations, and our customers have told us that they liked that, because it gives them some quick insight into what might have been the reason why things have failed—and maybe even suggestions for improvements on how they can iterate on the prompt or generation parameter values, or even for fine-tuning … Our explainability is very focused around the actual evaluation itself.” Looking toward the future of generative AI with AWS To build their cloud-based application, Patronus has worked with AWS since the beginning. Patronus uses a range of different cloud-based services; Amazon Simple Queue Service (Amazon SQS) for queue infrastructure and Amazon Elastic Compute Cloud (Amazon EC2) for Kubernetes environments, they take advantage of the customization and flexibility available from Amazon Elastic Kubernetes Service (Amazon EKS). Having worked with AWS for many years before he helped found Patronus, Anand and his team were able to leverage their familiarity and experience with AWS to quick...
Transcribe →Technology that teaches empathy? How mpathic uses AI to help us listen to each other
May 1, 2024Tap to summarizeOn a basic human level, we want to be heard. We want to connect with others, and we want to be understood. Unfortunately, we’re often faced with many things competing for our attention, which makes us bad listeners. Active listening is a learned behavior and not easy to master. But what if artificial intelligence (AI) could augment our ability to really listen and truly relate to others? What if technology could draw upon our collective lived experiences and help us be more human to each other? These are the questions Dr. Grin Lord, clinical psychologist and founder of conversation analytics company mpathic, has spent the last 15 years chasing. During her research, Grin and the team at mpathic have identified trust-building words, phrases, and communication behaviors and modeled them using AI. “We look at what is promoting trust, what is promoting engagement, and how those impact outcomes,” explains mpathic’s Chief Innovation Officer, Dr. Danielle Schlosser. In pursuit of a technology-driven approach to unlock empathy, mpathic developed something unique: a solution that not only analyzes and assesses the health of conversations but also provides recommendations for increasing their levels of empathy, trust, and engagement in real-time. “Our differentiator is trying to be more behavioral and actionable,” says Grin. “We want to coach people on how to improve.” Drawing on responses from a diverse range of experts with extensive empathy training, mpathic’s API quickly tags instances of misunderstanding within ongoing conversations and immediately offers feedback and suggestions on how to listen and respond with more empathy. The results have been astonishing. When deployed in clinical trials, healthcare providers using mpathic’s API have been seven times more likely to capture participant risk and provide critical feedback. Similarly, in sales and HR software as a service (SaaS) use cases, businesses using mpathic products witnessed more customer engagement, satisfaction, and other outcomes. Iterating on empathy education Taking context and nuance into consideration, mpathic defines empathy as “accurate understanding.” But designing a successful method for teaching empathy turned out to be much more elusive than defining it. In the early 2000s, Grin began her journey as part of a research study working with drivers involved in drunk driving accidents. The experiment consisted of brief interventions, including 15 minutes of empathic listening, showing acceptance and understanding of the driver’s experience. This brief empathic intervention led to reductions in drinking that held over three years later and a 46 percent reduction in readmissions to the hospital. After that, Grin trained medical professionals on how to listen with empathy, teaching behaviors such as reflective listening, asking open-ended questions instead of closed-ended ones, and using affirmations. When she found that a two-day workshop was not enough time to change deep-seated behaviors and styles of communication, she retooled her approach. Grin learned techniques from a nationwide phone coaching study where doctors would record themselves giving feedback. A psychologist would listen and provide doctors with performance-based suggestions on how to improve. This process could take weeks, so in 2008 she seized an opportunity to use machine learning (ML) to speed up the process. At the University of Washington, Grin was a part of the team that built the first speech signal processing pipelines for performance-based feedback in a medical settings. “With the computing power at the time, it took about 6 hours to process a 30-minute call,” she says. “But the fact you could get any feedback the same day was considered really revolutionary.” Now, with enhanced computing, power the original vision of performance-based feedback for medical providers was accelerated to actual real-time. Over the years, Grin built a team of dedicated subject matter experts and specialists pulling from those involved in the original research at University of Washington, as well as AI experts at Carnegie Mellon University, and industry experts from big tech. The idea for mpathic came about when Grin and team realized the commercial value of empathic listening: “Could we make an API that would instantly take any communication and make it more empathic, regardless of the use case?” The team built some of mpathic’s first models using data collected from Empathy Rocks, an empathy training game. In the game, therapists, including members of the Idaho State Crisis Line and California Indian Health Service, would respond to anonymous users from data in public forums with empathy and rank each other’s statements; they received continuing education for playing these games. “We had really diverse groups of people building these models through crowdsourcing that information,” explains Grin. Expanding empathy training and tools across industries As mpathic continues to evolve and grow their capabilities, the startup now has more than 200 different models for communication behaviors with tips and suggestions, including how to improve collaboration and power-sharing, and listen with more accuracy using reflections and open-ended questions. They also measure more unconscious metrics of human alignment, like language style synchrony, that have been found in Grin’s research to be more predictive of objective ratings of empathy than other skills. “The goal is not to replace human experience,” says Dr. Amber Jolley-Paige, Vice President of Clinical Product, “but to enhance it.” With a tailored and flexible approach, mpathic uses analysis and metrics to support customers’ specific needs and KPIs, whatever the industry. They currently offer a suite of AI-powered products: the core mpathic API, mConsult, and mTrial. The core API integrates into other software, analyzing communications and proposing actionable suggestions. For example, when mpathic used their API to analyze recruitment interviews for different companies, they found that those who received empathetic feedback had an 8 percent increase in candidate acceptance. mConsult provides immediate recommendations and coaching by reviewing audio or video recordings. And mTrial streamlines clinical trials by enhancing data quality and ensuring consistent care, while proactively reducing risk and easing medical professionals’ workloads. Envisioning the future of health equity mpathic’s journey shows no signs of slowing down. To better reach their goal of improving human communication, the team is expanding its API to specifically address diverse cultural behaviors and coach providers in cultural adaptation. Culture can affect how people communicate in various ways. For example, it may affect communication styles, how people deliver information, and their attitudes toward conflict. “With mpathic, we have the ability like never before to create more empathy in healthcare interactions and imagine a future where we can leverage AI to improve health equity,” says Dr. Alison Cerezo, Head of Research and Health Equity. The startup built training data from a diverse group of different genders, cultures, and backgrounds to help curb AI bias. “A lot of the issues that you see with AI bias comes down to models built from data collected from only one or two backgrounds and not understanding the lived experience of the people that those models will impact,” explains Grin. mpathic ensures that they regularly build, refine, and deploy their models with attention and alignment to an ethical AI framework. Moving forward, the team at mpathic plans to continue developing AI tools that recognize the nuanced and diverse viewpoints present in all human interactions. “There is no limit to the potential of this technology to train anyone to listen with empathy,” says Grin. Going big with AWS To scale their platform, mpathic needed a robust infrastructure. AWS provided a reliable, solid foundation for mpathic to grow and innovate securely. “We built on AWS to help us scale effectively and meet our customers’ needs quickly and seamlessly,” says Grin. “We’re a relatively tiny startup to be serving customers globally. To be able to tell our customers that we can host data wherever they are in the world is awesome, and wouldn’t b...
Transcribe →Saving the Earth in 60 Seconds, One Startup at a Time
Apr 30, 2024Tap to summarizeAs Earth Month draws to a close, we’re thrilled to showcase the innovative startups that participated in our “Saving the Earth in 60 Seconds, One Startup at a Time” challenge. These pioneering companies are each driving sustainability forward in their respective industries, from carbon planning to all-electric rideshare services and more. Let’s take a closer look at the startups featured! 1. Clarasight “At Clarasight, we believe that for companies to achieve their emissions goals, they need to know more than just what has happened in the past. They need to be able to look into the future to see what will happen ahead.” Adam Braun, CEO and co-founder of Clarasight, is leading the charge in forward-looking carbon planning and analysis software. With a focus on analytics, forecasting, and agile planning, Clarasight empowers organizations to seamlessly align their emissions goals—and their financial goals. Learn how Clarasight is using real-time data insights and integration capabilities to revolutionize sustainable decision-making, helping companies close the gap between intention and action. See Adam Braun, CEO and co-founder of Clarasight make his pitch to see if he can make his case in just 60 seconds. 2. Revel “Today in New York City, we have the four largest charging depots in the entire metro area; we run an all-electric rideshare fleet of over 500 vehicles. The entire rideshare sector in the city is going all-electric by 2030, by city mandate. Here at Revel, we are doing everything we can to accelerate that.” Frank Reig, co-founder and CEO of Revel, is transforming urban mobility with electric vehicles (EVs). Revel’s public, fast-charging network and all-electric rideshare service are driving dense urban cities toward cleaner, greener futures. With expertise in technology, engineering, and clean energy, Revel is accelerating the adoption of EVs and reshaping urban landscapes, starting with the most densely populated urban area in the United States: New York City. Our final founder in the ‘Saving the Earth in 60 Seconds, One Startup at a Time’ hashtag#EarthMonth challenge is Frank Reig, co-founder and CEO of Revel, an electric mobility and infrastructure company with a mission to accelerate EV adoption in America’s densest cities. Watch Frank Reig, co-founder and CEO of Revel, talk about transforming urban mobility with EVs. 3. BlocPower “Buildings in America represent 30 percent of our total US emissions. There’s no path to addressing the climate crisis without going building to building and greening all the buildings.” Donnel Baird, CEO and founder of BlocPower, has set out to green every city in America by 2030. BlocPower utilizes proprietary technology to upgrade homes and buildings with clean, energy-efficient, electric technology. By streamlining processes and making upgrades accessible, BlocPower is redefining sustainability in the built environment. Learn how BlocPower has already used artificial intelligence (AI) to build digital models of all 125 million buildings in America—and is currently working closely with four American cities to implement this plan, electrifying and decarbonizing every one of their buildings. Learn how Donnel Baird, CEO and founder of BlocPower, is revolutionizing buildings for smarter, greener, healthier communities for all. 4. HowGood Founder: Alexander Gillett “We can solve the climate crisis largely via the food system. Because not only does it produce the most [carbon emissions], but it is one of our best opportunities for sequestering carbon. [At HowGood], we map it out, we make it easy. We take the data workload off the hands of the people who want to be implementing the change.” Under the leadership of Alexander Gillett, co-founder and CEO, HowGood is revolutionizing sustainability intelligence for the food industry. With the world’s largest product sustainability database, HowGood enables companies to make informed decisions that reduce their carbon footprints and promotes environmental stewardship. Learn how HowGood’s Latis platform offers granular insights that drive sustainability reporting and carbon reduction efforts, allowing companies to easily implement change. Hear from Alexander Gillett, co-founder and CEO of HowGood, who talks about offering the world’s largest product sustainability database at your fingertips. As we conclude our Earth Month showcase, let’s continue to support and celebrate startups like Clarasight, Revel, BlocPower, and HowGood that are driving positive change for our planet. Together, we can build a more sustainable and resilient future for generations to come.
Transcribe →AWS Activate credits now accepted for third-party models on Amazon Bedrock
Apr 2, 2024Tap to summarizeIf there’s one thing we’ve learned at AWS, it’s the importance of experimentation. When you’re creating something new, it’s crucial to be able to try out different technologies and quickly iterate on an idea. But experimentation can be expensive, especially for a scrappy startup team at the earliest stages of innovating. That’s one reason why we launched AWS Activate, a program focused on supporting startup founders in every step of their journey, including providing more than $6 billion in credits to help you and others like you experiment on the AWS cloud with little-to-no upfront cost. Today, we’re taking another step to make it even easier for founders to build and iterate on their solutions using the latest technologies. We’re making AWS Activate credits redeemable for third-party models on Amazon Bedrock, our fully-managed service that offers a choice of high-performing foundation models (FMs) from leading artificial intelligence (AI) companies, like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon via a single API. This means founders everywhere can now use their AWS Activate credits to experiment with these and other FMs, along with a broad set of capabilities needed to build responsible generative AI applications with security and privacy. Our goal is to make it easier for startups to evaluate what FMs are more appropriate for their use cases and find the perfect match. Helping startups innovate with generative AI With our full-stack generative AI offering, AWS is helping more companies around the world embrace the potential of generative AI to transform customer experiences, enhance people’s productivity, and discover new business opportunities. Expanding AWS Activate credits to Amazon Bedrock is a key way to help startups leverage generative AI in their solutions from the start. This is one of the many benefits we have identified by working backwards from the needs of our customers, like Y Combinator. Since 2005, Y Combinator has funded and helped founders launch, build, and scale over 3,000 companies—including 60 unicorns—which currently have a combined valuation of more than $600 billion. “With virtually every startup quickly becoming an AI startup, our partnership with AWS has never been more relevant to the companies getting into our program,” said Michael Seibel, Group Partner at Y Combinator. “AWS has been a long-standing partner and a relentless advocate for our founders, helping them with hands-on support and access to the tools they need to build the products and services people all over the world use and love.” Since 2009, AWS has provided hundreds of millions of dollars and technical support to Y Combinator companies such as Stripe, Brex, Rappi, and many more—and in the last three years alone, AWS has provided more than $125 million in credits to Y Combinator startups. Our high-touch approach has resulted in Y Combinator companies consistently choosing AWS as their cloud provider—more than 70% of Y Combinator-funded companies run on AWS and if you look at the last two years alone, when the use of AI/ML has become more prevalent among startups, that number jumps to 80%.1 For the latest Y Combinator cohort (January 2024), AWS put together an exclusive package of benefits to help startups reduce upfront costs and get access to reliable, high-performance infrastructure to build their generative AI applications on. This includes $500,000 in AWS credits that can be used for: AWS Trainium, our purpose-built chip for training deep learning models, which offers up to a 50% cost-to-train savings over comparable Amazon Elastic Compute Cloud (Amazon EC2) instances; AWS Inferentia, a chip designed to enable models to generate inferences more quickly and at lower cost, with up to 40% better price performance; Reserved capacity of up to 512 NVIDIA H100 GPUs via Amazon EC2 through Capacity Blocks for Machine Learning, which dramatically increases GPU availability and ensures startups have reliable, predictable, and uninterrupted access to the GPU compute capacity required for their critical machine learning (ML) projects; and now third-party FMs on Amazon Bedrock. Foundation models for all Any startup can join AWS Activate and apply for up to $100,000 in AWS credits. In addition to experimenting with Amazon Bedrock, AWS Activate credits can be used to offset costs of AWS services, including infrastructure technologies like compute, storage, databases, AI/ML, and more. Learn more and become an AWS Activate member at startups.aws. 1 Numbers as of February 2024.
Transcribe →Unlocking the value of unstructured data: How Coactive built a visual analytics platform on AWS
Apr 1, 2024Tap to summarizeIt is said that a picture is worth a thousand words – and, according to Forrester Research, a minute of video may be worth 1.8 million words. For businesses ranging from ecommerce to social media, visual content is worth more than the amount of words that it conveys: it is an opportunity to build customer engagement, increase trust and safety, enhance personalization, and glean actionable insights based on content engagement. Coactive, a visual data analytics startup founded by CEO Cody Coleman and Will Gaviria Rojas in 2021, is democratizing the opportunity for businesses to analyze images and videos. Coactive’s co-founders Will Gaviria Rojas and Cody Coleman Images and videos are unstructured data—information that has not yet been ordered in a predefined way—that traditionally require machine learning expertise, a robust technical infrastructure, and significant amounts of time to accurately analyze. Coactive, the platform built on Amazon Web Services (AWS) and available on the AWS Marketplace, helps data practitioners derive rapid insights from unstructured data at scale and with minimal supervision. Accessible by user interface or APIs, the platform’s capabilities range from intelligent search to production analytics that use the full power of SQL. Proving what’s possible in AI Coactive’s innovative solution results from intensive amounts of time, research, and determination. During 2018, while earning his PhD in Computer Science at Stanford, Cody recognized that “artificial intelligence and intelligent applications were going to be the future. The blocker was that you needed hundreds of thousands of dollars’ worth of equipment and tremendous amounts of data to accomplish anything significant.” Use SQL to run analytics on your visual dataset. This photo shows a SQL query used to categorize images Bothered by these limitations, Cody committed to lowering the barriers to entry for machine learning so that everyone could benefit from it: “My mission during graduate school was to use my passion for computer science to benefit society at large while serving as a leader for future generations.” Cody joined the Stanford DAWN research project, a group focused on making it dramatically easier to build AI-powered applications. One of the many impressive breakthroughs from Cody’s work was DAWNBench, the first end-to-end machine learning (ML) systems benchmark used by global technology companies as the industry standard. In its first year, DAWNBench reduced model training time by 500x and training cost by 20x. Galvanized by the progress he’d made in creating accessible AI, Cody tackled the next big question: What to do next? At this time–by serendipity or coincidence–Cody’s friend Will moved to the San Francisco Bay area to begin a career at a large technology company. With a friendship spanning 10 years since their time as undergrads at Massachusetts Institute of Technology (MIT), Cody helped Will move in. “Will asked me the two questions you should never ask a PhD student because they cause an immediate existential crisis,” laughs Cody. “’When are you going to graduate?’ and ‘What are you going to do after?’” Cody had considered options that ranged from joining a prestigious technology company, becoming a university faculty member, joining a startup, or building a company. “Without hesitation, Will told me to build my own company,” says Cody. “He told me that it’s the right time and I have the right knowledge. And that he’d love to join me in this journey.” Months of conversations, research, and studying the problem first-hand led Cody and Will to come to the same realization: People need a visual analytics platform to unlock the value of their content, and it’s time to build it. With that decision, Coactive was founded. The Coactive team works together on version 1.0 of the Coactive platform. Creating a visual analytics solution for everyone Machine learning advanced significantly during Cody’s time at Stanford, but there was still much work to be done to make AI applications accessible to everyone: from the world’s largest companies, to a startup designing their minimum viable product. This was particularly true about machine learning that analyzed images and videos to derive actionable insights. For these unstructured data formats, the end-to-end workflow could require high-end large-scale compute in the form of GPUs, significant storage capacity, and large amounts of time and expertise due to the complexity of the process. A common workflow may include the following: Data scientists complete data exploration and build computer vision models to analyze and understand the visual data. ML engineers operationalize these models. Software engineers plug the model predictions into real world applications for consumers. To make visual content analysis more accessible, accurate, and efficient, Coactive pairs the breadth of existing large language models (LLMs) with the accuracy and automation that comes from applying a learning system to domain-specific data. After customers provide access to their large volumes of raw image and video files, Coactive uses pre-trained foundation models in conjunction with their proprietary active learning and classification system to embed and index the data. During this process, customers have the option to upload existing labels or provide a few examples so the Coactive platform can further learn any domain-specific nuances of their data. “One of the very powerful things about really large models is that we don’t actually need to toss a massive quantity of data to fine-tine for specific tasks,” explains Cody. “They call these large language models ‘few-shot learners’ for a reason. Rather than thinking about the quantity of data we toss at these systems, it’s really about quality.” The result? Customers can use Coactive to query, search, filter, and analyze visual content rapidly and at massive scale. Partnering with AWS to accelerate success As an innovative and rapidly-scaling startup, Coactive decided to migrate from their original cloud provider go all-in on AWS. Solutions offered by AWS align with Coactive’s four primary cloud provider needs: Depth and breadth of services, optionality in tooling, availability to power the scaling of their product, and security-first offerings. “We needed to build our solution on a cloud provider that could handle enterprise scale while being flexible enough to let us create something entirely new. With AWS, we were able to do this while ensuring best-in-class security to our customers,” says Cody. Building with AWS solutions After migration, Coactive set to work building a cutting-edge web application using AWS solutions such as Amazon Simple Storage Service (Amazon S3), Amazon Aurora, and Amazon Elastic Container Service (Amazon ECS). This web application helped Coactive establish their initial MVP and run proof of concepts for prospects. For their data-centric machine learning jobs, Coactive benefits from using Amazon Aurora PostgreSQL Serverless to serve low latency database requests without having to spend time managing their database infrastructure. Coactive’s many petabytes of image and video data are stored using Amazon S3. To front their web application, Coactive uses a combination of Amazon CloudFront as its content delivery network (CDN). The backen...
Transcribe →How Flodesk Is designing a new narrative for startups
Mar 25, 2024Tap to summarizeFor the creators of Flodesk, your inbox is personal. The San Francisco-based startup, founded in early 2018, is designing emails people love to get. As co-founder Rebecca Shostak says, “Flodesk is a relationship-building software. I want everybody to harness the power of these one-on-one, intimate relationships that they can have with their followers.” Rebecca’s design background is rooted in rock n’ roll; she began her career designing merchandise for a company that needed, in her words, “a female touch” for the artists on their roster. It was her first exposure to designing for different types of brands—and creating beautiful designs that sold in a variety of styles. These are the skills that Rebecca brought with her on her first solo venture: a template shop designed for popular email platform MailChimp. “I had always wanted my own business. I’m from Silicon Valley, and I have entrepreneurship in my blood from my dad. I wanted to start my own company. I saw a niche for Photoshop templates for wedding photographers. And there was a huge demand for it,” Rebecca explains. When she ran into customers struggling to implement her designs into the MailChimp platform, a bigger idea was formed: a platform that allows people to create beautiful, branded emails easily. “All email marketing companies were eco-oriented, focused on workflows and integrations. They weren’t focusing on the brand and the design,” Rebecca says. “I was like, ‘I have to do this.’ It was almost a religious calling.” Meanwhile, Flodesk’s co-founder CEO Martha Bitar was working in customer relationship management (CRM) for small business owners, in a similar market. “Martha was marinating in the same customer ecosystem that I was, just in different lanes,” Rebecca explains. Both Martha and Rebecca sensed a common problem in their world: “We see these people who are making millions of dollars with their services and their website looks amazing, their Instagram looks amazing, their photos look amazing, and their emails suck.” Designing a new narrative for startups For Flodesk, it has always been about flipping the narrative. The company has strong roots in design—they were an early leader in dreaming big about how beautiful emails can be. The oxymoron formed by “beautiful” and “emails” built the foundation of what would become Flodesk, according to Rebecca: “We’re taking softer products that, in the past, have been considered clunky, ugly, difficult to use, and just unappealing. For the small business market who’s extremely brand-focused, we’re flipping that on its head and making something that people never thought could be sexy, really sexy.” But the company’s disruption doesn’t stop at design. When Martha and Rebecca came together to create their prototype around January 2018, they turned to the path relatively untrodden for startups. As Rebecca details: “we thought, what if we pitched this and did it without funding? What if we were able to validate it and get the software going without even taking venture capital money?” Leaning into this concept, by the summer of 2019, Flodesk had a prototype. When an influencer friend of Rebecca’s built and sent an email using Flodesk, adding a plug for it at the bottom, trial requests began flooding in. “Flodesk launched itself long before we had the actual launch,” says Rebecca. Building a trusted brand through AWS Flodesk turned to Amazon Web Services (AWS) as a partner from the very beginning—the platform was built on Amazon Simple Email Service (SES). “I can’t imagine Flodesk without AWS intertwined with it,” says Rebecca. “They’re like the giant whose shoulders we’re standing on, in some sense, for our tech.” In the early days, the founders faced infrastructure problems with a sudden influx of subscribers. As Rebecca details: “we started out with AWS and we built on them. We were so open at the beginning with our policies because we just wanted as many people in the door as possible, but we found that that let a lot of scammers in. We were so brand new, things were going so quickly—we weren’t able to put the proper barriers in place to get the scammers out at first.” Rebecca spent a lot of late nights on the phone with AWS to troubleshoot the issue. “Early on, there were a lot of really close calls,” she explains. “We had moments where we were getting our server shut down because of scamming. But since we’ve worked with AWS and partnered with them to advise us on how to build out a more and more robust trust and safety team, that’s led us to be where we are today. I just can’t imagine our story without AWS in it.” Tony Silva, Flodesk’s startup portfolio lead at AWS, was there to answer those late-night calls from Rebecca. Tony now partners with Flodesk’s engineering team internationally to troubleshoot any issues that may pop up. As Tony explains, “with Flodesk, our relationship is outside even the tech perspective at this point. It’s about their entire infrastructure. It’s helping them lower their costs, making sure that they’re architected the right way, providing any sort of support, whether it’s a go-to-market or a personal relationship or a touchpoint.” For small businesses, the future is bright Today, with 70,000 customers, over $20 million in annual recurring revenue, and 35 team members all over the world, Rebecca is still adjusting to enormous growth: “sometimes I still feel like I have no idea what I’m doing. But I think we always had that bootstrap mindset.” Rebecca attributes a great deal of Flodesk’s success to the company’s founding principles. A focus on problem-solving for their customers continues to guide the company today. As Rebecca says: “we started with a founder idea. We had a product-market fit before we even touched a line of code. Start with the problem, not the solution. We were really focused on this segment of the market that’s been left behind by the legacy players. We wanted to create an experience where you could self-serve, that you weren’t reliant on going back and forth with our team. And one that was affordable too.” For Rebecca, the future is “wildly exciting” for small businesses. “I really believe that small businesses, and the creative people that run them, are going to own the future. And that future really excites me,” she says. Her outlook for Flodesk is particularly bright: “I want Flodesk to be the household name that they associate with growing business. My vision is that you have a household, you have an Amazon Prime account, you have a small business, and you have a Flodesk account.” Rebecca’s advice for startup founders? “Be scrappy. Sometimes the best ideas come from the scrappiest of places. Use your imagination and be creative.”
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