Openlayer GTM brain

category

market profile

• text: Enterprises need to evaluate AI before release, observe live behavior and maintain controls as models and agents spread across teams.
• text: The company grew from testing and production monitoring into governance and automated compliance as generative and agentic AI expanded.

personas

• title: Head of AI engineering
  pains:

  • Must catch hallucinations and unauthorized tool calls before users encounter them
  • Needs visibility into production quality, latency and spend across AI projects
• title: AI governance and risk leader
  pains:

  • AI systems and owners are scattered across departments
  • Preparing evidence for policy and regulatory reviews is time consuming
• title: Machine learning engineer
  pains:

  • Teams rebuild evaluation infrastructure and test libraries for each project
  • Unanticipated real-world edge cases can undermine model performance

icp

• text: Large enterprises operating multiple AI applications, particularly regulated organizations in finance, insurance, healthcare and telecom.
• text: Smaller technical teams can start with one member and five projects, while larger organizations require governance, SSO and private deployment.

message pillars

• text: Unify testing, monitoring and policy enforcement.
• text: Make compliance evidence part of day-to-day AI work.
• text: Build systems that deserve trust.

objections

• objection: We already have monitoring tools.
  response: Compare existing coverage with the connected workflow for release testing, production traces and policy evidence.
• objection: Sensitive data cannot leave our environment.
  response: Enterprise lists on-premise deployment; validate data handling and deployment requirements with the vendor.
• objection: We need proof before an enterprise purchase.
  response: Use the free Basic plan to evaluate a real project, then scope governance and security needs.

competitors

• name: Arize AI
  why: AI observability and evaluation tools address overlapping monitoring and model quality needs.
• name: Langfuse
  why: LLM tracing and evaluation offer an alternative for teams monitoring AI applications.
• name: Scorecard
  why: AI agent evaluation platform competes for testing workflows.

influencers

• name: Gabriel Bayomi
  url: https://openlayer.com/about
  why: Co-founder and CEO representing the company's trust and governance thesis.
• name: Rishab Ramanathan
  url: https://openlayer.com/about
  why: Co-founder and CTO responsible for technical leadership.

keywords

• AI governance platform
• LLM evaluation tools
• AI observability
• AI model monitoring
• AI compliance software
• agent guardrails
• AI audit evidence

voice

• Trust-centered and direct
• Technical and evidence-oriented
• Enterprise risk-aware

signals

• text: An organization is moving multiple AI agents into production and needs to evaluate tool calls and quality.
• text: A regulated enterprise is formalizing AI ownership, audit trails or governance controls.

stack

• API
• SDK
• CI/CD

pricing

motion

channels

• SEO
• case studies
• sales outreach
• Discord

confidence

Founders

• name: Gabriel Bayomi Tinoco Kalejaiye
  title: Founder
  role: Founder
  bio: I’m a Brazilian engineer and the co-founder and CEO of Openlayer. We help enterprises test, monitor, and govern AI systems, from traditional machine learning to LLMs and agents, with controls that span development and production. Before Openlayer, I was a Machine Learning Engineer at Apple, where I worked on Siri, combining multimodal signals to improve natural language understanding. I earned my master’s in Computer Science at Carnegie Mellon, where I worked on Amazon’s Alexa Prize project.
• name: Vikas Nair
  title: Founder
  role: Founder
  bio: Co-founder of Openlayer (S21). Previously, built future AI/ML-powered experiences at Apple.
• name: Rishab Ramanathan
  title: Founder
  role: Founder
  bio: Co-Founder of Openlayer