Hops Opens Collaborative AI Workspace for Shared Teamwork

Hops announced on November 10, 2026 that its Collaborative AI workspace is open to everyone after more than 2,500 teams used it in early access. Built by Wrike founder Andrew Filev, the product puts AI into shared team documents, reusable work methods and custom apps rather than confining it to private chats.

Hops is built by For Good AI and led by Filev, its founder and CEO; the same team develops Zencoder’s AI coding agents. The company’s about page says Filev started Wrike, which was acquired for $2.25 billion, before building Zencoder.

Filev’s premise is that faster individual output has not translated into equivalent company-wide gains. He said his conversations with founders and executives of established companies usually produce estimates that their organizations are moving 10% to 20% faster than a year earlier, short of 2x and of what individual gains would suggest. His comparison was with earlier workplace computing: “The PC era didn’t pay off when PCs got faster. It paid off when they were networked. I believe AI will follow the same path.”

Shared Files, Reusable Skills and Custom Apps

In Hops, people and the agent work in shared chats, documents, tables and decks. When someone asks it to draft or revise material, it writes into the shared file so participants work from the same version instead of passing AI output between private chats and team documents. The product page describes live editing, with AI document edits visible in version history.

Teams can save a working method as a reusable skill containing instructions, context and format. The person who supplied the knowledge controls access and can update it, so colleagues can apply the same method without reconstructing the process each time. Hops illustrated this with its own marketing lead, who taught the system the company’s voice so a salesperson could request a customer email written directly into a shared document.

If a team lacks a tool, it can describe an application and have Hops build and run it inside the workspace using the same data and permissions. Hops says it connects to more than 2,000 apps, including CRM and cloud-storage tools, allowing the agent to work from connected team data.

Permissions and Usage-Based Pricing

Under the permission model, Hops will not use a file that the person making a request cannot open. If an answer draws on information others in the thread cannot see, the reply is held until the requester approves it. External sends and sensitive changes also wait for human approval, while administrators can control which teams use connected tools or make those tools read-only.

The pay-as-you-go plan includes 5,000 free credits at $0 per user per month, with top-ups of 20,000 credits for $20. Teams draw from a shared balance, administrators can set spending limits per teammate, and purchased credits remain valid for 12 months. Chat, documents and collaboration do not consume credits.

Consumption for an AI task depends on the model, the amount of information read, tools used, number of steps and output length. Enterprise adds the full integration library, SSO, SCIM and audit controls, security review and custom terms, and the option to bring an API key. Hops says it does not use customer data to train models.

Early Use and Internal Deployment

Hops reports that 30 engineers and a fleet of AI agents rebuilt the collaboration stack in a year. The company now runs its own CRM, support desk, IT requests and project tracking in the product, and employees can ask about leave policy and receive an answer linked to the handbook. The setup illustrates Hops’s ambition to coordinate routine operations alongside drafting work.

Discern Security CEO Sai Venkataraman said in the launch release that his sales and marketing teams use Hops for pipeline reviews, event and website-prospect follow-ups, and campaigns, and that the product replaced tools including Slack and Jira at his company.

The public opening turns Hops’s early-access period into a broader adoption test. Shared skills and permission controls are intended to make AI output reusable without widening access to underlying information, while credit pricing removes per-seat charges and ties heavier automation to consumption. The central question is whether teams will move enough recurring work into the workspace for those shared methods to improve the company-level gains Filev described as lagging.