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

It’s that time of year again. This morning, Y Combinator (YC) held a Demo Day for its 2022 Summer Cohort — the 35th Demo Day in the incubator’s history. Featuring founders from 30 countries and startups in fields including developer tools, fintech and healthcare, there was no shortage of impressive pitches today.

Competition is fiercer than usual due to YC’s decision in early August to reduce the batch size of around 250 companies by 40% in the wake of financial difficulties. But one particular category of startups stood out: those applying AI and machine learning to solve problems, especially for business-to-business clients.

There are only 14 startups this year compared to 20 last year, which makes sense since the entire cohort is also smaller. But the batches share a unifying theme: sales. At a time when businesses are up against the pressures of recession, their products are increasingly targeting bottlenecks in sales and marketing.

Financial challenges aside, the large addressable market makes sales an attractive problem for startups. Grand View Research estimates the sales force automation software market alone at $7.29 billion in 2019.

Pilot AI

Pilot AI Developing a tool for sales representatives that automatically translates call recordings into structured data that directly updates a customer relationship management (CRM) system. The idea is to save reps time and reassure their managers that pipeline data is up-to-date.

It’s worth noting that other platforms like and Microsoft’s Viva Sales also do this. But Pilot AI founder Max Lu, formerly a software engineer at Salesforce, says his product is more comprehensive than most and can generate a summary of each call as well as data points that map to CRM fields and questions reps ask. Key parts of the recipient’s answer.

Pilot AI

Image Credits: Pilot AI

Kind of

Kind of Also in the sales space, but it focuses on text prediction in web apps via a browser extension and server-side API. Initially developed as a smartphone app, TypeWise — which Fortune claims has 500 customers in the e-commerce and logistics industries — can auto-complete sentences, insert smart snippets, auto-reply to messages and check style and grammar consistency.

It’s a text expander and feels magical. But TypeWise is compatible with any CRM system and can be customized to company data with an analytics component that suggests which words and phrases to use, says founder David Eberle.

YC Summer 2022 AI startups focused on dev tools that don’t fall into the sales and marketing tech category, another lucrative avenue for growth. According to a recent survey that found 55% of developers struggle to find time to build internal apps, VCs certainly see an opportunity: They invested $37 billion last year in startups that create development tools.

Monterey AI

Monterey AI addresses a decidedly different part of the product lifecycle: development. Founder Chun Jiang pitched it as a “co-pilot for product development” that replaces documents with workflows that automatically generate product specs, including feature ideas, metrics, designs and launch plans.

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

Monterey AI

Image Credits: Monterey AI

Dev Tools AI

Dev Tools AI Maybe used in conjunction with Monterey AI.

Dev Tools AI provides a library designed to easily write tests for web apps in existing dev environments by drawing a box on a screenshot. By applying computer vision, it can find elements on webpages, such as search boxes and buttons, and can even see 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 has traditionally been a time-consuming process, requiring digging into the page’s code multiple times as the tested app evolves. Assuming the Dev Tools AI works as intended, it could be a valuable addition to quality assurance testing teams’ arsenals.

Maya Labs

Maya Labs Creating a platform for translating natural language into code. Similar to GitHub’s Copilot, Maya also generates programs and displays results in response to steps in English.

The service builds apps with a combination of conditional logic, AI-based search and classification, fine-tuned language models and template generation, says Sibesh Kar, one of the founders of Maya. 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 navigation, connecting APIs, and workflow automation, which — given the current state of AI text-to-language systems — seems within the realm of possibility.


For those who prefer a hands-on approach to programming, hello It claims to use AI to “instantly” answer developers’ technical questions with explanations and relevant code snippets from the web. The platform is powered by large language models (think GPT-3) that refer to multiple sources to find the most likely answers, according to co-founder Michael Royzen.

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


Image Credits: hello


Major is another startup with language models Numind, which provides a tool for data scientists, data analysts, and software engineers to build adaptive natural language processing models. Exploiting large language patterns similar to GPT-3, for example, NuMind can be used to find which job offers best match a given resume on a recruitment platform.

NuMind founders Etienne Bernard (former head of machine learning at Wolfram Research) and co-founder Samuel Bernard claim interest in the company has been overwhelming, with its paying customer base growing to nine in a month.

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