It’s that time of year again. This morning, Y Combinator (YC) hosted a demo day for its 2022 Summer Cohort – the 35th demo day in the history of the incubator. With founders from 30 countries and startups in a variety of industries including developer tools, fintech and healthcare, there was no shortage of compelling pitches on the day.
Competition was fiercer than usual, following YC’s decision in early August to reduce batch size by 40% to around 250 companies in the face of economic headwinds. But one particular category of startups stood out: those that use AI and machine learning to solve problems, especially for business-to-business customers.
This year there were only 14 such startups compared to 20 last year, which makes sense because the overall cohort is also smaller. But the batches share a unifying theme: sales. Their products largely address sales and marketing hurdles at a time when companies are dealing with recessionary pressures.
Economic challenges aside, the large addressable market makes sales an attractive issue for startups to tackle. Grand View Examination pinned down the sales automation software market alone at $7.29 billion in 2019.
Pilot AI
Pilot AI develops a sales rep tool that automatically translates call recordings into structured data that then directly updates a customer relationship management (CRM) system. The idea is to save reps time and assure their managers that the pipeline data is up to date.
It’s worth noting that other platforms like Fireflies.ai and Microsoft’s Viva Sales do this as well. But Pilot AI founder Max Lu, formerly a software engineer at Salesforce, says his product is more thorough than most, generating a summary of each conversation, as well as data points referencing CRM fields and questions from reps, in addition to key parts of the recipient’s response.
Image Credits: Pilot AI
typed
typed is also in the sales space, but it focuses on text prediction in web apps through a browser extension and server-side API. Originally developed as a smartphone app, Typewise — which claims to have Fortune 500 customers in the e-commerce and logistics industries — can auto-complete sentences, insert smart snippets, auto-reply messages, and check for style and grammatical consistency.
It sounds a bit like TextExpander and Magical. But founder David Eberle says Typewise is compatible with any CRM system and adapts to a company’s data, with an analytics component that suggests which words and phrases to use.
YC Summer 2022 AI startups that didn’t fall into the sales and marketing technology category tended to focus on dev tools, another lucrative path to growth. Given that 55% of developers struggle to find the time to build in-house apps, according to a recent questionnaireVCs definitely see an opportunity: they invested Last year $37 billion worth of startups making dev tools.
Monterey AI
Monterey AI tackles a distinctly different part of the product life cycle: development. Founder Chun Jiang calls it a “product development co-pilot” replacing documents with workflows that automatically generate product specifications, including feature ideas, stats, designs, and launch plans.
With Monterey, customers choose a product template based on their use case (e.g., “software-as-a-service”) and configure the input, checking dependencies to resolve conflicts. Jiang says the platform can uncover cross-team conflicts and dependencies while also providing a portfolio overview to align functions.
Image Credits: Monterey AI
Dev Tools AI
Dev Tools AI could perhaps be used in conjunction with Monterey AI.
Dev Tools AI provides a library designed to make writing tests for web apps in existing development environments easier by simply drawing a box over a screenshot. By applying computer vision, it finds elements on web pages such as search boxes and buttons, and can even see controls in web games. It can also test for page crawl errors, 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 you to dig multiple times into the page code as the tested app evolves. Assuming that Dev Tools AI works as intended, it can be a valuable addition to the arsenal of quality assurance testing teams.
Maya Labs
Maya Labs creates a platform for translating natural language into code. Like GitHub’s Copilot, Maya incrementally generates programs and displays results in response to increments in English.
One of Maya’s founders, Sibesh Kar, says the service builds apps using a combination of conditional logic, AI-powered search and classification, refined language modeling, 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 into tasks like web navigation, connecting APIs, and workflow automation, which – given the current state of AI text-to-language systems – appears to be within the realm of possibility.
Hello
For those who prefer a hands-on approach to programming, Hello claims to use AI to “instantly” answer technical questions from developers with explanations and relevant code snippets from the web. The platform is powered by large language models (think GPT-3) that reference various sources to find the most likely answers, said co-founder Michael Royzen.
When Hello users submit a query, the service retrieves raw site data from Bing and reorders it, then extracts understanding from the above models. Another set of models translates the results into human-readable answers.
Image Credits: Hello
NuMind
Another startup with language models at its core is NuMind, which provides data scientists, data analysts, and software engineers with a tool for creating custom natural language processing models. Using large language models similar to GPT-3, NuMind can be used, for example, to find which job openings best match a particular resume on a recruiting platform.
NuMind founders Etienne Bernard (the former head of machine learning at Wolfram Research) and Make.org co-founder Samuel Bernard claim interest in the company is quite high, with a growing number of paying customers in a time frame of one month.
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