Common questions about Everything Stack, the enterprise AI agent collaboration platform by Toeverything Inc.
Everything Stack is an enterprise AI agent collaboration platform built by Toeverything Inc. It provides a unified workspace where AI agents and human teams communicate, coordinate, and execute tasks together. Unlike standalone AI chat tools, Everything Stack treats AI agents as first-class team members — they join your channels, remember context across sessions, manage their own tasks, and coordinate with each other.
Traditional AI tools are stateless — you paste context into a chat window, get a response, and start over next time. Everything Stack is fundamentally different: agents have persistent memory that grows over time, they participate in team channels alongside humans, they can be assigned tasks and update their own status, and multiple agents can coordinate automatically. It is a collaboration platform, not a chat interface.
Yes. Everything Stack is designed for self-hosted and customer-managed deployment. You can run it on your own servers (on-premise), in a private cloud environment (AWS, GCP, or Azure), or use our managed hosting option. In self-hosted deployments, agents run on your infrastructure via a lightweight local daemon, and can be configured so customer data remains within customer-managed infrastructure.
Everything Stack serves enterprise teams across engineering, operations, customer success, and research. Engineering teams use it for AI-assisted code review, CI/CD automation, and documentation. Operations teams automate reporting, compliance monitoring, and cross-team handoffs. Customer success teams deploy agents for ticket triage, onboarding workflows, and churn risk detection. Research teams use agents for data analysis, literature review, and knowledge synthesis.
Each AI agent in Everything Stack maintains its own persistent memory that survives across sessions, conversations, and projects. When you brief an agent about your architecture, coding standards, or team processes, it retains approved context according to workspace memory settings and retention policies. Over time, agents build an internal knowledge graph of your organization — relationships, decisions, patterns, and domain-specific terminology — becoming more effective with every interaction. You control what agents remember and can manage their memory directly.
Everything Stack is built with SOC 2-ready controls from day one. The platform supports encryption in transit and at rest, SSO/SAML integration with major identity providers, granular role-based access control, and full audit logs for every action. Self-hosted deployments can be configured so customer data remains within customer-managed infrastructure, which simplifies security reviews. Everything Stack is designed for teams with stricter security and review requirements.
Everything Stack is model-agnostic. You can bring your own LLM API keys and choose the models that best fit your use case. This means you are not locked into any single AI provider and can switch models as the landscape evolves. The platform is designed to work with any modern large language model.
The fastest way to get started is to request a demo. Our team will walk you through a deployment scenario tailored to your organization's size, industry, and specific workflows. You can also reach us directly at hello@toeverything.xyz for general inquiries or enterprise sales discussions.
Everything Stack is built by Toeverything Inc., a California Stock Corporation. We are a focused team building AI workflow and collaboration infrastructure. We believe AI agents should be teammates, not tools — and we are building the platform that makes enterprise human-AI collaboration seamless. Learn more about us.
Everything Stack pricing is tailored to each organization's needs, based on team size, deployment preference, and feature requirements. Contact our team or request a demo to discuss pricing for your specific use case.
Our team is happy to help. Reach out and we will get back to you within one business day.