What is an AI module?
An AI module is a self-contained component that performs a specific task within a larger system. In the context of AI and data workflows, modules are like building blocks—you can combine them to create more complex solutions without starting from scratch each time.
Each AI module focuses on doing one job well, whether that's processing data, running an AI model, or visualising results.
At MosaiQ, AI modules are tailored to the needs of quantitative researchers and finance professionals. They’re designed to help users rapidly build, test, and iterate on investment strategies.
Each module is focused on a single, clearly defined function—such as data ingestion, feature engineering, signal generation, model training, or backtesting.
How to access AI modules on MosaiQ?
Modules can be accessed from the Homepage or from the MosaiQ Assistant.
From the Homepage
Click on the tab "AI modules" next to Projects
This is where your AI modules are. At the beginning the page will be empty
Access the AI Library to search for the module that most serves your need. Public AI modules are ready for use or, for specific needs, you can contact us to discuss building a bespoke AI module
Or create your own AI Modules by clicking on "New +"
From the MosaiQ Assistant
Click on the MosaiQ Assistant icon on the right side of the screen
Click on the "Modules" button on the bottom right corner
Select the required AI module and continue the conversation in the MosaiQ Assistant chat
Why integrating AI Modules to your workflow?
Designed to enhanced your workflow
AI modules are built with the knowledge professionals in mind—focused on the needs of quantitative researchers, data analysts, and strategy teams.
Flexible & collaborative
AI modules make it easier to collaborate across teams by standardising components, enabling clear documentation, and allowing for easy handoff between research and production.
For example, a user might start with a Data Ingestion module to pull in market data, link it to a Signal Generation module to identify trading opportunities, and then run everything through a Backtesting module to see how the strategy would have performed historically.
Further documentation
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