Matagi offers autonomous AI agents that act as persistent virtual employees, handling end‑to‑end tasks across 3,000+ SaaS integrations without requiring API keys or custom code. Users simply describe the desired outcome, and the agents plan, execute, and monitor work 24/7, with pre‑trained agents available for outreach, support, research, and operations, all billed via a flat subscription plus transparent usage fees.
Funding
Funding not disclosed
Founders
Product
Problem
Businesses generate more ideas than they can execute because teams are constrained by limited time, headcount, and technical capacity. Existing AI tools act as assistants that require constant prompting and do not take ownership of tasks, leaving the execution bottleneck unresolved.
Solution
Matagi provides autonomous AI agents that function as persistent virtual employees. Users describe the desired outcome, and the agents plan, act, remember context, and coordinate across the 3,000+ integrated SaaS tools without needing API keys or custom code. Agents run continuously on Matagi’s servers, handling overnight jobs, monitoring for changes, and notifying humans only when intervention is required. Pre‑trained agents are available for common functions such as outbound outreach, customer support, research, and operations, allowing teams to deploy a working AI worker within minutes. The platform records usage and costs transparently, passing compute, inference, and data expenses through at exact provider prices.
Target Audience
Matagi targets small to medium businesses, product teams, and operations groups that need to automate repetitive workflows and scale execution without hiring additional staff or building custom integrations.
Features
- Over 3,000 out‑of‑the‑box integrations; agents can also create ad‑hoc connections via any open API or MCP
- Always‑on agents hosted on Matagi’s infrastructure, operating 24/7 and autonomously picking up pending tasks
- Pre‑trained agents for outbound, support, research, and ops that can be customized and combined
- Contextual memory and continuous learning from feedback to improve task execution over time
- Simple declarative task description interface—no workflow engineering or prompt engineering required
- Transparent cost model with flat platform subscription and pass‑through compute/inference/data pricing