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HomeFunding London-based AI Startup TitanML Secures $2.8 Mn Pre-Seed Funding

[Funding alert] London-based AI Startup TitanML Secures $2.8 Mn Pre-Seed Funding

The London-based AI startup TitanML secured $2.8 million in pre-seed funding and released Titan Takeoff, an AI-powered tool to accelerate the deployment of large language models (LLMs).
TitanML founders

The London-based AI startup TitanML secured $2.8 million in pre-seed funding and released Titan Takeoff, an AI-powered tool to accelerate the deployment of large language models (LLMs).

Octopus Ventures spearheaded the funding round, which also included several deeptech-supporting angel investors.

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Drs. James Dborin, Meryem Arik, and Fergus Finn founded TitanML, which has developed enterprise-ready programmes that makes LLM installation “faster, less costly, and more accessible for machine learning teams”

“Machine learning teams are facing a whole host of problems in model deployment – from GPU shortages to concerns over privacy when deploying through third parties like OpenAI. We’re transforming what was once the most challenging part of the development cycle into the easiest and most efficient, and it’s here where our flagship product, Takeoff, comes into its own,” says Arik.

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The pace of AI development is unprecedented. TitanML has created the AI deployment solution that will power the subsequent million AI applications in response to this need. Every ML team now has access to the best and simplest LLM deployments because they’ve sorted the LLM Ops. Thus, ML teams can concentrate on generating business value rather than dealing with the nightmare of LLM deployment.

“TitanML makes it possible for businesses to significantly reduce the processing requirements of high specification models quickly, cheaply, and with minimal trade-offs, unlocking the true potential of these technologies,” says Mat Munro, Investor, Deep Tech Team, Octopus Ventures.

Read also – [Funding alert] Godalming, UK-based heata Secures Seed Funding

About TitanML

TitanML is a group of highly skilled machine learning researchers, engineers, and architects who are all working to speed up the adoption of deep learning in businesses by bridging the gap between what is theoretically possible and what is actually done.

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