Predicting the success of start-up companies, defined as achieving an exit through acquisition or IPO, is a critical problem in entrepreneurship and innovation research. Datasets such as Crunchbase provide both structured information (e.g., funding rounds, industries, investor networks) and unstructured text (e.g., company descriptions), but effectively leveraging this heterogeneous data for prediction remains challenging. Traditional machine learning approaches often rely only on structured features and achieve moderate accuracy, while large language models (LLMs) offer rich reasoning abilities but struggle to adapt directly to domain-specific business data. We present \textbf{CrunchLLM}, a domain-adapted LLM framework for startup success prediction. CrunchLLM integrates structured company attributes with unstructured textual narratives and applies parameter-efficient fine-tuning strategies alongside prompt optimization to specialize foundation models for entrepreneurship data. Our approach achieves accuracy exceeding 80\% on Crunchbase startup success prediction, significantly outperforming traditional classifiers and baseline LLMs. Beyond predictive performance, CrunchLLM provides interpretable reasoning traces that justify its predictions, enhancing transparency and trustworthiness for financial and policy decision makers. This work demonstrates how adapting LLMs with domain-aware fine-tuning and structured--unstructured data fusion can advance predictive modeling of entrepreneurial outcomes. CrunchLLM contributes a methodological framework and a practical tool for data-driven decision making in venture capital and innovation policy.

Investors are continuously seeking profitable investment opportunities in startups and, hence, for effective decision-making, need to predict a startup's probability of success. Nowadays, investors can use not only various fundamental information about a startup (e.g., the age of the startup, the number of founders, and the business sector) but also textual description of a startup's innovation and business model, which is widely available through online venture capital (VC) platforms such as Crunchbase. To support the decision-making of investors, we develop a machine learning approach with the aim of locating successful startups on VC platforms. Specifically, we develop, train, and evaluate a tailored, fused large language model to predict startup success. Thereby, we assess to what extent self-descriptions on VC platforms are predictive of startup success. Using 20,172 online profiles from Crunchbase, we find that our fused large language model can predict startup success, with textual self-descriptions being responsible for a significant part of the predictive power. Our work provides a decision support tool for investors to find profitable investment opportunities.

Investors are interested in predicting future success of startup companies, preferably using publicly available data which can be gathered using free online sources. Using public-only data has been shown to work, but there is still much room for improvement. Two of the best performing prediction experiments use 17 and 49 features respectively, mostly numeric and categorical in nature. In this paper, we significantly expand and diversify both the sources and the number of features (to 171) to achieve better prediction. Data collected from Crunchbase, the Google Search API, and Twitter (now X) are used to predict whether a company will raise a round of funding within a fixed time horizon. Much of the new features are textual and the Twitter subset include linguistic metrics such as measures of passive voice and parts-of-speech. A total of ten machine learning models are also evaluated for best performance. The adaptable model can be used to predict funding 1-5 years into the future, with a variable cutoff threshold to favor either precision or recall. Prediction with comparable assumptions generally achieves F scores above 0.730 which outperforms previous attempts in the literature (0.531), and does so with fewer examples. Furthermore, we find that the vast majority of the performance impact comes from the top 18 of 171 features which are mostly generic company observations, including the best performing individual feature which is the free-form text description of the company.

Before they were robots, they were "androids" or "automatons." The word "robot" is commonly accepted as having arrived in English through -- of all places -- a Czech play. "R.U.R." made its public debut in Prague 102 years ago, yesterday. It would arrive in the States a year and a half later, with Spencer Tracy making his nonspeaking Broadway debut as one of Rossum's titular Universal Robots. The playwright Karel Čapek humbly noted the following decade that he couldn't take full credit for the word's origin.

A new text-based artificial intelligence (AI) tool called ChatGPT is making waves in the technology industry for its ability to accurately answer questions and complete a wide range of tasks, from creating software to formulating business ideas. Launched on November 30, 2022 by OpenAI, the AI program has already impressed users and technologists with its ability to mimic human language and speaking styles, all the while providing coherent and topical information. In the span of just a couple of days, the service managed to cross the one million user threshold. Now, industry observers and commenters are theorizing on the technology's potential implications in the finance and banking sector. According to Ethan Mollick, an associate professor of management at The Wharton School of the University of Pennsylvania, ChatGPT is a tipping point for AI and proof that the technology can be useful to a broader population of people.

There are many reasons robotics companies fail. From an ill-conceived idea to poor execution or the inability to raise funding, building and running a sustainable robotics company is challenging. This is never a fun recap to write. We don't want to see startups fail, but inevitably many do. The last couple of years have been especially difficult thanks to a global pandemic, economic uncertainties and ongoing supply chain issues.

New research in which machine-learning models were trained to verify more than one million companies has demonstrated that artificial intelligence (AI) can precisely quantify the success and failure aspects of a startup. The outcome is a tool that allows investors to identify the next opportunities. A known fact is that about 90% of startups are unsuccessful - about 10% to 20% fail within their first year. This shows the notable risk to Venture Capitalists and other investors in early-stage companies. In an attempt to identify which companies are most likely to succeed, researchers have developed a machine-learning model trained on the historical performance of more than one million companies.

Conversational AI is a fast-growing industry with a number of start-ups and established companies offering a wide variety of products and services for an even wider variety of customers. We compiled, reviewed, and curated nearly 200 companies and technologies, created one big list and categorized them in several ways to try to help understand what's taking place in the space: As we started reviewing the various companies and their offerings it became clear there were broadly two classes of offerings: those companies that offer technologies for builders: Developer Platforms vs. companies that offer products and services for enterprise end-users: Enterprise Platforms. Within the builder category, there are several types of companies most of which tend to be closer to the machine learning software itself and designed for software developers or product analysts. As mentioned in a previous blog, we found interesting domain-specific bots in the following areas: finance & insurance, health & medical, HR & recruiting, restaurants, and contact centers & customer service. Because of the volume of activity and interest in the area, we've also included sales and marketing/lead generation as another domain-specific area.

AI or artificial intelligence could predict startup success to an impressive 90% accuracy, a study using machine learning models that look into tons of companies showed. As per Embroker, startups turn out to be a complete failure in most cases. To be precise, about 90% of them do not become successful. What's more, about 10% of startups end up being a failure every year, regardless of what industry it is in--whether it is from tech or retail. Not to mention that failure began at roughly the second to the fifth year of the firm. However, CBInsights learned in its recent data that 42% of the unsuccessful startups are due to misreading the market demand.

Much of the U.S. artificial intelligence (AI) discussion revolves around futuristic dreams of both utopia and dystopia. However, it bears remembering that AI is also becoming a real-world economic fact with major implications for national and regional economic development as the U.S. crawls out of the COVID-19 pandemic. Based on advanced uses of statistics, algorithms, and fast computer processing, AI has become a focal point of U.S. innovation debates. Even more, AI is increasingly viewed as the next great "general purpose technology"--one that has the power to boost the productivity of sector after sector of the economy. All of which is why state and city leaders are increasingly assessing AI for its potential to spur economic growth.