What is Monterey AI?

Monterey AI is a copilot for product insights that allows teams to aggregate, analyze, and monitor millions of qualitative data in a smarter, faster, and scalable way. It is built by AI and data leads from Uber, Scale, and MIT, and is designed to empower next-gen companies betting on AI as their competitive edge.

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How to use Monterey AI?

To use Monterey AI, you can start by booking a demo to get a better understanding of its capabilities. Once you have a demo, you can integrate Monterey AI into your existing tools instantly, allowing you to gather accurate and real-time data pulled directly from thousands of existing sources. Monterey AI also offers custom models that continuously learn and improve over time, providing innovative and robust solutions for companies. You can also scale customer obsession by setting up feedback infrastructure, monitoring user voice to identify risks and opportunities, and triaging reports to the right team for action. Finally, Monterey AI allows you to leverage data to make evidence-based decisions in product development and deeply integrate with your engineering platform for precise communication and collaboration.


Monterey AI's Core Features

Aggregating and analyzing millions of qualitative data
Real-time and accurate data pulled from existing sources
Custom models that continuously learn and improve
Scalable infrastructure for processing millions of data points
Privacy and security first approach with industry-level compliance
Setting up feedback infrastructure
Monitoring user voice for risk identification and opportunity uncovering
Deep integration with engineering platform for evidence-based decision making
Support for 85+ global languages
Triage reports to the right team for action

Monterey AI's Use Cases

Gaining insights from user feedback and improving product development
Identifying trends and user needs in real-time
Scaling customer obsession without scaling the team
Making evidence-based decisions in product development
Collaborating and communicating effectively with engineering and marketing teams
Shortening the distances between users and the development team
Saving time and effort in triaging and prioritizing areas of improvement

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