Unify boosts sales pipeline 30% with targeted AI models
Unify scales outreach by matching OpenAI models to specific tasks, generating 30% more pipeline for GTM teams
Unify, a sales automation platform, has achieved a 30% increase in pipeline generation by strategically deploying different OpenAI models for specific go-to-market (GTM) tasks. The company treats sales growth as an engineering challenge, combining OpenAI o3, GPT-4.1, and Computer-Using Agent (CUA) technologies in an agentic architecture.
AI-Powered GTM Transformation
- Observation Model: Uses OpenAI o3 to continuously monitor market signals like new hires or tech stack changes
- Research Agent: Leverages GPT-4.1 for planning and CUA for dynamic browsing to answer complex prospect questions
- Copywriting System: Employs GPT-4o to transform research into hyper-personalized email drafts
"We're using AI to automate busywork so teams can focus on human conversations," said co-founder Connor Heggie. The platform has shifted GTM from manual prospecting to an automated search problem across unstructured data.
Model Evaluation Strategy
The team developed unique evaluation metrics focusing on:
- Reasoning quality for early-stage signal detection
- Adaptability across diverse use cases
- Structured outputs for synthesis tasks
"OpenAI o3 stood out for its 2-3 turn reasoning capabilities," noted Software Engineer Kunal Rai. The system now powers 30% of Unify's own pipeline and supports hundreds of millions in customer pipeline annually.
Future Outlook
Unify plans to:
- Expand agentic systems for more growth workflows
- Increase visibility and leverage for GTM teams
- Maintain focus on product-market fit as the core growth driver
"Success should be determined by product quality and customer fit," Heggie emphasized, highlighting the company's mission to let AI handle operational tasks while humans focus on strategic relationships.
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About the Author

Dr. Sarah Chen
AI Research Expert
A seasoned AI expert with 15 years of research experience, formerly worked at Stanford AI Lab for 8 years, specializing in machine learning and natural language processing. Currently serves as technical advisor for multiple AI companies and regularly contributes AI technology analysis articles to authoritative media like MIT Technology Review.