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Topic: Announcement

Orby AI closes $30M Series A to continue building AI Agents for the Enterprise

Posted on by lucy lee

Orby AI recently announced its $30 million Series A round, co-led by NEA, Wing Venture Capital, and WndrCo, with participation from Pear VC. We are proud to be Orby’s earliest investors (when a LinkedIn message from their CEO first connected us) and we are thrilled to continue our support now.

Orby’s Enterprise AI Automation tool automates complex workflows by observing users at work, identifying repetitive tasks, and writing the code to automate those tasks. Within minutes, a custom automation is ready to be implemented with user approval. 

This is game changing.

Orby AI is Changing the Game by Disrupting Process Automation Market

Co-founder and CEO Bella Liu was heading AI Product at UI Path, a leading business automation software company, when she was first inspired with the idea behind Orby. At the time, the RPA (robotics process automation) software being used relied on human users to input specific “if this, then that” rules, which turned out to be rather fragile. For example, a user who frequently opens invoices and transfers numbers to a spreadsheet must specify exactly which buttons to click and where on the screen those buttons will be— a system that is prone to error, slow, and hard to scale. 

Orby AI’s Model Learns and Implements Without User Input

Orby’s approach to business automation is a huge leap forward. Unlike traditional RPA models, Orby’s LAM (Large Action Model) approach means their product doesn’t need to be told which tasks to automate, or how. Orby simply observes a user at work, learns what could be automated, and creates the actions to implement it. The user just approves the process and can correct the model at any time, thus continuously helping Orby improve.

Why We Chose Orby AI

We’re very excited about Orby’s team. Co-founders Bella Liu and Will Lu bring deep experience and expertise in the AI and automation technology space. Bella (CEO) was previously the AI product leader at UiPath, from early-stage to post-IPO. Will (CTO) was previously the data platform leader at Google Cloud AI and was involved in three AI products with real world deployments within Google. Orby’s team was a great founder-market match for Pear’s thesis on AI automation for human to machine and machine to machine automation. We are pleased to have backed Orby early on, and remain certain they are the right group to work on this problem.

What Orby AI Does

Before partnering with Orby, the Pear team was already deeply interested in AI automation for enterprise applications, aiming to solve specific problems within distinct industries one vertical at a time. They believed that the kinds of AI tools which could understand specific use cases, gather necessary datasets, and execute targeted solutions were the future. Additionally, they had a thesis that semantic understanding of workflows, enabled by backend interaction data, could enhance the generalizability of RPA.

Orby’s team embraced a similar approach but expanded it to build a horizontal enterprise AI automation platform applicable across many verticals. Initially focusing on widely used workflows like invoice processing and expense auditing, they aimed to enhance their action-based foundational models. This led to the creation of a platform that delivers immediate value in various enterprise scenarios while achieving general-purpose AI automation.

Orby is pioneering a Generative Process Automation (GPA) platform, leveraging the industry’s first Large Action Model (LAM) for enterprise use. This platform enhances efficiency by enabling teams to automate complex tasks independently. Orby’s multimodal large action model, combined with an AI agent capable of symbolic reasoning and neural network analysis, seamlessly handles intricate automation requests.

When tasked with an assignment, Orby’s AI autonomously generates workflows, integrating with specialized AI agents for sub-tasks such as data analysis or customer interaction. By learning and automating workflows contextually and semantically, Orby surpasses traditional RPA systems. The LAMs empower Orby’s AI to understand and automate repetitive processes across unstructured datasets, emulating human capabilities.

This neuro-symbolic programming captures standard process flows and ensures robust exception handling, making AI-driven automation accessible and efficient for enterprises. Orby’s patented technology, which combines LAMs with advanced programming techniques, empowers workers to automate tasks without needing technical assistance. The system continuously learns and adapts, improving productivity and efficiency over time.

Market Opportunity

The market potential for automation in enterprises has been evidenced by the success of Robotic Process Automation (RPA). However, AI Process Automation, like that offered by Orby, goes beyond traditional RPA by making previously uneconomical use cases viable. The return on investment (ROI) for RPA is often hindered by high implementation and maintenance costs, limiting its applicability.

Orby’s innovative approach addresses two critical challenges of RPA:

1. Semantic understanding of automatable workflows versus fragile rule-based systems.

2. Hands-off, continuous online learning and improvement of both workflow discovery and implementation.

By discovering automatable repetitive workflows and generating maintenance-free AI automations, Orby significantly reduces implementation and maintenance costs. This makes a much larger share of repeatable workflows candidates for automation, substantially improving ROI and expanding an already large market.

This advancement is not merely an efficiency gain for high-volume, repeatable workflows. Imagine an AI capable of automating any workflow, regardless of volume, simply by demonstrating the process. This capability would enable enterprises to innovate their workflows at an accelerated pace, shifting focus to strategic improvements. In this competitive landscape, no enterprise can afford to ignore such technology, as those who adopt it will innovate faster.

To provide a baseline, a 2017 McKinsey Future of Work report estimates that 60% of jobs involve at least 30% repetitive tasks that can be automated. Orby has already demonstrated massive productivity gains in several Fortune 500 companies through successful use cases. This is just the beginning; the market opportunity is far greater.

How we partnered together

After funding Orby’s seed round in July 2022, in addition to our close partnership on product and vision, we leveraged the full Pear team to partner with them in the following two years. Ana Leyva and Pepe Agell worked with the Orby team on their product-market fit and GTM strategy, and Jill Puente helped them in marketing and PR, including landing the Business Insider piece announcing the initial seed round. Nate Hirsch from Pear’s talent team helped Orby hire eight out of their first ten team members (eight engineers, two designers, and a recruiter). When it was time for Orby’s Series A raise, the company went through Pear’s fundraising bootcamp with Mar’s full support behind them. As we say to founders at Pear: if you get one of us, you get all of us as partners.

We’re excited to have been supporting Orby AI since day one and look forward to their promising journey ahead!

Arash and Bella at our AGM summit in March 2024.

PearX S21 alum Valar Labs raises $22M to continue building AI that helps oncologists make better decisions

Posted on by jill

Last week, PearX S21 alum, Valar Labs, announced its $22 million Series A round co-led by DCVC and a16z with participation from Pear. We’re incredibly excited for this milestone for Valar Labs and wanted to take this opportunity to look back on our history working with their team.

We met the Valar Labs co-founders at a free boba event we hosted on Stanford’s campus in 2020. After that first meeting and introduction, the Valar Labs team applied to Pear Competition. And from there, they joined our PearX S21 cohort. 

PearX S21 cohort, which included companies including: Valar Labs, Kale, and Aklivity

When we met the team, we were incredibly excited to back them for a few big reasons:

  • This is a massive and unsolved market opportunity. Valar is using AI to help oncologists choose the correct treatment for their patients. Unfortunately, for thousands of cancer patients each year, there is a high degree of uncertainty around which treatment option works best for them. This uncertainty leads to billions of dollars of potentially unnecessary drug costs, and even more importantly, wastes precious time for thousands of patients battling cancer. The sad reality is that most cancer patients end up on a treatment sequence that fails them, and Valar Labs is on a mission to change that.
  • The team had a clear vision for the type of product that could best meet this unmet need. Valar is truly a software company that enables capabilities that legacy diagnostic companies do not have. Unlike many diagnostics companies for which the core technology revolves around a novel laboratory assay, Valar uses AI to analyze images of a patient’s tumor and to predict the likelihood of response to different standard-of-care treatments. This information helps oncologists determine the best treatment pathway for a patient.
  • The team was well suited to tackle this problem: Valar’s co-founders, Anirudh Joshi,  Viswesh Krishna, and Damir Vrabac, met at Stanford in 2020 when they were all part of Dr. Andrew Ng’s machine learning group. While at Stanford, they had the opportunity to spend time at the intersection of AI and medicine, speak to hundreds of oncologists, and find the key areas of high medical unmet need. Not only were they had were innately curious and incredibly quick learners, they also gathered the most esteemed KOLs in the space including Professor Eric Collison from UCSF and Professor Pranav Rajpurkar from Harvard/Stanford  to solve this problem. 

The team hit the ground running during PearX, and the Pear team was there to assist them in hiring their first employee, introduce them to strategic advisors on both go-to-market and the clinical side, and get their story in shape for fundraising. While whiteboarding with the team, it was clear that to be successful, they needed data access and partnerships with cancer centers. 

Valar’s Demo Day pitch in October 2021

Upon completing PearX, Valar hit a number of milestones in their company growth. After Demo Day, Valar Labs closed their seed round of $4.15M led by the a16z Bio + Health fund. They’ve leveraged their seed funding to reach clinical validation and form partnerships with top academic and medical institutions, and they’ve unveiled Vesta, the first-ever, AI-based oncology test for bladder cancer.  

Anirudh and the Pear team

When we first met Valar Labs, they were 3 founders operating out of a dorm room. Now they are more than 14 employees with $26M raised. Their growth path has been incredible to date and we know the best is yet to come for this team. 

Pear + Open AI Hackathon

Posted on by jill

Pear and OpenAI teamed up to welcome over 150 hackers to Pear Studio SF for our first ever hackathon on April 27th! Kicking off bright and early at 9:00 am, the space was buzzing with energy as hackers dove into their projects, brainstorming and coming up with some seriously cool ideas. We had 49 teams submit projects and were completely blown away by the quality of execution and the diversity of ideas.

Winners:

Congratulations to all of the finalists and category prize winners! 🏆 

Winners:

Finalists:

Here is the full gallery of projects built at the hackathon. There’s an incredible variety of ideas and innovations created in a single day.

Founders Panel:

Aparna Sinha (Advisor, former Pear AI/ML Partner) also hosted a founder panel with Harrison Chase (Co-Founder/CEO at Langchain), Shreya Rajpal (Co-Founder/CEO at Guardrails AI), Rayan Krishnan (Co-Founder/CEO at vals.ai), and Simon Suo (Co-Founder/CTO at LlamaIndex). We discussed what type of problem each of their companies solves, addressing Data, RAG, reliability, and Eval, and what future areas are most important, such as multimodality, new model architectures, and agents.

About PearX:

PearX invests between $250k to $2mn, and offers access to Pear Studio SF, bespoke recruiting support to close your first hires, GTM support to acquire your first customers, and a dedicated Pear partner who works directly with you. PearX alumni have raised over $2bn in follow-on capital.

Some more pictures from the day:

This hackathon not only highlighted the immense talent within the AI community but also reinforced the potential of AI-driven capabilities in addressing real-world problems. We can’t wait to host another hackathon again soon, and we hope to see you there!

Celebrating 100 hires for Pear companies 🥳

Posted on by jill

Last February, we introduced Pear Talent Services to the public. At the core of our announcement was our promise to directly hire the most crucial talent for our founders—a commitment we extend to every company we invest in. Today, we’re thrilled to announce that we’ve made 100 hires for our founders, marking a significant milestone in our journey.

To mark this occasion, we wanted to share a few trends that emerged from our initial 100 hires.

We specialize in supporting early stage companies and the distribution of hires reflects just that.

The vast majority of hires made were technical, particularly founding or early Engineers.

75% of hires came directly from other tech companies. 

21% of hires attended either Stanford, CMU or Berkeley.

Hiring has shifted back to in-person or hybrid roles, with only 25% of positions being remote.

If you’re a founder interested in learning more about our Talent Service offering, click here.

Welcoming Ana Leyva to the team!

Posted on by jill

Pear is thrilled to welcome Ana Leyva to our go-to-market team! Ana’s journey is a testament to her passion for the startup ecosystem, with past roles at tech unicorns Box, ServiceTitan, and Vanta. We’re so excited to have her onboard as part of our GTM team, offering her first-hand experience as a seasoned operator. 

In her short time with us, Ana has already supported over 30 Pear companies, strategizing with them on all things GTM. This includes many critical steps of sales like: nailing ICP, prospecting, customer discovery, messaging, and more. 

A Bay Area native, Ana is a first-generation college graduate from Princeton University and holds an MBA/MA in Education from Stanford. After graduating from Princeton, she joined Box pre-IPO at Series E. At Box, she was hooked on startup culture, especially the GTM and commercial arms. Following Box, she was an early sales hire at ServiceTitan and then, while at Stanford GSB, worked with Vanta to hone their early sales motion. Following business school, Ana became a founder and CEO herself with her ed-tech startup Lelu. Ana embodies the entrepreneurial spirit that Pear champions. 

“From early on, I saw both Mar and Pejman champion founders in a way that was genuine and authentic. That authenticity and commitment to being helpful is the backbone of Pear’s culture and makes it stand out in the sea of VCs.”

Ana was a Pear Fellow while at Stanford GSB, and we’re so excited to have her with us full time. She hosts Winning Wednesdays, a bi-weekly webinar series on GTM topics, and will continue to build out Pear’s GTM programming. 
Interested in connecting with Ana? Email her at ana@pear.vc.

Welcoming Hannah Berke to Pear!

Posted on by jill

We’re excited to announce that Hannah Berke joined Pear a few months ago as part of our Community + Operations team! A seasoned community leader, we can’t wait to have her on board, especially after our recent Pear Studio SF expansion. 

Born and raised in Chattanooga, Tennessee, Hannah was introduced to the Bay Area as an undergraduate at Stanford. As an undergrad, Hannah worked in technology integration and strategic communications at Amazon and MongoDB. She was also deeply involved in university life, serving in a number of different university offices, including the Board of Trustees, and community groups. “I learned the impact that a strong community can have on someone’s success— and that I was super passionate about creating those types of environments for people.” 

Hannah first encountered Pear through a classmate and friend who participated in PearX. She’d been hooked on startup culture ever since arriving at Stanford, and Pear’s founder-first approach stood out among VCs— “the mentality of giving before getting”. At Pear, Hannah now supports this goal, creating the community support founders need to build the best companies possible. 

“Mar and Pejman’s vision for a best-in-class ecosystem and community of tech builders is amazing, and I’m honored I get to be a part of bringing it to life.”

We’re thrilled to have Hannah on board. You can reach her at hannah@pear.vc.

PearX: join the ranks of elite founders

Posted on by jill

The most elite founders walk through walls to build category-defining companies. They are unique individuals, who don’t come by very often. Having seeded and helped build companies like DoorDash, Dropbox, Vanta, Aurora Solar, Gusto, Guardant Health and Affinity from their earliest days, we know what these founders look like and what they need. We have developed PearX to partner and support the very best entrepreneurs at the very beginning of their journey to help them get to the next stage. 

We believe small cohorts of less than 20 teams give PearX companies a disproportionate advantage. As a result, 90% of our PearX alums go on to secure funding following our Demo Day. Pear’s unparalleled resources lead to these unrivaled results. Here are some of the things we offer to each PearX company.

  • Capital to build your company, your way: We invest between $250k and $2M in all PearX companies. We know that some founders only need a small amount of capital to ideate and other teams are in more cost-intensive verticals that require more funding. All companies are unique so we’ll work with you. 
  • Founder to founder: Work 1:1 with a partner who has been in your shoes and knows your industry. Our team has started and sold 10 companies to the likes of Cisco, Instacart, Plaid, and Zynga.
  • Join the best community: Entrepreneurship doesn’t have to be a lonely journey. Joining PearX means joining a community of like-minded founders. We kick off each cohort with Camp Pear: a 3-day retreat for the entire cohort to come together, learn key company building tactics, and get to know one another. Not only will you work alongside your PearX batch for 14+ weeks, but you’ll also have the wider PearX alumni network to lean on. 
  • Access Pear Studio: Everyone in PearX receives dedicated office space in Pear Studio SF, our 30,000 square foot state-of-the-art office space with standing desks, conference rooms, phone booths, and more. This space is completely free to you for the first 12 months.
  • Build a scalable sales motion: Our go-to-market team, Pepe and Ana, will guide you through the critical steps of sales: nailing ICP, prospecting, customer discovery, messaging, and more. 
  • Recruit the best talent: Our dedicated PearX Recruiter, Nate, will find your founding engineer, co-founder, or whatever critical hire your team needs. In the last two cohorts, Nate has hired 25 people for our PearX companies. Nate leads the full cycle of recruiting for your team – from sourcing to closing candidates. This is an unprecedented level of support for an accelerator, but that’s how much we believe that hiring impacts company building. Last batch, Nate hired an average of two people per PearX company.
  • Fundraise strategically: We help you raise additional capital when you’re ready. From perfecting the story and creating a pitch deck to creating a target investor list and negotiating and closing your round. In fact, 90% of companies that go through PearX raise capital from institutional investors.

Are you interested in joining our PearX S24 cohort? We’re actively looking for our next batch of founders, and we’d love to hear from you. Please apply at pear.vc/pearx!

Introducing Pear’s Healthcare and Biotech Industry Advisory Councils!

Posted on by vivien

Our team at Pear is thrilled to announce an incredible new resource for our Healthcare and Biotech founders. Today, we are officially unveiling Pear’s Healthcare and Biotech Industry Advisory Councils!

At Pear, we feel privileged to work closely with our founders at the earliest stages of company development, specializing in seed and getting founders to product market fit. We also aim to provide them with best-in-class platform support across talent, go-to-market, fundraising, and PR & communications. 

We recognize, however, that our founders building in Biotech and Healthcare have specialized needs. Over the past few months, our Biotech Partner, Eddie and our Healthcare Partner, Vivien have been hard at work carefully crafting and recruiting a “dream team” of leading founder-CEOs, operators, executives and academics. We’re so honored to be partnering with this esteemed group and are grateful for their generosity to support the next generation of founders.

Introducing Pear’s Biotech and Healthcare Industry Advisory Councils:

In Biotech, we often back stellar scientists and engineers starting companies based on innovative platform technologies with multiple applications. They are leaders within their domains of technical expertise, but they understandably need help in areas such as selecting a lead indication, developing a partnering strategy, negotiating an IP license, or refining a program budget and timeline. 

In Healthcare, we are looking for multidisciplinary teams with clinical, technical, and go-to-market DNA.  Technology can only make a difference if health systems providers, payers, and pharma are willing to adopt these solutions and we believe our advisory council helps refine customer strategy and supercharge our founders’ customer adoption, along with our hands on GTM support at Pear. 

Learn about how we invest in Healthcare and Biotech:

We have created landing pages on our website for our Healthcare and Biotech investing practices which share more details about our investment scope, approach to working with founders, and current Industry Advisory Councils.

We’ve already been impressed by the impact these advisors have made on our founders and their companies, and we can’t wait to see what’s next!

If you’re a Healthcare or Biotech founder and interested in partnering with Pear VC or getting to know our advisors, please reach out at vivien@pear.vc and eddie@pear.vc.

Anatomy of a successful Artificial Intelligence startup

Posted on by jill

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! 

PearX alum Transcera announces seed round to further advance innovative delivery platform for biologic drugs

Posted on by jill

Last month, PearX S21 alum Transcera announced its seed round led by Xora Innovation, joined by Tau Ventures and existing pre-seed investors Pear, Digitalis Ventures, and KdT Ventures. To mark this milestone, we wanted to share more about Pear’s partnership with Transcera and its founders, Hunter Goble (CEO), Justin Wolfe (CSO), and Wayne Lencer (scientific co-founder).

We first met Hunter and Wayne when they applied to Pear Competition. Hunter was still an MBA student at HBS and Wayne was a professor at Harvard Medical School and researcher at Boston Children’s Hospital. At the time, they were part of the Nucleate program and looking to commercialize a new drug delivery platform they were working on. 

S21 PearX Demo Day

When we met the team, we were excited about Transcera for several reasons:

  • Enormous unmet need and market opportunity. Today, many of the most impactful and best-selling medicines are so-called biologic drugs, comprising complex molecules that are typically synthesized via living systems rather than chemical means. These are large, bulky molecules including peptides or proteins that often exhibit little-to-no uptake upon oral dosing, and instead have to be injected into the blood or under the skin. For obvious reasons, when given a choice, patients strongly prefer the convenience of a self-administered pill. For example, pharma companies are now racing to develop orally administered versions of GLP-1 receptor agonists for obesity, as the first such drugs approved for this blockbuster indication have required administration via subcutaneous injection. 
  • Broad, differentiated, and defensible technology platform – inspired by nature, informed by high-quality science, and validated preclinically. For almost three decades, Wayne and his group have been studying how large, orally ingested bacterial toxins, such as cholera toxin, are able to cross the intestinal epithelial barrier to cause disease. The lab discovered that structural features of key membrane constituents called glycosphingolipids directly influence the cellular sorting of these lipids and any associated payloads. Building on this foundational work, Transcera has demonstrated preclinically that synthetic lipids can be harnessed to achieve transport of biologics across intestinal barrier cells, enabling oral delivery and enhanced biodistribution. This active transport mechanism is differentiated from most existing approaches to enhancing oral bioavailability, which have primarily focused on passive diffusion enabled by permeation enhancers and other formulation excipients, and have suffered from relatively low absorption and narrow applicability. 
  • Ambitious operating team with complementary skill sets. Prior to HBS, Hunter spent 5 years at Eli Lilly working on the commercial launch of a key drug product, and he also served as an internal consultant around new product planning for the pharma company’s immunology division. Justin completed his PhD in the Pentelute lab at MIT where he focused on delivery strategies for biologic drugs, including novel conjugation approaches for peptides, and he later worked as a scientist advancing the discovery and medicinal chemistry of macrocyclic peptides at Ra Pharmaceuticals through its acquisition by UCB.  Whereas Hunter brings a savvy commercial mindset and disciplined financial rigor to Transcera, Justin in turn brings apt academic and industry domain expertise and strong scientific leadership skills. Hunter, Justin, and Wayne all share a passion for translating basic science research into technologies and programs that can make a tremendous impact for patients.

We have been impressed with the Transcera team’s execution and scientific progress so far. During PearX S21, we worked closely with Hunter on shaping the story and crafting the pitch ahead of Demo Day. Before joining the Pear team full-time as Pear’s biotech partner, I served as an industry mentor to Transcera, advising the team on questions related to IP in-licensing. After joining Pear, I continued to work closely with Transcera and fellow syndicate co-investors on scientific strategy, fundraising, and partnering.

The full PearX S21 cohort after Demo Day

Over the past two years, the Transcera team has proven to be resilient and resourceful. The production and scale-up of the synthetic lipid conjugates were challenging to master, but the team’s diligent efforts yielded multiple promising lead compounds. Critically, the team has expanded to bring in hands-on expertise in chemistry, cellular biology, and preclinical development, among other areas. 

With this recent financing, we are excited to continue to back the Transcera team, and we are eager to see further development of the platform, with the ultimate goal of unlocking the full potential of biologic drugs for patients.

The Transcera team is hiring! Please check out the job posting here: https://jobs.polymer.co/transcera/28175. If you’re interested please reach out to Eddie and we can connect you!