Our favorite startups from Pear VC’s invitational demo day
Reading Time: 3 minutesPear VC, a prominent pre-seed and seed-focused venture firm, has been running an accelerator for about a decade with about 10 startups in each batch.
Over those years, the small but mighty program has helped launch numerous companies like Viz.ai, whose FDA-approved AI can diagnose strokes (and was valued at $1.2 billion in 2022), relationship management company Affinity that raised an $80 million Series C at a $620 million valuation, according to PitchBook data, and Valar Labs, which uses AI to help doctors make cancer-treatment decisions. (It closed a $22 million Series A in May.)
This year, Pear has decided that it’s time to grow the size of its accelerator and provide the companies more services by offering them recruiting help and space inside its new 30,000-square-foot San Francisco office. Going forward, the 14-week program, now called PearX, will run twice a year. Each batch will consist of approximately 20 companies. The larger program is still a far cry from Y Combinator’s, which accepts hundreds of startups annually.
It’s not just the smaller size that distinguishes PearX from YC. The startups in each batch are usually not revealed until the demo day, an in-person event attended by over a hundred VC general partners, including from top firms such as Sequoia, Benchmark and Index Ventures. While YC says that it offers each company the same standard terms, the funding PearX startups receive from the firm can range from $250,000 to $2 million, depending on needs and stage of development.
This year’s demo day, which took place earlier this month, included 20 companies, most of which focused on AI. Among them, here are five that stood out to us and the crowd in attendance with fresh approaches to complex business problems.
What it does: identifies best infrastructure for multi-model AI applications
Why it stood out: AI companies want to make sure they’re using the best tools for the job. Figuring out which LLMs or small language models are best for each application can be time-consuming, especially since these models are constantly changing and improving.
Nuetrino wants to make it easier for AI companies to find the right mix of models and other systems to use in their applications. This way, developers can work faster and save money on running their products.
What it does: Automates market research
Why it stood out: Brands spend millions each year on market research. The process of surveying potential customers is time-consuming. Quno AI’s agents can call customers and gather qualitative and quantitative data. Results can then be analyzed in real-time. A bonus is that AI can quickly analyze results from these conversations.
What it does: Develops catastrophe models for home insurance carriers
Why it stood out: With natural disasters on the rise, property insurance companies are struggling to figure out which houses are at the highest risk of suffering significant damage during catastrophes. That’s because access to information about home structures is difficult and expensive to obtain.
Founded by two Ph.D.s in structural engineering, ResiQuant is creating models to estimate building features and how they’ll hold up during earthquakes, hurricanes, and fires. The company claims it can help insurance carriers assess risk more accurately, potentially lowering homeowner insurance premiums for those deemed to be lower-risk.
What it does: Monitors real-world production and alerts operators of mistakes
Why it stood out: In January, the doors of a Boeing 737 Max blew out mid-flight because four important bolts were missing, according to investigators. That situation is just one high-profile example of what can go awry within quality assurance systems. But manufacturers of all sorts of products have similar needs to detect defective products before they leave the factory.
Using cameras and AI, Self Eval hopes to address such concerns by verifying that tasks are completed correctly, flagging manufacturing errors in real time.
What it does: Creates lesson plans adapted for each teacher’s needs
Why it stood out: Software that adjusts difficulty based on individual student knowledge has been available for some time. However, TeachShare’s founders argue that many educational companies still offer a one-size-fits-all approach to curriculum development. This forces teachers to spend significant time modifying lesson plans to suit their specific classrooms. TeachShare aims to assist teachers in tailoring daily content, ensuring alignment with educational standards.
Ref: techcrunch
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