Brief:
I was asked to review and edit a tech startup's application for Y Combinator, a startup accelerator that helps new companies secure funding. It was important that the application use American English and convey a professional, yet relatively informal TOV. Included is an excerpt of the original text, followed by my edit. Changes are shown.
Copy (original):
We're building a computer vision system that helps industrial managers to monitor their production output. The value is in transparency of the production process and resulting output for the managers, even when they're not on site. It provides detailed vision of every item produced and large-scale analysis. The system identifies individual items on the production line, counts them and looks for anomalies. It keeps record of all inspected items, provides analytics for data-driven optimization and visual proof to investigate quality issues or customer complaints. The system is designed to be easy to install out-of-the-box by the end user, and fit most discreet manufacturing lines.
Team: The team has 15 years experiece in AI consulting and industrial manufacturing. AB InBev, Siemens, Boston Sceintific, VW, GM, Peugeot-Citroen, Intel, GE. Product: Proof of concept, including image capture, segmentation, preprocessing, anomaly detection, hardware and installation design Implementations: a pilot integration on the production line of a major beverage manufacturer. Partnerships: strategic cooperation with a major industrial computers manufacturer; strategic partnership with NVIDIA (Elite partner status).
All founders have been working on the system for three years, one of the founders - full time since Sept 2020 (engineering the system on-site, managing the development team, working with the customer)
Copy (edited):
We're building a computer vision system that helps industrial managers monitor their production output. Its value is in the transparency of the production process and the resulting output for managers, even when they're not on site. It provides detailed supervision of every item produced and large-scale analyses. The system identifies individual items on a production line, counts them, and looks for anomalies. It also keeps a record of all inspected items and provides analytics for data-driven optimization and visual proof to investigate quality issues or customer complaints. The system is designed to be easy to install out-of-the-box by the end user, and it fits most discreet manufacturing lines.
Team: Our team has over 15 years’ experience in AI consulting and industrial manufacturing. We have collaborated with such companies as AB InBev, Siemens, Boston Scientific, VW, GM, Peugeot-Citroen, Intel, and GE. Product: Proof of concept, including image capture, segmentation, preprocessing, anomaly detection, hardware, and installation design Implementations: A pilot integration on the production line of a major beverage manufacturer. Partnerships: Strategic cooperation with a major industrial computers manufacturer; a strategic partnership with NVIDIA (Elite partner status).
All of our founders have been working on the system for three years, with one of them in a full-time capacity since Sept 2020 (engineering the system on-site, managing the development team, and working with the customer).