On August 3rd, PearX S20 alum Sequel, received FDA approval. Sequel’s co-founders, Greta Meyer and Amanda Calabrese, worked hard to develop their product and work through the FDA clearance process. This can be a grueling process, so it’s truly a huge milestone for Sequel to be able to launch their FDA-approved tampons. We wanted to mark the occasion by looking back on our journey working with the Sequel team.
Founders, Greta Meyer and Amanda Calabrese
We met Sequel co-founders, Greta Meyer and Amanda Calabrese, when they were still students at Stanford. They were both undergraduate engineering students, where they participated in the LaunchPad d-school course in 2018 to design and launch a product. They were both professional athletes that had lived first hand, menstruation leaks during practice or worse an athletic tournament. Mar was tasked with helping them refine their fundraising pitch and this is how our partnership with Sequel got started.
Sequel joined our PearX S20 cohort, right at the start of the pandemic, when they were still called Tempo. And even though it was unexpectedly a virtual cohort, it was an outstanding PearX class, with many other breakout companies like: Seven Starling, Interface Bio, Federato, Expedock, Gryps, and rePurpose Global.
PearX check-in during Covid.
After meeting Amanda and Greta, we chose to invest in Sequel for a few key reasons:
Big market opportunity:
Greta and Amanda were both superstar athletes and had a pain point: menstruation products are still not reliable for women. While participating in the Launchpad course, they dug into this more to see if others felt the same way. They learned that most women still prefer a tampon to all of the options out there, but they unveiled a common frustration for women: they had to combine their tampons with pads or pantyliners to avoid leaks. Amanda and Greta realized that, while the tampon had been around for ages, it hadn’t seen any innovation in nearly a century. They saw a big opportunity to build a women’s health company that was focused on innovating this big, unmet need for women.
Their product vision:
We were excited about their mission and vision: to re-engineer the tampon. For their product, the two engineers envisioned a redesign of a tampon in a spiral-shape to fit more comfortably and better absorb periods to prevent leaks. They designed and patented their design, so it’s truly proprietary. We really liked the product innovation and their vision to shake up an old industry that’s dominated by a few huge players like Tampax.
Greta and Amanda were stellar founders and superstar athletes too: Amanda competes for the US Lifesaving Association and Greta played Varsity Lacrosse while at Stanford. They had first hand experience with tampons failing them while being active, and we loved that they were both extremely purpose-driven.
Amanda and Greta presenting during Demo Day
During PearX, they built out a pretty robust go-to-market strategy, assembled influencers, and started a beta program. They got a quality system up and running to prepare for FDA testing. They were able to beta test with 75+ users before PearX Demo Day. They presented to thousands of investors at Pear Demo Day and raised a seed round from MaC VC, Long Journey Ventures, and others.
Since participating in PearX, they’ve had a number of exciting wins. Greta and Amanda were included on the Forbes 30 under 30 list for manufacturing and industry. Amanda moderated a SXSW panel on content creators and stigmatized spaces. And they received positive press from Fortune, the Wall Street Journal, Forbes and others.
With FDA approval behind them, we are excited for the Sequel team to be able to focus on commercialization and getting their product out in the world. We know the best is yet to come for this company!
PearX alumni company, Via, just announced that they’ve been acquired by Justworks. To mark this occasion, we wanted to share a look back on how we met and worked with the Via team over the years.
Mar first met sisters and Via co-founders Maite and Itziar when they were students at Stanford’s Graduate School of Business. They were enrolled in the Launchpad d.school course where Mar was a guest lecturer. Through the class, they developed an idea to build a marketplace to connect top professionals to short-term project work at different companies around the world. They built strong conviction through the course and we thought they were the perfect fit for as founders of these companies. We invited them to join PearX, our early-stage bootcamp for founders, and partnered with them from then on to build Via.
We decided to partner with Maite and Itziar for a few major reasons:
We saw a big market opportunity:
The Via co-founders discovered that there was a massive multi-billion dollar need for hyper local employer of record (EOR) and native payroll services for companies in international tech hubs. Through research, they learned that, when companies hire internationally, they typically do so via contractor agreements, but this happens without any truly scalable benefits, compliance, or payroll services. This works for companies that are hiring a small handful of employees in international markets, but it breaks down for companies that are hiring at scale with dozens or even hundreds of employees. Via saw a big opportunity to make that easier for these larger companies.
Strong ability to execute:
After seeing the big market opportunity, the Via team focused on building a product that met that need. They built deep infrastructure in over a dozen large, international markets, allowing Via to provide the right level of service to multinational Fortune 500 customers. Companies no longer had to spend years laying down the foundation to open a new hub, they could partner directly with Via and launch their new offices in a fraction of the time. They were quickly able to attract a number of top professionals to join companies internationally. We realized the cyclicality of this business model, and at the same time, they were hearing from their corporate customers that there was a big challenge in hiring employees internationally. Starting a new hub in a different country was desirable for many reasons, but payroll, insurance, benefits, and compliance all become very complicated, which prevented many companies from starting international hubs.
The founders were the right ones to tackle this:
The sisters and co-founders, Maite and Itziar, were the perfect duo to start and run Via. We loved the team because of their deep understanding of their customer’s pain point and their international focus. They are both GSB alums and brought deep business experience to the table: Maite has a background in investment banking at JP Morgan and Itziar was Head of Strategy for Citi Fintech. They also worked in markets like Argentina and Mexico, so they understood the pain points of hiring internationally.
Via’s work became even more relevant in a post-pandemic world, where there has been a big rise in companies hiring internationally. Put simply, they’ve become the best choice for Fortune 500 customers. This acquisition by Justworks makes sense: together they can offer the Via service at scale.
We are proud of their journey. After raising an initial pre-seed round after PearX, they realized that it would be hard to build a venture scale business focusing only on short term job opportunities. We huddled with them and they decided it would be best to pivot to the employer side and solve their problem which was more around building a permanent workforce internationally. Maite and Itziar navigated this pivot with rigor and optimism.
The team then scaled out their hyper-local EOR platform, scaled to millions of dollars in annual revenue, and raised a Series A in December 2022. To date, they’ve raised from an incredible group of investors, including Industry Ventures, Switch VC, Entree Capital, Burst Capital, Pear and more.
Given the infrastructure that they’ve built, it’s no surprise that they had inbound interest for M&A. We think they’ve found the perfect home in the Justworks team. The acquisition by Justworks provides an ideal platform to scale their joint business internationally allowing any company to become a global organization. Congrats to Maite, Itziar, and the entire Via team!
Founders often ask us what kind of AI company they should start and how to start something long lasting?
Our thesis on AI and ML at Pear is grounded in the belief that advances in these fields are game-changing, paralleling the advent of the web in the late ’90s. We foresee that AI and ML will revolutionize both enterprise software and consumer applications. A particular area of interest is generative AI, which we believe holds the potential to increase productivity across many verticals by five to ten times. Pear has been investing in AI/ML for many years, and in generative AI more recently. That said, there’s still a lot of noise in this space, which is part of the reason we host a “Perspectives in AI” technical fireside series to cut through the hype, and connect the dots between research and product.
Much of the progress in Generative AI began with the breakthrough invention of the transformer in 2017 by researchers at Google. This innovation combined with the at-scale availability of GPUs in public clouds paved the way for large language models and neural networks to be trained on massive datasets. When these models reach a size of 6 billion parameters or more, they exhibit emergent behavior, performing seemingly intelligent tasks. Coupled with training on mixed domain data such as the pile dataset, these models become general-purpose, capable of various tasks including code generation, summarization, and other text-based functions. These are still statistical models with non zero rates of error or hallucination but they are nevertheless a breakthrough in the emergence of intelligent output.
Another related advancement is the ability to employ these large foundational models and customize them through transfer learning to suit specific tasks. Many different techniques are employed here, but one that is particularly efficient and relevant to commercial applications is fine tuning using Low Rank Adaptation. LoRA enables the creation of multiple smaller fine tuned models that can be optimized for a particular purpose or character, and function in conjunction with the larger model to provide a more effective and efficient output. Finally one of the most important recent innovations that allowed the broad public release of LLMs has been RLHF and RLAIF to create models that are aligned with company-specific values or use-case-specific needs. Collectively these breakthroughs have catalyzed the capabilities we’re observing today, signifying a rapid acceleration in the field.
Text is, of course, the most prevalent domain for AI models, but significant progress has been made in areas like video, image, vision, and even biological systems. This year, in particular, marks substantial advancements in generative AI, including speech and multimodal models. The interplay between open-source models (represented in white in the figure below) and commercial closed models is worth noting. Open-source models are becoming as capable as their closed counterparts, and the cost of training these models is decreasing.
Our thesis on AI breaks down into three parts: 1. applications along with foundation / fine tuned models, 2. data, tooling and orchestration and 3. infrastructure which includes cloud services software and hardware. At the top layer we believe the applications that will win in the generative AI ecosystem will be architected using ensembles of task specific models that are fine tuned using proprietary data (specific to each vertical, use case, and user experience),along with retrieval augmentation. OrbyAI is an early innovation leader in this area of AI driven workflow automation. It is extremely relevant and useful for enterprises.We also believe that tooling for integrating, orchestrating, evaluating/testing, securing and continuously deploying model based applications is a separate investment category. Nightfall understands this problem well and is focused on tooling for data privacy and security of composite AI applications. Finally, we see great opportunity in infrastructure advances at the software, hardware and cloud services layer for efficient training and inference at larger scales across different device form factors. There are many diverse areas within infrastructure from specialized AI chips to high bandwidth networking to novel model architectures. Quadric is a Pear portfolio company working in this space.
Successful entrepreneurs will focus on using a mixture of specialized models fine tuned using proprietary or personal data, to a specific workflow along with retrieval augmentation and prompt engineering techniques to build reliable, intelligent applications that automate previously cumbersome processes. For most enterprise use cases the models will be augmented by a retrieval system to ensure a fact basis as well as explainability of results. We discuss open source models in this context because these are more widely accessible for sophisticated fine tuning, and they can be used in private environments for access to proprietary data. Also they are often available in many sizes enabling applications with more local and edge based form factors. Open source models are becoming progressively more capable with new releases such as Llama2 and the cost of running these models is also going down.
When we talk about moats, we think it’s extremely important that founders have compelling insight regarding the specific problem they are solving and experience with go-to market for their use case. This is important for any start up, but in AI access to proprietary data and skilled human expertise are even more important for building a moat. Per our thesis, fine tuning models for specific use cases using proprietary data and knowledge is key for building a moat. Startups that solve major open problems in AI such as providing scalable mechanisms for data integration, data privacy, improved accuracy, safety, and compliance for composite AI applications can also have an inherent moat.
A high level architecture or “Anatomy of a modern AI application” often involves preprocessing data, chunking it and then using an embedding model, putting those embeddings into a database, creating an index or multiple indices and then at runtime, creating embeddings out of the input and then essentially searching against the index with appropriate curation and ranking of results. AI applications pull in other sources of information and data as needed using traditional APIs and databases, for example for real time or point in time information, referenceable facts or to take actions. This is referred to as RAG or retrieval augmented generation. Most applications require prompt engineering for many purposes including formatting the model input/output, adding specific instructions, templates, and providing examples to the LLM. The retrieved information combined with prompt engineering is fed to an LLM or a set of LLMs/ mixture of large language models, and the synthesized output is communicated back to the user. Input and output validation, rate limiting and other privacy and security mechanisms are inserted at the input and output of LLMs. I’ve bolded the Embedding model, and the LLMs, because those benefit from fine tuning.
In terms of applications that are ripe for disruption from generative AI, there are many. First of all, the idea of personalized “AI Assistants” for consumers broadly will likely represent the most powerful shift in the way we use computing in the future. Shorter term we expect specific “assistants” for major functional areas. In particular software development and the engineering function overall will likely be the first adopter of AI assistants for everything from code development to application troubleshooting. It may be best to think of this area in terms of the jobs to be done (e.g., SWE, Test/QA, SRE etc), while some of these are using generativeAI today, there is much more potential still. A second closely related opportunity area is data and analytics which is dramatically simplified by generative AI. Additional rich areas for building generative AI applications are all parts of CRM systems for marketing, sales, and support, as well as recruiting, learning/education and HR functions. Sellscale is one of our latest portfolio companies accelerating sales and marketing through generative AI. In all of these areas we think it is important for startups to build deep moats using proprietary data and fine tuning domain specific models.
We also clearly see applications in healthcare, legal, manufacturing, finance, insurance, biotech and pharma verticals all of which have significant workflows that are rich in text, images or numbers that can benefit greatly from artificial intelligence. Federato is a Pear portfolio company that is applying AI to risk optimization for the insurance industry while VizAI uses AI to connect care teams earlier, increase speed of diagnosis and improve clinical care pathways starting with Stroke detection. These verticals are also regulated and have a higher bar for accuracy, privacy and explainability all of which provide great opportunities for differentiation and moats. Separately, media, retail and gaming verticals offer emerging opportunities for generative AI that have more of a consumer / creator goto market. The scale and monetization profile of this type of vertical may be different from highly regulated verticals. We also see applications in Climate, Energy and Robotics longer term.
Last but not least, at Pear we believe some of the biggest winners from generative AI will be at the infrastructure and tooling layers of the stack. Startups solving problems in systems to make inference and training more efficient, pushing the envelope with context lengths, enabling data integration, model alignment, privacy, and safety and building platforms for model evaluation, iteration and deployment should see a rapidly growing market.
We are very excited to partner with the entrepreneurs who are building the future of these workflows. AI, with its recent advances, offers a new capability that is going to force a rethinking of how we work and what parts can be done more intelligently. We can’t wait to see what pain points you will address!