MacPaw taps Liquid AI for on-device inference in its app store

MacPaw integrates Liquid AI for on-device inference in Setapp

MacPaw, the Ukrainian company behind the Setapp ecosystem and CleanMyMac, announced the development of a local version of its AI assistant Eney. The new feature uses Liquid AI's models to run inference directly on the device, without relying on cloud servers. The initiative aims to provide app store developers with a private, fast, low-latency AI tool.

Technical impact: efficiency and privacy on-device

Liquid AI develops neural architectures that are highly efficient, requiring fewer computational resources to operate in real time on local hardware. By integrating these models into Eney, MacPaw enables intelligent assistants to process commands and generate responses without sending data to the cloud. This reduces infrastructure costs, eliminates network delays, and enhances user privacy — critical factors for applications handling sensitive data.

Business perspective: retention and developer acquisition

For MacPaw, offering on-device inference via Eney is a strategic growth move. The company aims to increase Setapp subscriber retention by adding direct value to the ecosystem — an AI assistant that works offline without an extra subscription. Moreover, by making this capability available to third-party developers, MacPaw creates a competitive edge over rival app stores, encouraging the creation of apps with local automation, which reduces implementation friction and accelerates adoption cycles.

10Dobro's take: scale and automation with local AI

From 10Dobro's perspective, the bet on lightweight models and on-device inference represents a mature market trend: AI applications that prioritize performance and cost-effectiveness over expensive, high-latency APIs. For companies operating marketing automation systems, data analysis, or even real-time video processing, running inference on the client device means greater scalability, privacy control, and operational cost reduction. MacPaw's integration of Liquid AI shows that the future of product AI lies in lightweight, decentralized solutions — a direct lesson for any business seeking efficiency in automation and growth.