Weavz: The Action Layer Between AI and Business Apps

Overview

Weavz is a platform built to connect artificial intelligence models with everyday business software. It acts as a secure bridge that allows AI agents to perform real tasks inside tools like Gmail, Slack, and CRMs. Instead of letting AI have unlimited access to a company's data, Weavz creates a controlled environment where these agents can work safely and within specific rules. This makes it easier for teams to use AI for automation without worrying about security risks.

Benefits

Weavz offers several key advantages for teams building AI solutions.

First, it provides strong security and control. The platform ensures that AI agents only have access to the specific apps and data they need. It uses scoped access to limit permissions and includes human gates that pause sensitive actions for a person to review before they happen. Every single action an agent takes is recorded in an audit trail, so teams can always see what is going on.

Second, it supports many different AI clients. Users can connect tools like ChatGPT, Claude, Cursor, and custom agents to the platform. This flexibility allows teams to choose the AI model that fits their workflow best.

Third, the platform simplifies complex integrations. It handles the technical setup of connecting apps and managing tokens, so developers do not have to build this infrastructure from scratch. This lets them focus on creating useful AI features instead of managing security protocols.

Use Cases

Weavz is designed for SaaS companies and AI development teams who want to automate workflows.

One common use case is automating customer support. An AI agent can use Weavz to read emails in Gmail or Zendesk, draft responses, and update tickets in a CRM like HubSpot. Because the access is scoped, the agent cannot accidentally delete important customer data or send messages to the wrong people.

Another use case is software development. Developers can use AI agents to write code, run tests, or manage tasks in project tools like Jira or GitHub. Weavz allows these agents to run code in a safe sandbox environment, preventing them from breaking the developer's local machine or accidentally deploying bad code to production.

Teams can also use Weavz for internal productivity. An agent could be set up to organize files in Google Drive, update status in Asana, or summarize meeting notes from Zoom. The platform ensures these tasks happen within the boundaries set by the team.

Pricing

Weavz offers a clear pricing structure with options for different needs.

The Free plan costs $0 per month. This tier is perfect for testing the platform and exploring how AI agents interact with apps. It allows users to build and test workflows without any cost.

The Pro plan is priced at $29 per month. This option is designed for teams that are ready to launch production workflows. It provides the necessary tools to run AI agents in a live business environment.

The Team plan costs $129 per month. This tier is best for larger teams shipping embedded workflow products. It includes features that support multiple users and more complex integrations.

All plans come with a 7-day trial for new accounts. Users can start using the service without needing to enter a credit card immediately.

Vibes

While specific customer testimonials are not available in the provided information, the platform has received positive attention for its approach to security. The emphasis on governance and audit trails addresses a major concern in the AI industry. Many developers are looking for ways to use AI safely, and Weavz provides a structured solution that fits this need. The ability to connect to over 1,000 business apps suggests strong utility for a wide range of industries.

Additional Information

Weavz positions itself as the action layer for the AI ecosystem. It supports a vast network of tools including communication platforms like Slack and Discord, productivity suites like Notion and Google Workspace, and development tools like GitHub and GitLab. The platform uses the Model Context Protocol to standardize how agents connect to these applications. This standardization helps ensure that integrations remain stable and secure as the AI landscape evolves.