Beyond “AI Safety”: How Openlayer Crafted Their Enterprise Marketing Message
In the hyper-competitive AI infrastructure market, everyone claims to make AI “safe” and “reliable.” For Openlayer, breaking through the noise meant completely rethinking how they communicated their value proposition.
In a recent episode of Category Visionaries, CEO Gabriel Bayomi shares how they evolved their messaging from abstract promises to concrete value.
The Problem with Abstract AI Marketing
Coming from technical backgrounds at Apple, the team initially framed their solution in broad, technical terms. “I think a lot of the pitches for AI, like LM ops or ML ops companies do sound a little bit similar,” Gabriel admits. “Like, hey, I want to make this safe. I want to make this performant. I want to make sure that you’re not going to hallucinate.”
The challenge wasn’t just differentiation – it was connecting with enterprise buyers who cared more about business outcomes than technical specifications.
Finding Concrete Value Propositions
The breakthrough came when they started focusing on specific, tangible benefits. “Instead of going to the abstract idea space of like, we make your AI safe, we try to market things more directly. For example, hey, get alerts when your LLM fails,” Gabriel explains.
This shift from abstract to concrete wasn’t just about messaging – it reflected a deeper understanding of enterprise needs. Rather than promising general “safety,” they focused on specific pain points that resonated with buyers.
Building Enterprise Trust Through Specificity
The team developed a framework for enterprise credibility built on what Gabriel calls “the team, the market, and the proof.” This meant demonstrating:
- “A strong team that can execute”
- That “the market is big enough so it makes sense to execute on this market”
- “The progress that you have made that shows that this team can perform in this market”
From Technical Features to Business Outcomes
Their experience at Apple helped them understand the technical problems deeply. “Know building models was not the hard part,” Gabriel shares. “The hard part was everything around it. How do you test to make sure it’s good and safe? How do you monitor it in production to make sure the performance is as good as you expect?”
But technical understanding wasn’t enough. They had to translate these insights into business value. Instead of talking about model performance in technical terms, they focused on business impacts like preventing customer-facing failures and maintaining service quality.
Evolving the Vision While Staying Concrete
Even as they aim to become “the guardrail of the AI revolution,” they keep their messaging grounded in specific value. Gabriel envisions a future where companies “have confidence on what are the things that my model does well, what are the things that my model does not do well, and what are the remedies that I’m using to fix that in particular.”
This balance of ambitious vision and concrete value has helped them land major enterprise customers like eBay while maintaining credibility in the technical community.
Tactical Lessons for Deep Tech Marketing
For technical founders marketing deep tech to enterprises, Openlayer’s journey suggests three key principles:
- Start with specific problems, not abstract solutions
- Translate technical capabilities into business outcomes
- Build credibility through concrete proof points, not theoretical benefits
The goal isn’t to dumb down the technology, but to make its value immediately apparent to business decision-makers. As Gabriel puts it, they want enterprises to think “okay, what’s our Openlayer stack? To be able to mitigate all the issues that are going to come naturally from deploying it.”
In an industry where everyone promises to make AI safe, Openlayer has found success by focusing on making those promises concrete, measurable, and immediately valuable to enterprise customers.