In a major step toward redefining how large language models (LLMs) are deployed in real-world enterprise environments, Doubleword - formerly known as TitanML - has announced a successful $12 million funding round. The round was backed by an elite group of investors including Dawn Capital, K5 Global, Hugging Face CEO Clem Delangue, and Florian Douetteau of Dataiku.

Founded by Meryem Arik, Jamie Dborin, and Fergus Finn, PhD, Doubleword is targeting one of the most critical pain points in artificial intelligence today: the challenge of operationalizing LLMs at scale without the heavy compute costs, latency issues, and infrastructural complexity that plague most deployments.

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From TitanML to Doubleword: The Evolution Toward Production-First AI

The name change from TitanML to Doubleword marks more than a rebrand - it signals a maturing vision aimed squarely at enterprise clients. With the explosion of generative AI in 2023–2024, businesses across sectors have raced to adopt LLMs. Yet very few have been able to deploy these models in a cost-effective, compliant, and performant manner.

That’s where Doubleword comes in. Their flagship offering is an inference engine that dramatically improves how LLMs are served - reducing latency, GPU costs, and energy usage while preserving model quality. It enables organizations to move from experimentation to production seamlessly.

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Investors Backing Deep AI Infrastructure

The $12 million injection comes from a powerful mix of AI visionaries and enterprise veterans:

• Dawn Capital – One of Europe’s leading B2B software VCs, known for backing enterprise-scale disruptors.
  
• K5 Global – A high-impact investor with a track record of scaling startups with cultural and technological influence.
  
• Clem Delangue, CEO of Hugging Face – A key figure in the open-source AI community, backing Doubleword speaks volumes about the startup’s credibility and trajectory.
  
• Florian Douetteau, CEO of Dataiku – A builder of one of the most successful AI platforms in the world, now placing a bet on the next generation of AI deployment.
  

With backing like this, Doubleword isn't just another AI tooling company - it's shaping up to be the infrastructure layer powering the next wave of LLM adoption.

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Why This Matters: The LLM Bottleneck No One Talks About

While the spotlight often shines on model performance and fine-tuning, the real-world constraint for LLM deployment is inference. Enterprises can get a good enough model from open-source repositories or via APIs, but when it’s time to go live - especially at high scale or with sensitive data - latency and cost become massive blockers.

According to recent data from Stanford’s Center for Research on Foundation Models, inference workloads account for over 80% of ongoing AI compute costs. And for businesses scaling across departments, this number becomes unsustainable without optimization.

Doubleword’s inference engine targets this directly, enabling models like LLaMA 3, Mistral, and others to be served in low-latency, low-GPU environments while maintaining accuracy and customizability.

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The Ultra Value Insight for Founders

As enterprise adoption of LLMs ramps up, a quiet divide is forming between teams building “quick wins” with off-the-shelf tools and those laying down infrastructure that compounds.

What most outsiders miss is that in the current wave of generative AI, margin compression is inevitable. Founders betting on LLMs as a service must quickly move from “proof of concept” to sustainable unit economics. The teams that win aren’t the ones that just build clever prompts - but those who understand the math of inference and build around it.

That’s what makes Doubleword so strategic. With its inference engine, startups and enterprises alike gain control over throughput, latency, and deployment flexibility, giving them the ability to decouple from third-party dependencies and gradually increase margins.

One CTO at a mid-sized fintech shared that switching from a hosted model API to a self-hosted setup using optimized inference reduced their GPU spend by over 68% - without needing to retrain the model. These are the economics that define market leaders over time.

If you're building with open-source models and not thinking about how you're serving them, you’re not just leaving money on the table - you’re letting your competitors outrun you on infrastructure efficiency. That difference becomes exponential at scale.

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Market Landscape: A Massive Opening in LLM Infrastructure

According to Grand View Research, the global AI infrastructure market is forecasted to hit $422.5 billion by 2030, growing at a CAGR of 27.3%. Enterprise-specific tooling for LLMs - especially infrastructure for serving, fine-tuning, and monitoring - will account for a major share of this growth.

What’s more, industry analysts from Gartner predict that by 2026, 60% of enterprises using LLMs will shift to self-hosted or private model deployments due to rising data compliance and cost concerns.

Doubleword sits in the bullseye of this trend. Rather than reinventing the wheel with new foundation models, it improves how existing ones are brought to life in production environments. Their system is compatible with the growing universe of OSS models (like Mistral, Mixtral, and Meta’s LLaMA family), allowing companies to retain model flexibility while benefiting from superior infrastructure economics.

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Competition and Differentiation

While a few startups like OctoML, Baseten, and Modal are also targeting the LLM ops stack, Doubleword’s edge lies in its laser focus on inference. Rather than trying to be a full MLOps platform or model builder, Doubleword positions itself as the premier inference solution - allowing AI teams to plug into existing workflows while massively upgrading performance.

This focus also opens up a competitive moat. As LLMs become more commoditized, speed, cost, and security will define which AI tools survive.

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What’s Next for Doubleword

The newly raised capital will go toward:

• Expanding their engineering team, particularly in inference systems and distributed computing
  
• Scaling their customer success operations to onboard enterprise clients faster
  
• Enhancing support for OSS LLMs, including multilingual and multimodal models
  
• Building out integrations with leading platforms like Hugging Face, AWS, and Azure
  

With industry momentum on their side, a strong founding team, and some of the most strategic AI investors in the world, Doubleword is positioned to become the gold standard for LLM inference at scale.