Who is the client?
Colleva is an AI-powered platform that delivers avatar-led, interactive coaching and professional development at scale.
The product enables organizations to simulate real business interactions using advanced AI avatars, allowing professionals to practice sales conversations, leadership scenarios, presentations, and feedback sessions while receiving structured, data-driven insights.
Colleva supports teams across sales enablement and learning and development by providing AI-native experiences that replicate high-stakes, real-world interactions.
How Colleva met us
Colleva's founder Michael discovered Matteo through LinkedIn, where Matteo was publishing content around go-to-market systems, outbound strategy, and pipeline engineering.
After several conversations on LinkedIn, they scheduled a call to discuss how to build a structured outbound system that could support both learning and revenue generation. From that first call, they decided to work together on engineering a LinkedIn-led outbound motion designed to validate segments and generate qualified pipeline.
Context
Colleva was at a go-to-market stage where learning speed was critical.
Growth depended on quickly understanding which segments responded to the product, which roles converted into buyers, and how to turn that learning into predictable pipeline.
They needed to test multiple industries, ICPs, and buyer personas while simultaneously generating pipeline to support commercial traction. This required a system that allowed experimentation without sacrificing structure, visibility, or control.
What was the problem?
1. Product market fit validation
Colleva needed to identify which segments, roles, and messages consistently converted into sales conversations and pipeline.
2. Lack of a structured outbound system
A previous outbound attempt failed due to the absence of a clear system connecting targeting, messaging, execution, follow-up, and measurement.
3. Lack of reporting and CRM connection
There was no reliable way to connect LinkedIn activity to pipeline outcomes, which made learning slow and scaling difficult.