TheStage AI makes neural network optimisation easy
Backed by $4.5 million in funding, TheStage AI aims to make advanced model optimisation accessible to startups and enterprises alike.

In a move set to ease one of the most stubborn hurdles in AI development, Delaware-based startup TheStage AI has secured $4.5 million to launch its Automatic NNs Analyzer (ANNA).
Instead of requiring months of manual fine-tuning, ANNA allows developers to optimise AI models in hours, cutting deployment costs by up to five times. The technology is designed to simplify a process that has remained inaccessible to all but the largest tech firms, often limited by expensive GPU infrastructure.
TheStage AI’s system automatically compresses and refines models using techniques like quantisation and pruning, adapting them to various hardware environments without locking users into proprietary platforms.
Instead of focusing on cloud-based deployment, their models, called ‘Elastic models’, can run anywhere from smartphones to on-premise GPUs. This gives startups and enterprises a cost-effective way to adjust quality and speed with a simple interface, akin to choosing video resolution on streaming platforms.
Backed by notable investors including Mehreen Malik and Atlantic Labs, and already used by companies like Recraft.ai, the startup addresses a growing need as demand shifts from AI training to real-time inference.
Unlike competitors acquired by larger corporations and tied to specific ecosystems, TheStage AI takes a dual-market approach, helping both app developers and AI researchers. Their strategy supports scale without complexity, effectively making AI optimisation available to teams of any size.
Founded by a group of PhD holders with experience at Huawei, the team combines deep academic roots with practical industry application.
By offering a tool that streamlines deployment instead of complicating it, TheStage AI hopes to enable broader use of generative AI technologies in sectors where performance and cost have long been limiting factors.
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