OpenMetal delivers hosted private cloud and bare metal infrastructure for organizations that have outgrown public cloud pricing or need more control than a hyperscaler will give them.
Built on OpenStack and Ceph, our platform gives you a fully managed private cloud without the cost and complexity of building your own from the ground up. You get dedicated hardware, root-level access, and a transparent fixed-cost model so your infrastructure bill stays predictable as your workloads grow.
Need bare metal without the private cloud overhead? Our dedicated bare metal servers deploy in minutes and can run standalone or integrate directly with an OpenMetal private cloud. Same fixed pricing, same dedicated hardware, no shared resources.
OpenMetal is built for engineering teams, DevOps, and infrastructure leads who are done deciphering complex cloud bills and being financially punished for their growth.
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AlisQI is a cloud-based Quality Management platform built for process and batch manufacturers who want to move beyond reactive firefighting toward stable, predictable operations while maintaining full compliance control.
Rather than organizing quality around static documents and isolated events, AlisQI was designed as a data-first system. Quality, laboratory, and production data are structured and connected in a shared operational backbone. This gives cross-functional teams early visibility into deviations, faster response times, and greater confidence in product integrity and daily execution.
The platform combines configurable quality modules, including document control, training, deviations, CAPA, audits, risk management, supplier quality, SPC, and EHS, with targeted, ready-to-use Solvers. Solvers integrate forms, workflows, dashboards, and business logic to address specific operational problems without unnecessary scope.
Because the system is built on structured data, manufacturers can apply practical AI within workflows, from automated COA extraction to conversational access to quality data and pattern detection across incidents.
Solvers are production-ready from day one and evolve as processes, products, or plants change. This progression does not require custom development or disruptive IT projects.
Manufacturers use AlisQI to harmonize quality practices across sites, reduce waste and rework, strengthen audit readiness, accelerate root cause analysis, and connect shop-floor and lab data directly to quality decision-making across industries including chemicals, plastics, packaging, food and beverage, personal care, automotive, and industrial manufacturing.
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ESMC
ESMC represents the newest advancement in the ESM series of protein language models, pushing the boundaries of representation learning within the field of protein biology. With training on billions of evolutionary sequences, it adeptly captures representations that encapsulate a mechanistic understanding of protein structure and function. The model utilizes a transformer architecture, focusing on sequences as its primary modality, and is trained on a vast dataset comprising up to 6 billion proteins. ESMC is tailored for various protein science applications, such as predicting structures, annotating functions, designing proteins, and exploring evolutionary connections among proteins. Additionally, it possesses the capability to create novel proteins based on partial sequences, structures, or functional constraints, thereby enabling researchers to investigate innovative avenues in protein design and biological discovery. Accessible through the Biohub Platform, ESMC can be utilized via an API and the ESM Python package, which includes quickstart resources for installation, API key generation, and platform connectivity, ensuring a seamless experience for users. This comprehensive accessibility encourages a broader engagement with protein research and enhances collaborative efforts in the scientific community.
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Biohub
Biohub serves as an accessible platform dedicated to advancing the understanding of protein biology. It offers users the ESM model family, which includes ESMC, ESMFold2, and ESM3, alongside interactive tools and resources tailored for developers involved in protein science research. ESMC stands out as a cutting-edge protein language model, meticulously trained on vast amounts of evolutionary sequence data, allowing it to create representations that elucidate key mechanisms underlying protein structure and function. This model facilitates various applications such as functional analysis, predicting structures, designing proteins, and investigating the evolutionary connections among different proteins. Meanwhile, ESMFold2 specializes in predicting high-resolution, all-atom 3D structures of biomolecular complexes from sequences, while offering the option of including multiple sequence alignments to improve accuracy for difficult targets. Additionally, ESM3 takes a holistic approach by simultaneously modeling sequence, structure, and function, thus enabling the generation of innovative proteins through conditioning on a blend of these aspects. This unique integration of tools and models empowers researchers to explore new frontiers in protein science.
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