UBOS

- Gretel is a cutting-edge platform specializing in synthetic data generation. - It leverages advanced AI to create realistic, privacy-preserving datasets for training machine learning models and conducting data analysis. - Ideal for organizations needing high-quality data without compromising privacy.

- Askcodi is an AI-powered tool designed to assist developers by generating code snippets, debugging, and providing coding assistance in real-time. - It leverages advanced machine learning algorithms to understand programming queries and deliver accurate, context-aware responses.

Our journey at Kite While we built next-generation experiences for developers, our business failed in two important ways. First, we failed to deliver our vision of AI-assisted programming because we were 10+ years too early to market, i.e. the tech is not ready yet. We built the most-advanced AI for helping developers at the time, but it fell short of the 10× improvement required to break through because the state of the art for ML on code is not good enough. You can see this in Github Copilot, which is built by Github in collaboration with Open AI. As of late 2022, Copilot shows a lot of promise but still has a long way to go. The largest issue is that state-of-the-art models don’t understand the structure of code, such as non-local context. We made some progress towards better models for code, but the problem is very engineering intensive. It may cost over $100 million to build a production-quality tool capable of synthesizing code reliably, and nobody has tried that quite yet. Nonetheless, we could have built a successful business without 10×’ing developer productivity using AI, and we did not do that. We failed to build a business because our product did not monetize, and it took too long to figure that out. We sequenced building our business in the following order: First we built our team, then the product, then distribution, and then monetization. Because our product was very difficult to build, we began by building a world-class engineering team. We did that very successfully. Then we focused on building our product. We did not reach product-market fit until 2019, five years after starting the company. It took many iterations and heavy engineering lifts to get there. Then we grew our user base. We executed very well here, and grew our user base to 500,000 monthly-active developers, with almost zero marketing spend. Then, our product failed to generate revenue. Our 500k developers would not pay to use it. Our diagnosis is that individual developers do not pay for tools. Their manager might, but engineering managers only want to pay for discrete new capabilities, i.e. making their developers 18% faster when writing code did not resonate strongly enough. Then we explored pivoting the business. We did a lot of customer discovery, and found a new direction — code search — that could leverage our AI technology and bottoms-up developer footprint. But after seven years of intense work and early-stage-startup stress, our team was too tired to pursue that pivot, and we decided to find a soft landing.