Build With Us


Our mission is to build the future by creating and launching enterprise software companies. We are in the business of starting new businesses and are constantly researching, vetting, and validating new business ideas. Below are a handful of ideas and industries we are currently pursuing. We're looking for exceptional entrepreneurs and early customers to join us and turn these ideas into businesses.

LLMs and Proprietary Datasets

Large language models (LLMs) like GPT-4 and PaLM 2 have been trained on hundreds of billions of parameters and made available to anyone in the world - for a fee. The wide availability of these so-called "foundation models" means that differentiation and competitive advantage for businesses building on top of them will come in the form of datasets that can be used to improve product offerings or be licensed to others.

Measuring Carbon Emissions Together

Not all emissions are created equal - and some are harder to accurately measure than others. Scope 3 emissions touch not just your own business, but those you work with and buy from. Measuring Scope 3 requires that businesses collect data from across their entire value chain. Even once that step is complete, carbon accounting is far from standardized - organizing and interpreting the data is another onerous process. We want to build a platform that helps businesses collect and standardize vendor emissions data so they can more easily and accurately measure their Scope 3 emissions.

Creating a Marketplace for Training Data

Large language models like GPT-4 have been trained with trillions of parameters and made available to anyone in the world - for a fee. The availability of these models - powered by the same underlying data - means that differentiation and competitive advantage will come in the form of proprietary data against which others can train their own models. There is some precedent for data collaboration and “give-to-get” models in data sharing, but the end-to-end purchase of datasets and immediate deployment in ML environments remains unsolved.

Servicing the Nation’s Electric Vehicle Charging Network

Billions of federal investment dollars is funding the buildout of the nation's EV charging station network. At present, 25% of public EV chargers are unusable due to broken connectors, network failures, payment system failures, and unresponsive screens. There is consensus that charger reliability and downtime will be a big challenge to overcome for the EV transition. As a result, federal money comes with a 97% charging station uptime requirement. Typically, site hosts aren’t directly overseeing charging stations. They use turnkey service providers who often subcontract different parts of the value chain. It is common for design, construction, and install to be handled by one firm and maintenance and repair to be handled by another. There are problems to solve around the fragmented nature of service providers, efficiently deploying certified technicians, and ensuring service-level agreements uptime requirements are met.

Innovating Around Reporting Requirements

Advances in ML will enable businesses to meet reporting requirements with less manual work and greater accuracy than they are currently able. In particular, we've spent time investigating the eXtensible Business Reporting Language (XBRL). XBRL is the open international standard for digital business reporting. Due to changes in reporting requirements, XBRL adoption has skyrocketed on a global scale over the past decade. Tagging data and converting a financial report to XBRL requires technical expertise. Even with software, the workflow is manual and tedious. Due to the complexity, finance teams often outsource XBRL conversion to professional services arms of reporting and audit software companies - signaling to us an opportunity to innovate.