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Driving scalable growth with OpenAI o3, GPT-4.1, and CUA
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Driving scalable growth with OpenAI o3, GPT-4.1, and CUA

Unify⁠(opens in a new window) is a system of action built to reach the right customer, at the right time, with the right message, at scale. The platform enables this through intelligent prospecting, hyper-personalized messaging, and a repeatable, always-on workflow that automates what’s working.

Unify built its system with AI at its foundation. The team believes sales growth should be treated as an engineering problem: one that is observable, measurable, and fast to iterate. Unify used OpenAI o3, GPT‑4.1, and the Computer-Using Agent (CUA), along with continuous model benchmarking, to design a system that helps go-to-market (GTM) teams generate millions in pipeline with a fraction of the manual work, and which now generates 30% of its pipeline.

“There’s something really special about human-to-human interactions that isn’t going away,” says co-founder Connor Heggie. “We’re using AI to automate the busywork and give teams the leverage to spend more time where it matters most: talking to customers and making timely decisions.”

Turning go-to-market into a search problem and solving it with AI

The job of GTM teams is to find the companies and people who have a problem your product can uniquely solve and get in front of them. While many consider this an acquisition problem, Unify thinks of this as a search problem over huge amounts of unstructured, semantically rich data.

Historically, solving this problem meant hiring large teams of sellers to manually research prospects, comb through websites, and write outreach from scratch. With OpenAI’s models and Unify’s agentic architecture, teams can now run this search at scale with consistency, context, and speed.

Unify’s AI product stack includes three core components:

Pairing OpenAI o3, GPT‑4.1, and CUA with the right tasks

Similarly, GPT‑4.1 and CUA unlocked new research and planning tasks that had not been possible before, and were integrated directly into the product’s decisioning layer.

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