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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Process Street is the Compliance Operations Platform built for teams that need to move fast without breaking standards. It combines document control, workflow automation, and AI-powered oversight in a single system so every policy is followed, every step is tracked, and every audit is effortless.
Unlike legacy GRC tools or static SOP docs, Process Street turns compliance into a living system. Policies are documented in governed, version-controlled Pages. Those policies are executed through dynamic workflows with built-in task assignment, approvals, and forms. Every action is logged, monitored, and optimized in real time by Cora, our AI compliance agent.
Used across industries like financial services, real estate, healthcare, and manufacturing, Process Street helps teams automate employee onboarding, streamline audits, manage policy updates, enforce vendor reviews, and run critical processes at scale.
No code required. No micromanagement. Just proof that work gets done right, every time.
Companies like Salesforce, Colliers, Drift, and Hartford Healthcare trust Process Street to eliminate busywork, improve operational visibility, and reduce compliance risk across the business. With native integrations, role-based access, audit trails, and ISO-aligned workflows, it is the platform that makes compliance a competitive advantage.
From onboarding to audits, Process Street is how high-stakes teams enforce standards, automate execution, and prove compliance by default.
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Consensus
Formulate a question to draw insights from academic research articles.
Consensus leverages artificial intelligence to extract answers from scholarly papers, with the most effective approach being to pose a specific inquiry.
Concentrate on topics that have been explored by researchers, spanning various fields, from biology to the social sciences.
Scientific research represents an unparalleled reservoir of knowledge and insights. However, the primary audience for this information often overlaps with those generating it. Our mission is to create tools that democratize access to scientific knowledge, fostering a more informed society and ultimately enhancing the quality of life for everyone.
Developed by experts in machine learning, science, and technology from some of the leading institutions globally, Consensus aims to bridge the gap between research and its application in real-world scenarios. This initiative is not just about accessing information; it's about transforming how we engage with and utilize scientific data for the betterment of society.
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GPT-Rosalind
GPT-Rosalind is an advanced reasoning model created by OpenAI, aimed at enhancing scientific exploration in fields like biology, drug development, and translational medicine. Tailored for workflows in life sciences, it assists researchers in managing extensive literature, experimental findings, and specialized databases to formulate and test innovative concepts. By integrating a profound understanding of disciplines such as chemistry, genomics, protein engineering, and disease biology with sophisticated tool-usage capabilities, it effectively interacts with scientific databases, examines experimental results, and facilitates intricate, multi-stage reasoning tasks. Its functionalities span evidence synthesis, hypothesis formulation, literature assessment, sequence analysis, and experimental design, empowering scientists to transition more swiftly from raw data to meaningful insights. Furthermore, GPT-Rosalind revolutionizes cumbersome, time-consuming research methodologies into streamlined, AI-enhanced workflows, ultimately fostering a more productive scientific environment. This model exemplifies the fusion of artificial intelligence with scientific inquiry, paving the way for groundbreaking discoveries.
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