SciSure
SciSure is revolutionizing laboratories across the globe with innovative digital solutions designed for the future. Our Digital Lab Platform (DLP) integrates essential tools such as Electronic Lab Notebooks (ELN) and Laboratory Information Management Systems (LIMS), alongside cutting-edge technologies like artificial intelligence and machine learning. Engineered for effortless integration with your laboratory's existing hardware and software, this platform significantly boosts flexibility, security, and overall efficiency. By streamlining and optimizing your research and development processes within a secure and compliant framework, we enable researchers to focus more on driving innovation. Our dedicated team of experts is here to assist you throughout every phase of your digital lab transformation journey, ensuring a smooth transition.
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AlisQI
AlisQI is a Quality Management platform built for process and batch manufacturers who want operational control without adding administrative overhead.
Where many QMS platforms were designed around document storage and event tracking, AlisQI was architected as a data-first system. Quality, laboratory, and production data are structured and connected in a single operational backbone. This enables teams to see deviations earlier, understand performance trends in context, and act before issues escalate into waste, rework, or customer complaints.
The platform includes modular capabilities across document control, training, deviations, CAPA, audits, risk management, supplier quality, SPC, and EHS. These capabilities are deployed through focused, ready-to-use Solvers that combine workflows, logic, dashboards, and analytics to address specific operational challenges without unnecessary scope.
Because the system is built on structured, connected data, manufacturers can apply practical AI directly inside their workflows. This includes automated extraction of supplier COA data without predefined templates, conversational access to quality records, intelligent rule generation, and pattern recognition across incidents to strengthen corrective action effectiveness.
Solvers are production-ready from the outset and evolve as products, processes, or sites change. Improvements do not require custom development or large IT programs, allowing organizations to modernize quality step by step.
Manufacturers across chemicals, plastics, packaging, food and beverage, automotive, and industrial sectors use AlisQI to reduce firefighting, increase predictability, strengthen compliance, and turn quality data into operational intelligence.
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alvaMolecule
alvaMolecule is a no-code cheminformatics platform that enables users to visualize, curate, and standardize molecular datasets in preparation for analytical work. It supports widely-used molecular formats like SMILES and SDF/MOL2, allowing seamless navigation through collections in either grid or spreadsheet views while automatically importing pertinent data. The platform guarantees structure verification and standardization through built-in standardizers and customizable SMIRKS rules, aids in the detection and management of duplicates, and offers scaffold analysis to summarize essential frameworks.
Moreover, it includes integrated filtering and charting features that enable sorting by substructures, calculated molecular descriptors, and various physicochemical properties. alvaMolecule can calculate approximately 88 structural and physicochemical properties, which include drug-like and lead-like scoring metrics such as LogP, TPSA, and the Lipinski alert index, ultimately supporting users in creating high-quality datasets for QSAR/QSPR modeling, descriptor computations, and virtual screening tasks. In addition, its intuitive interface makes it accessible for researchers of all coding skill levels, allowing them to effectively enhance their cheminformatics endeavors and streamline their research processes.
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ADME Suite
Forecasting the absorption, distribution, metabolism, and excretion (ADME) characteristics based on chemical structure is crucial. This set of precise calculations pertaining to pharmacokinetic properties is beneficial for facilitating high-throughput screening of various libraries. Additionally, it offers valuable insights into pharmacological impacts, aiding in the assurance of human safety for the products being developed. Such predictive modeling can significantly enhance the efficiency of drug discovery processes.
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