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AI didn't break recruiting.
It exposed what was already broken.

Every wave of recruiting technology has optimized for speed. None of it has optimized for context.

We're the context layer between your business strategy and your next hire.

Language Model Concept

We think about recruiting the way engineers think about AI systems.

AI engineers learned this first: a model needs context — a system prompt, defined tools, a clear picture of a good output — before it can perform. Skip that layer and the system still runs. It just produces answers no one can trust.

Without context layer

Sourcing starts immediately. Candidates evaluated against criteria no one agreed on. Four interview rounds before realizing two interviewers are looking for different things.

With context layer

Role defined before sourcing. Stakeholders aligned on 90-day outcomes. Every interviewer evaluating against the same rubric. Faster decision, fewer re-opens.

What MCP is to AI agents, we are to recruiting.
MCP — Model Context Protocol — is the layer that gives AI models the structured context they need to perform. Without it, models hallucinate. With it, they're precise. Hiring works the same way.

In AI systems

  • System prompt defines the model's role and constraints

  • Tool definitions specify what the agent can connect to

  • Context window holds the information the model reasons from

  • Output schema defines what a good response looks like

In our recruiting process

  • Role brief defines the hire's mandate and constraints

  • Stakeholder alignment clarifies who this role connects to and how

  • Business context — stage, GTM motion, team gaps — informs sourcing

  • Hiring rubric defines what a strong candidate looks like

AI changed the economics of sourcing overnight. What used to take a recruiter two weeks now takes minutes. That's genuinely useful. But it exposed a problem that was always there: most companies don't actually know what they're hiring for.

They have a job title, a copy-pasted description, and three stakeholders with three different definitions of success. More throughput into an undefined role doesn't solve that. It amplifies it.

Every AI system needs a context layer.
So does your hiring process.

We're the protocol between your business strategy and your next hire.

Let's define the role before you source.

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