Our AI Workers are built on latest-generation language models, a RAG architecture for knowledge access, and persistent memory that lets them learn from your processes.
This combination delivers agents that are reliable, high-performing and able to run complex tasks end to end.
The RAG (Retrieval-Augmented Generation) architecture lets each worker access your documents, knowledge bases and business data at the moment of acting. Instead of relying only on what it learned during training, the worker retrieves relevant context from your own sources (Notion, Google Drive, Confluence, Slack…), which drastically reduces hallucinations and keeps responses grounded in your reality.
Persistent memory keeps the history of your interactions, your preferences, your templates and your processes. A worker that learned your brand voice last week still remembers it today. This memory is isolated per tenant: your data is never shared across organizations.
On orchestration, workers chain together in multi-agent workflows: a marketing worker can brief a design worker, which produces the visuals, then an SEO worker optimizes everything. This coordination relies on open standards like the Model Context Protocol (MCP), which standardizes communication between AI agents and external tools.
On security, each worker runs in an isolated environment with granular permissions. Tool calls are logged, enabling a full audit of the actions taken. Workers can only access the resources you have explicitly granted, and any sensitive action can require human approval based on the rules you define.
Our workers run on the most advanced language models on the market, including the GPT, Claude and Gemini families. We continuously evaluate new models on internal benchmarks covering reasoning quality, tool-calling accuracy, and safety. Workers automatically switch to the best available model for each type of task.