AI Squared

AI Squared provides software to simplify the integration of AI models directly into existing business applications like CRMs and ERPs. The platform enables teams to quickly deploy, experiment with, and scale AI solutions while capturing performance feedback. This streamlined process accelerates AI adoption, boosts operational efficiency, and improves decision-making across the organization.

Traction 10/10

Washington, United States · HQ

Founded 2019735K+ followers

Artificial Intelligence
Data & Analytics
Enterprise Software
Software Only

Updated 2 days ago

Funding

Raised to date

$13.8MRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.

Funding rounds are not available yet.

Founders

2 founders

Benjamin Harvey

CEO

Keenan Thomas

Founder

Product

Problem

Many organizations struggle to integrate AI-driven insights into their daily business operations, leading to underutilization of machine learning models and a disconnect between data science teams and business users. This gap hinders real-time decision-making and limits the potential business impact of AI investments.

Solution

AI Squared provides an AI integration platform designed to embed actionable data insights directly into existing business applications, enabling real-time decision-making. The platform facilitates a feedback loop between AI consumers and AI developers, fostering collaboration and optimizing machine learning model performance. By delivering tailored, AI-derived intelligence to business teams within their familiar workflows, AI Squared helps organizations operationalize AI and drive business value. The platform aims to bridge the gap between data scientists and business users, ensuring that AI insights are readily accessible and impactful.

Target Audience

AI Squared targets organizations seeking to improve the integration of AI into their business operations, particularly those with existing investments in data science and machine learning.

Features

Direct embedding of AI insights into front-end business applications
Facilitation of a feedback loop between AI consumers and AI developers
Automation of tailored AI intelligence delivery to business teams
Optimization of workflows and decision-making processes
Enhancement of collaboration between data scientists and business users
Fine-tuning of machine learning model performance