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The AI Automation Wave: Enterprise AI Startups to Watch

May 24, 2023, Post from FoundersX team. - With the GPT storm in the tech world, there is no doubt that the true AI Automation wave is here. In this post, we will share our new investments in enterprise AI automation from YC W23 batch. We picked the teams in strong position for growth with speed and scalability.

Layup - Complete workflows, with just one line

Layup is an AI-powered assistant for automating workflows, founded by Ryan Xue, Badal Jain and Michael Lemm. It helps individuals and organizations streamline their daily tasks with simple prompts. Layup integrates with over 170 commonly used products across functions such as File Storage, HR & Payroll, ATS, Accounting, CRM, Ticketing, and Communications. For example, users can generate a summary of weekly company updates from Slack and Notion, create an MSA with past data from Salesforce and export it to Google Docs to share instantly with their team, or simply pull what they're looking for from all the files they have access to.

The Layup founder team saw the huge pain-point in finding information and executing workflows across multiple applications within a company. They experienced firsthand how tedious and repetitive these tasks can be. Layup solves this problem by condensing multiple steps into a single prompt, simplifying daily workflows and reducing the need to switch between different services. With its expanding list of integrations, Layup enables enterprise users to complete tasks effortlessly across a wide range of applications and save time to focus on what matters.

Berri AI - The #1 API for Building Production-Ready Enterprise LLM Apps

BerriAI is founded by Krrish Dholakia and Ishaan Jafferoffers. It provides a powerful API for enterprise users to build production-ready ChatGPT applications in just minutes by connecting their data to a Language Language Model (LLM). The platform supports various use cases, including customer support, internal knowledge base queries, product data analysis, generating LinkedIn posts, and textbook question answering. BerriAI handles all the infrastructure needs related to LLMs, such as data ingestion, embedding storage, querying databases, and supports major LLM providers like OpenAI, Google, and Anthropic. It also provides features like finetuning and testing, allowing users to improve their results and deploy the apps easily. BerriAI can supercharge your products with the power of LLMs like ChatGPT & go from prototype to production in less than a week.

BerriAI has proven to be effective in real-world scenarios. For example, Hosteeva, a property rental platform, increased automated ticket coverage by 35% and reduced customer support costs. They achieved this by creating a finetuned BerriAI chatbot for each property on their website, enabling customers to obtain detailed answers without having to call support. Another success story involves Wellness XYZ, which managed to reduce latency by 86% and simplify their processes by adopting BerriAI for managing their end-to-end LLM app flows. By leveraging BerriAI, they were able to handle data ingestion, finetuning, and logging seamlessly. These examples demonstrate how BerriAI can empower businesses to enhance their operations and achieve significant improvements in efficiency and customer satisfaction.

Keeling Labs - Next-Generation Energy Management for Grid-Scale Batteries

Keeling Labs is founded by Jack OGrady, building a best-in-class energy management service for grid-scale batteries powered by machine learning. It aims to bring novel power-scheduling technology to market while seamlessly integrating directly into your existing operations. Not all energy management is created equally. With solutions from trading desks to computer auto-bidders, picking the right (or wrong) provider can lead to day-and-night differences in performance and experience. With energy storage, optimization Is everything. There’s constant pressure to improve returns on our battery energy storage assets.

The Grid system is constantly changing, we can’t completely model it in reality, and the number of states it can be in is massive. For example, there’s going to be a 40x increase in installed battery capacity by 2030 (per the IEA). The right strategy for operating a battery today will not be the right strategy a week from now, a month from now, a year from now, etc. Further, the right strategy will change specific to each battery and where in the grid it is. We need self-learning systems to get the full performance out of these grid assets. Keeling Labs is redefining energy optimization with Reinforcement Learning (RL). It's pioneering the application of RL to grid-scale batteries. It’s a paradigm shift in energy management.


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