Trueface applies machine learning and AI to convert camera feeds into actionable data, offering facial recognition, threat detection, age and ethnicity detection, license plate recognition, emotion analysis, and object detection. Its product claims include identifying employees for building access, detecting a weapon as it is being wielded, and preventing fraudulent spoofing attempts. The company provides multiple deployment options — a dockerized container, SDKs for partners, and a plug-and-play no-code solution — and emphasizes on-premise deployment with features like fleeting data and blurring for people who have not opted in. Trueface has a partnership with the U.S. Air Force to enhance base security and its software was originally embedded in the Chui intelligent doorbell covered in 2014. The company recently raised $3.7 million to scale the team, grow partnerships, and increase market share. The article situates Trueface amid rapid computer-vision market growth and ongoing privacy debates over facial recognition, noting the company’s built-in controls as a response. Trueface.ai develops a facial-recognition product focused on detecting spoof or presentation attacks and running on the edge in constrained environments. The technology was spun out of the Chui smart‑doorbell project after customers preferred to use their own hardware, and the company shifted from manufacturing to selling standalone software. Founders Shaun Moore and Nezare Chafni raised $500,000 in angel funding for Chui, opened a manufacturing facility in Medellín, Colombia, and then spun Trueface out in June 2017. Trueface subsequently raised $1.5 million in seed capital from investors including Scout Ventures, with participation from Harvard Business Angels and GSV Labs; that round has powered product development and integrations such as with IFTTT. The company is also partnering with a global nonprofit to collect anonymized facial images from Africa, Asia and Southeast Asia to reduce bias in its training data. Trueface has published three governing principles for responsible use: reduce bias in training data, privilege human decision‑making in legal cases, and prioritize data security and transparency.

Total raised

$4M

Funding rounds

2

Latest round

Equity

Latest activity

Jul 2019