These AI startups stood out most in Y Combinator’s Winter 2024 cohort

Despite an overall decline in startup investment, artificial intelligence funding increased last 12 months. Capital dedicated to generative AI ventures alone increased almost eightfold between 2022 and 2023, reaching $25.2 billion at the top of December.

So it’s no surprise that AI startups dominated Y Combinator’s 2024 winter demo day.

According to the official YC startup directory, the Y Combinator Winter 2024 cohort has 86 AI startups – almost double the variety of the Winter 2023 cohort and almost triple the variety of Winter 2021. Call it a bubble or overhyped, but clearly AI is the technology of the moment.

Like last 12 months, we looked through Y Combinator’s newest cohort — the group unveiled at this week’s Demo Day — and picked out a few of the more interesting AI startups. Everyone decided to chop for a unique reason. However, early on, they stood out from the remainder, whether it was their technology, the market they might reach, or the backgrounds of the founders.

Hazel

August Chen (formerPalantir) and Elton Lossner (former Boston Consulting Group) say the federal government contracting process is hopelessly broken.

Contracts are posted on 1000’s of various web sites and will contain a whole lot of pages of overlapping provisions. (The US federal government itself signs the agreement estimated Over 11 million contracts per 12 months.) Responding to those offers can take the equivalent of entire business departments, supported by outside consultants and law firms.

Chen and Lossner’s solution is to make use of artificial intelligence to automate the technique of discovering, drafting and ensuring compliance with government contracts. This is the name of a pair who met in college Hazel.

Using Hazel, users can match a possible contract to a possible contract, generate a draft response based on the request for proposal (RFP) and their company information, create a to-do checklist, and robotically perform compliance verification.

Given the AI’s tendency to hallucinate, I’m a bit skeptical that the responses and controls generated by Hazel will at all times be accurate. But in the event that they’re even close, they might save an incredible amount of effort and time by giving smaller corporations a shot at a whole lot of billions of dollars value of presidency contracts awarded every year.

Andy A.I

Home nurses take care of a variety of paperwork. Tiantian Zha knows this well – she previously worked at Verily, the life sciences division of Google’s parent company Alphabetwhere she covered areas starting from personalized medicine to mosquito-borne disease control.

During her work, Zha discovered that documentation consumed a variety of home-based nurses’ time. According to one in all them, it is a common problem testNurses spend over a 3rd of their time on documentation, reducing time spent on patient care and contributing to burnout.

To help nurses reduce their documentation burden, Zha is co-founder Andy A.I with ex Max Akhterov Apple staff engineer. Andy is basically a man-made intelligence author who captures and transcribes the spoken details of a patient’s visit and generates electronic medical records.

As with any AI-powered transcription tool, it exists risk of bias — this implies the tool doesn’t work well for some nurses and patients, depending on their accent and word selections. From a competitive standpoint, Andy is not precisely the first of its kind in the marketplace – its rivals include DeepScribe, Heidi Health, Nabla, and Amazon’s AWS HealthScribe.

But as health care increasingly more you progress home, evidently the demand for applications like Andy AI will increase.

Especially

If your experience with weather apps is anything like this reporter’s, you have been caught in a downpour after blindly trusting predictions of clear blue skies.

But it doesn’t must be this manner.

At least that is the premise of Precip, an AI-powered weather forecasting platform. Jesse Vollmar got here up with the concept after founding FarmLogs, a startup selling crop management software. To do that, he collaborated with Sam Pierce Lolla and Michael Asher, formerly principal data scientist at FarmLogs Especially reality.

Precip provides rainfall evaluation – for instance, it estimates the quantity of rainfall in a given geographic area over the previous couple of hours or days. Vollmar says Precip can generate “high-precision” data for any U.S. location all the way down to a kilometer (or two), forecasting conditions as much as seven days ahead.

So what’s the value of rainfall indicators and alerts? Well, Vollmar says farmers can use them to trace crop growth, construction crews can reference them to schedule work, and utilities can use them to anticipate service disruptions. Vollmar says one transportation customer checks Precip each day to avoid poor road conditions.

Of course, there isn’t a shortage of weather forecasting apps. But AI like Precip guarantees more accurate predictions – if it’s actually well worth the price.

Have

Claire Wiley launched a couples coaching program while pursuing her MBA at Wharton. This experience led her to explore a more technologically advanced approach to relationships and therapy, which culminated in Have.

Maia, which Wiley co-founded with Ralph Ma, a former Google researcher, goals to empower couples to construct stronger relationships through AI-powered guidance. In Mai’s Android and iOS apps, couples communicate with one another in a bunch chat and answer on a regular basis questions akin to what they consider challenges to beat, painful points from the past and lists of things they’re grateful for.

Maia plans to earn cash by charging for premium features akin to therapist-created programs and unlimited messaging. (Maia currently copies texts sent between partners – a frustratingly arbitrary restriction in the event you ask me, nevertheless it happens.)

Wiley and Ma, each from divorced households, say they worked with a relationship expert to create Ma’s experience. However, the next questions come to mind: (1) how solid is Mai’s relationship science, and (2) can it stand out in the extremely crowded couples app market? We’ll must wait and see.

Data curve

The AI ​​models at the center of generative AI applications like ChatGPT are trained on massive datasets, a mixture of public and proprietary data from across the web, including e-books, social media posts and private blogs. However, a few of this data is legally and ethically problematic, not to say flawed Other ways.

If you ask Serena Ge and Charley Lee, the issue is a transparent lack of knowledge curation.

Ge and Lee are co-founders Data curve, which provides “expert-quality” data for training generative AI models. This is especially coded data, which Ge and Lee say is especially difficult as a consequence of the knowledge needed to label it for AI training and restrictive use licenses.

Datacurve operates a gaming annotation platform that pays engineers to unravel coding problems, which contributes to Datacurve’s training datasets on the market. These datasets will be used to coach models for code optimization, code generation, debugging, UI design, and more, Ge and Lee say.

This is an interesting idea. However, Datacurve’s success will depend upon how well curated its datasets are and whether it will possibly encourage enough developers to proceed developing and improving them.