7 AI startups that stood out in YC’s summer ’22 batch


It’s that time of year again. This morning, Y Combinator (YC) hosted a demo day for the 2022 summer cohort – the 35th demo day in the incubator’s history. Featuring founders from 30 countries and startups across a range of sectors including developer tools, fintech and healthcare, the day had no shortage of compelling lineups.

In early August, YC’s decision to reduce the group’s size by 40 percent to about 250 companies due to economic headwinds made the competition tougher than usual. But a different category of startups stood out: those applying AI and machine learning to solve problems, particularly for business-to-business clients.

He only had 14 starts this year compared to 20 last year, which makes sense because the overall team is smaller. But the teams share a unifying theme: sales. Their products often target barriers to sales and marketing as businesses struggle against economic downturns.

Economic challenges aside, a large addressable market makes it an attractive problem for startups to tackle in sales. Grandview Research puts the sales force automation software market alone at $7.29 billion in 2019.

Pilot AI

Pilot AI It is developing a tool for sales reps that will update a customer relationship management (CRM) system that automatically translates call recordings into structured data. The idea is to save reps time and assure their managers that pipeline information is up-to-date.

It’s worth noting that other platforms like Fireflies.ai and Microsoft’s Viva Sales do this too. But Pilot AI founder Max Lu, formerly a software engineer at Salesforce, says their product is more in-depth than most, and can additionally generate a summary of each call as well as data points showing CRM fields and questions asked by agents. Key parts of the recipient’s answer.

Image Credits: Pilot AI

in a kind way

in a kind way It’s also on point of sale, but focuses on text prediction across web apps via a browser extension and server-side API. Originally developed as a smartphone app, Thaiways — which says it has Fortune 500 customers in the e-commerce and logistics industries — can auto-complete sentences, insert smart snippets, auto-reply to messages and check spelling and grammar.

It looks a bit like TextExpander and Magical. But founder David Eberle says TypeWise is compatible with any CRM system and can be customized for company information, with an analytics component that suggests which words and phrases are being used.

YC Summer 2022 AI startups will focus on dev tools that don’t fall into the sales and marketing technology category, which is another growth path. Considering that 55% of developers struggle to find time to build internal apps in the first place, VCs certainly see an opportunity, according to a recent study: Last year, startups invested $37 billion in dev tools.

Monterey AI

Monterey AI It deals with a specific part of the product life cycle: development. Founder Chun Jiang positions it as a “co-pilot for product development” that replaces documents with workflows that automatically generate product specifications, including feature ideas, metrics, sketches and launch plans.

Using Monterey, customers choose a product template based on their use case (eg, “software-as-a-service”) and configure the resources, checking dependencies to resolve conflicts. Jiang says the platform can expose cross-team conflicts and dependencies while providing a bird’s-eye view of the portfolio to adjust features.

Monterey AI

Image Credits: Monterey AI

Dev Tools AI

Dev Tools AI Can be used in conjunction with Monterey AI.

Dev Tools AI provides a library designed to simplify tests for web applications in existing dev environments by simply drawing on screenshots. By applying computer vision, it finds elements on web pages like search boxes and buttons, and can even visualize controls in web games. It can also test for crawl errors on pages, including broken links, 404s, and console errors.

As founder Chris Navrides points out, writing end-to-end web tests is typically a time-consuming process, requiring one to dig deeper into the page’s code as the application being tested evolves. Assuming Dev Tools AI works as intended, it could be a valuable addition to QA testing teams’ arsenals.

Maya Labs

Maya Labs It is creating a platform for translating natural language into code. Similar to GitHub’s Copilot, Maya generates programs and responds to actions in English.

Sibesh Kar, co-founder of Maya, said the service builds apps using conditional logic, AI-powered search and classification, fine-tuned language models, and template generation. Currently, Maya can query and plot data from an external source, such as Google Sheets, Notion, or AirTable, and perform actions on that data, such as sending an email, uploading a file, or updating a database entry.

The long-term goal is to extend Maya to tasks like web browsing, connecting APIs, and automating workflows, which — given the current state of AI text-to-language systems — seems within reach.

Hello

For those who prefer a hands-on approach to programming, Hello He says he uses AI to “quickly” answer developers’ technical questions with explanations and relevant code snippets. The platform is powered by large language models (think GPT-3) that reference multiple sources to find the most probable answers, says co-founder Michael Rosen.

When Hello users submit a query, the service pulls raw site data from Bing and ranks and extracts insights using the aforementioned models. A set of different models translates the results into human-readable answers.

Hello

Image Credits: Hello

Calling

It’s another startup with language models at its core. CallingIt provides data scientists, data analysts, and software engineers with a tool to create custom natural language processing models. Using large-scale language models similar to GPT-3, NumMind, for example, can be used to determine which jobs best match a given job experience on a hiring platform.

NumMind co-founders Etienne Bernard (formerly of Machine Learning at Wolfram Research) and Make.org co-founder Samuel Bernard said demand for the company was so high, the number of paying customers grew to nine within a month.



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