
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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SciSure is a Scientific Management Platform built to support the full range of laboratory operations for scientific organizations. It combines ELN, LIMS, and Health & Safety functionality, giving teams a single system to document experiments, track sample lineage, manage chemical inventory, and run structured, audit-ready compliance processes.
Instead of relying on disconnected systems, organizations get one governed platform that improves reproducibility, increases visibility into lab operations, and reduces risk as they scale.
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ESMFold2
ESMFold2 builds upon its predecessor, ESMFold, by establishing a new benchmark in single-sequence structure prediction and facilitating the creation of novel functional proteins via exploration of the latent space within the ESMC model. This advanced model is capable of forecasting high-resolution, all-atom 3D structures of biomolecular complexes straight from the amino acid sequence, and it allows for the incorporation of multiple sequence alignments to improve accuracy on difficult targets. Tailored for predicting structures through both sequence and structure modalities, it employs ESM representations that drive a series of looped folding layers while a diffusion model translates pairwise representations into atomic-resolution outcomes. ESMFold2 excels in predicting protein structures from amino acid sequences, providing detailed structural data, including precise all-atom coordinates for both backbone and side chains, along with confidence metrics and optional distogram predictions for in-depth structural evaluation. Furthermore, its innovative approach enhances the understanding of protein folding dynamics and functional implications, making it a valuable tool for researchers in the field.
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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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