SciSure is a platform for managing laboratory operations end-to-end, built for scientific organizations. It brings together ELN, LIMS, and Health & Safety tools so teams can document experiments, track samples, manage chemical inventory, and maintain compliance workflows that are structured and audit-ready.
By replacing fragmented systems with a single governed platform, SciSure helps organizations improve reproducibility, gain clearer operational visibility, and scale lab operations with less risk.
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StackAI is an enterprise AI automation platform built to help organizations create end-to-end internal tools and processes with AI agents. Unlike point solutions or one-off chatbots, StackAI provides a single platform where enterprises can design, deploy, and govern AI workflows in a secure, compliant, and fully controlled environment.
Using its visual workflow builder, teams can map entire processes — from data intake and enrichment to decision-making, reporting, and audit trails. Enterprise knowledge bases such as SharePoint, Confluence, Notion, Google Drive, and internal databases can be connected directly, with features for version control, citations, and permissioning to keep information reliable and protected.
AI agents can be deployed in multiple ways: as a chat assistant embedded in daily workflows, an advanced form for structured document-heavy tasks, or an API endpoint connected into existing tools. StackAI integrates natively with Slack, Teams, Salesforce, HubSpot, ServiceNow, Airtable, and more.
Security and compliance are embedded at every layer. The platform supports SSO (Okta, Azure AD, Google), role-based access control, audit logs, data residency, and PII masking. Enterprises can monitor usage, apply cost controls, and test workflows with guardrails and evaluations before production.
StackAI also offers flexible model routing, enabling teams to choose between OpenAI, Anthropic, Google, or local LLMs, with advanced settings to fine-tune parameters and ensure consistent, accurate outputs.
A growing template library speeds deployment with pre-built solutions for Contract Analysis, Support Desk Automation, RFP Response, Investment Memo Generation, and InfoSec Questionnaires.
By replacing fragmented processes with secure, AI-driven workflows, StackAI helps enterprises cut manual work, accelerate decision-making, and empower non-technical teams to build automation that scales across the organization.
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Gemini for Science
Gemini for Science revolutionizes scientific discovery by providing AI-powered tools and resources tailored to enhance scientific projects. By combining experimental tools from Google Labs with the scientific workflows available in Google Antigravity, it seeks to accelerate research efforts, enhance analytical capabilities, and empower researchers to explore the future of AI-driven scientific inquiry. The Literature Insights feature aggregates scholarly articles to identify new research opportunities, generate robust research outputs, and transform paper data into organized tables connected to original sources. Simultaneously, Hypothesis Generation utilizes a multi-agent strategy that mimics the scientific method, enabling it to identify gaps in knowledge, propose promising research paths, and outline testable research methodologies that have the potential to yield significant advancements. Furthermore, Computational Discovery aids researchers in pinpointing models and algorithms through a smart research engine that develops and assesses code variations based on user-defined optimization goals, thus further streamlining the research workflow. Overall, these cutting-edge tools are designed not just to enhance the efficiency of scientific research but also to fundamentally change the way it is perceived and executed. The integration of these advanced features signifies a major leap forward in the collaboration between AI and scientific exploration.
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FutureHouse
FutureHouse is a nonprofit research entity focused on leveraging artificial intelligence to propel advancements in scientific exploration, particularly in biology and other complex fields. This pioneering laboratory features sophisticated AI agents designed to assist researchers by streamlining various stages of the research workflow. Notably, FutureHouse is adept at extracting and synthesizing information from scientific literature, achieving outstanding results in evaluations such as the RAG-QA Arena's science benchmark. Through its innovative agent-based approach, it promotes continuous refinement of queries, re-ranking of language models, contextual summarization, and in-depth exploration of document citations to enhance the accuracy of information retrieval. Additionally, FutureHouse offers a comprehensive framework for training language agents to tackle challenging scientific problems, enabling these agents to perform tasks that include protein engineering, literature summarization, and molecular cloning. To further substantiate its effectiveness, the organization has introduced the LAB-Bench benchmark, which assesses language models on a variety of biology-related tasks, such as information extraction and database retrieval, thereby enriching the scientific community. By fostering collaboration between scientists and AI experts, FutureHouse not only amplifies research potential but also drives the evolution of knowledge in the scientific arena. This commitment to interdisciplinary partnership is key to overcoming the challenges faced in modern scientific inquiry.
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