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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Charlie
Charlie serves as a committed AI scientific assistant, tailored for researchers and laboratories with an emphasis on biomedical research, aimed at accelerating scientific progress by delivering precise and well-sourced information. It supports users in conducting literature reviews, analyzing scientific documents, organizing research data, and optimizing R&D workflows while prioritizing traceability in every response provided. Researchers can ask questions in plain language and obtain information drawn directly from scientific literature, complete with precise citations that validate each source. Charlie can efficiently search through numerous PDFs at once, synthesizing information, comparing various studies, understanding scientific contexts, and allowing users to focus more on breakthroughs instead of laborious document examinations. Additionally, its research workspace includes libraries, project management features, note-taking tools, shared collections, PDF reading functionalities, highlighting options, annotation capabilities, and tools for team collaboration, ensuring that critical quotes, references, and insights are meticulously organized across various devices. This all-encompassing strategy not only increases research productivity but also creates a supportive atmosphere for collaborative scientific exploration, ultimately empowering researchers to achieve their goals more effectively. By streamlining the administrative aspects of research, Charlie enables scientists to devote more time to innovative thinking and experimentation.
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Evo 2
Evo 2 is an advanced genomic foundation model that excels in predicting and creating tasks associated with DNA, RNA, and proteins. Utilizing a sophisticated deep learning architecture, it models biological sequences with precision down to single-nucleotide accuracy, demonstrating remarkable scalability in both computational and memory resources as context length expands. The model has been trained on an impressive 40 billion parameters and can handle a context length of 1 megabase, analyzing an immense dataset of over 9 trillion nucleotides derived from diverse eukaryotic and prokaryotic genomes. This extensive training enables Evo 2 to perform zero-shot function predictions across a range of biological types, including DNA, RNA, and proteins, while also generating novel sequences that adhere to plausible genomic frameworks. Its robust capabilities have been highlighted in applications such as the design of efficient CRISPR systems and the identification of potentially disease-causing mutations in human genes. Additionally, Evo 2 is accessible to the public via Arc's GitHub repository and is integrated into the NVIDIA BioNeMo framework, which significantly enhances its availability to researchers and developers. This integration not only broadens the model's reach but also represents a pivotal advancement in the fields of genomic modeling and analysis, paving the way for future innovations in biotechnology.
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