Teradata VantageCloud: Open, Scalable Cloud Analytics for AI
VantageCloud is Teradata’s cloud-native analytics and data platform designed for performance and flexibility. It unifies data from multiple sources, supports complex analytics at scale, and makes it easier to deploy AI and machine learning models in production. With built-in support for multi-cloud and hybrid deployments, VantageCloud lets organizations manage data across AWS, Azure, Google Cloud, and on-prem environments without vendor lock-in. Its open architecture integrates with modern data tools and standard formats, giving developers and data teams freedom to innovate while keeping costs predictable.
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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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Genedata Biologics
Genedata Biologics® enhances the development of biotherapeutics, including bispecifics, ADCs, TCRs, CAR-Ts, and AAVs, providing a comprehensive solution for the industry. Recognized as the leading platform in the field, it seamlessly unifies all discovery workflows, allowing researchers to prioritize genuine innovation. By utilizing a pioneering platform that was purposefully created to digitalize the biotherapeutic discovery process, research can be accelerated significantly. The platform simplifies intricate R&D tasks by facilitating the design, tracking, testing, and evaluation of novel biotherapeutic drugs. It is compatible with various formats, such as antibodies, bi- or multi-specifics, ADCs, innovative scaffolds, and therapeutic proteins, as well as engineered therapeutic cell lines like TCRs and CAR-T cells. Functioning as a comprehensive end-to-end data backbone, Genedata Biologics connects all R&D processes, including library design, immunization, selection and panning, molecular biology, screening, protein engineering, expression, purification, and protein analytics, ultimately leading to thorough assessments of candidate developability and manufacturability. This holistic integration ensures that researchers can make informed decisions and push the boundaries of biotherapeutic innovation effectively.
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NVIDIA BioNeMo
BioNeMo is a cloud service and framework for drug discovery that leverages AI, built on NVIDIA NeMo Megatron, which enables the training and deployment of large-scale biomolecular transformer models. This service features pre-trained large language models (LLMs) and offers comprehensive support for standard file formats related to proteins, DNA, RNA, and chemistry, including data loaders for SMILES molecular structures and FASTA sequences for amino acids and nucleotides. Additionally, users can download the BioNeMo framework for use on their own systems. Among the tools provided are ESM-1 and ProtT5, both transformer-based protein language models that facilitate the generation of learned embeddings for predicting protein structures and properties. Furthermore, the BioNeMo service will include OpenFold, an advanced deep learning model designed for predicting the 3D structures of novel protein sequences, enhancing its utility for researchers in the field. This comprehensive offering positions BioNeMo as a pivotal resource in modern drug discovery efforts.
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