Imagine being able to privately express your unstructured needs and ambitions—“I’m exploring ways to decentralize access to research infrastructure,” or “Looking for someone who’s worked on interoperability between agent networks.” Instead of shouting into the void or waiting for the right person to scroll past, your intent is picked up by autonomous agents competing to deliver the best possible match.
You’ve probably been there, looking for someone, not just anyone, but someone who gets it. Maybe you’re building something new and don’t want to do it alone. Maybe your idea doesn’t even have a name yet, but you know it needs others to take shape.
You’ve got places to post, share, search, and shout. Too many, really. But despite the flood of tools, you’re still stuck trying to meet someone who actually gets what you're doing.
It’s not that the people you’re looking for don’t exist. They do. It’s that everything about the way discovery works online makes it harder than it should be to find them. You’re constantly being pushed to make things public, polished, and legible, taught to market what you’re looking for like a product, to post in the right channels, use the right keywords, catch the right person’s attention. And if your timing’s off or your message isn’t clear enough, you get silence. No response. No insight. Just another empty loop where you’re not sure if no one saw it, or if they saw it and didn’t care.
So you try again. Or give up. Or settle for a partial match and hope it works out. Over time, you start repeating yourself, rewriting the same paragraph for different people, reposting the same message in different groups, reframing the same problem with different jargon. And when something finally connects, it often feels like luck. Like you just happened to be visible at the right moment.
This is the system we pretend works: discovery as noise, identity as content, and visibility as a full-time job.
But what if that’s the part that’s broken?
What if being understood didn’t mean constantly explaining yourself? Not just how do we find better people or stronger opportunities, but how do we do that without putting the entire burden on the person doing the looking? What if the right people could find you just because your intent naturally aligned with theirs?
We’re building a protocol for discovery —connecting people, knowledge, and opportunities through a network of autonomous agents. Users define their specific “intents” (for example, finding a co-founder, seeking a date, or hiring a particular skill). Independent “Broker Agents” compete to fulfill these intents by staking tokens on their match recommendations, enabling highly relevant intros without exposing data. If both parties accept the match (double opt-in), the broker and its backers earn rewards, if not, they lose some of their stake. Private data adds depth to every intent, letting agents match on things you haven’t said out loud yet—surfacing collaborators, builders, or ideas before you're actively looking. This architecture allows permissionless value creation for private data—agents or users can leverage it to create better connections.
Private, Intent-Driven Discovery
A user-owned data layer coupled with a confidential compute environment ensures that personal information remains confidential while still enriching every intent to identify the right match. By localizing each user’s intents to a secure enclave, the protocol opens the door to domain-specific agents that drive more meaningful connections without leaking data. This approach allows Context Broker Agents to detect opportunities based on signals you haven’t explicitly shared, leading to connections that are both deeper and more contextually relevant.
Competitive Agent Discovery for Better Connections:
Broker agents actively compete by staking tokens on their match recommendations. Only when both parties confirm the match (double opt-in) does the successful agent earn its rewards, fostering a self-reinforcing cycle of innovation that continuously elevates the quality of outcomes. By directly aligning economic incentives with the success of a connection, the protocol cuts through the noise of superficial recommendations, yielding results that are orders of magnitude more precise and impactful than conventional systems.
| Stage | Model | Payer | Example Use Case | Goal / Outcome |
|---|---|---|---|---|
| 1. Community Onboarding | Potential: B2B Subscription | Community / DAO / Accelerator | Ambient discovery inside Slack, Discord, web app | Identify strongest demand, validate willingness to pay |
| 2. Global Network | Per-Outcome Fee (10–15%) | User / Agent / Ecosystem | Hiring, Fundraising, Partner Matching | Align monetization directly with delivered value |
| 3. Discovery Economies | Protocol Fee (1–3%) | Community using Index infra | Local marketplaces & tokenized discovery | Scale with ecosystem adoption, Stripe-style take rate |
Future of AI interaction is moving toward generative AI interfaces. Soon, the everyday tools people rely on—chat apps, copilots, assistants—will connect them to agents that act on their behalf, routing intents seamlessly across platforms, protocols, and networks. Index is designed to serve as the protocol layer that powers this intent-driven discovery.
But to claim that role, we must demonstrate its value today. Our go-to-market strategy focuses on two core objectives: