US-based Standard AI has acquired Pathr.ai, a company specialising in in-store movement analytics and spatial intelligence.
The acquisition aims to accelerate the adoption of in-store movement analytics and spatial intelligence in retail.
The companies did not reveal the financial details of the deal.
Following the deal, Pathr.ai founder and CEO George Shaw will join Standard AI, along with several members of the Pathr.ai team.
Shaw brings experience spanning early research at the MIT Media Lab, large-scale analytics deployments at RetailNext, and senior engineering roles at Intel.
Shaw said: “The rigour behind Standard AI’s data - its accuracy and fidelity - is genuinely impressive. By bringing our teams and expertise together, we’re helping establish the data layer that will become foundational to physical retail.
“The impact we can deliver at a global scale is what excites me most.”
Standard AI said the acquisition comes at a time when demand for computer vision technologies is increasing, with retailers moving beyond pilot projects towards large-scale implementations that show clear returns on investment.
The company said its methodology prioritises long-form video analysis and direct measurement in physical environments, rather than relying on proxy indicators or modelled assumptions.
The transaction is intended to expand Standard AI’s capacity to deliver deployable, scalable in-store insights that can be tied to measurable results for retailers.
Founded in 2019, Pathr.ai focuses on applying behavioural analytics at enterprise scale.
Over the past year, Standard AI has expanded its international footprint and now operates in 24 countries across several sectors, including automotive, speciality retail, grocery and travel.
Standard AI CTO Dave Woollard added: “We’re thrilled to have George joining our engineering organisation as an SVP AI Strategy.
“He's spent years solving the exact problems our customers face: how to make spatial data reliable and interpretable. George’s expertise accelerates our momentum as we continue to make complex physical-world data more actionable.”
