Multiverse Computing - Pioneering the Era of Efficient and Sovereign AI

We empower organizations to run secure, production-ready AI with tailored solutions — reducing compute costs and retaining full control across cloud, data centers and edge environments

7 members

Contact info

Link

multiversecomputing.com

Members

Jeff Ester

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3rd+

CRO & Founder | Bestselling Author of “Never Be Closing” | Building AI-First Revenue Engines for Seed to Series C Startups.

Dominika Albotova

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3rd+

Talent Acquisition Partner at Multiverse Computing

Daniel Doll-Steinberg

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3rd+

cofounder/partner EdenBase. Seen a lot. Done a bit. Trying to make sense of it. Investor. Author of Unsupervised. How AI, tech are changing our lives & society. A Bloomberg Best Book 2023: People in Power Found Essential

AMAIA GARMENDIA

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3rd+

Chief People Officer at Multiverse Computing

Rana Prathap

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3rd+

Enabling Frictionless Recruitment @ MapRecruit.ai Quantum Computing for life. Squash, Food and Music during what's left.

Onur L Cetin

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3rd+

Ex-academic (briefly, escaped during post-doc) and ex-quant (UBS, Barclays Global Investors). After a bit of corporate life at Bloomberg and IHS Markit, last 5 years included sales roles at various start-ups and scale-ups across Climate Risk, Big Data Analytics, Systematic Trading Infrastructure, Alt Data/ML-based Macro Forecasting and Quantum Computing. Now a GTM consultant/fractional salesperson focused on AI and deeptech.

Benjamin Cox

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3rd+

○ Are your LLM or Computer Vision training and inference costs growing? ○ Are your model compression efforts compromising model accuracy? ○ Concerned about third-party data security for GenAI tools and want to control your models? ○ Need to make your AI systems more efficient and portable? CompactifAI by Multiverse Computing is a foundational AI model compression tool, using patented neural network techniques to halve cost & inference time without compromising on accuracy. Compatible with existing compression methods, CompactifAI delivers lighting fast and crazy small foundational models: 🔍 93% size reduction 🚀 2X faster training and inference speed 🤑 50% lower costs of running in both training and inference time 👋 30% reduction in compute resource needs If you are deploying and fine-tuning AI models, this could be a huge cost saving, and directly impact the user experience due to lower latency. Let's talk! EMAIL: benjamin.cox@multiversecomputing.com MEET: https://calendly.com/bcox-multiversecomputing