DeepVista
It's Time for Relational Intelligence
Jing Conan Wang, Founder of DeepVista · Former Google Brain researcher
AI can reason about the world. But it often lacks the relationship history needed to act in it.
Consider a simple question: Who could introduce me to this investor?
Answering well requires more than finding a mutual connection. It requires understanding how those people know each other, whether they have worked together, how recently they have interacted, and whether an introduction would make sense.
A visible connection is only a small part of that story. The history behind it is what makes the connection useful.
We call this relational context: the accumulated history behind human relationships—how people know each other, what they have done together, and how those relationships change over time.
For AI to help us navigate real-world opportunities, it needs access to that context.
More reasoning power alone cannot recover context that was never captured. And retrieving individual records is only part of the challenge: an AI system must connect those records, preserve their history, and revise its understanding as new evidence arrives.
Relational context needs to persist beyond any single conversation with a model.
Relationships shape what becomes possible
Human relationships carry information that changes which actions are relevant, credible, and timely.
Relational contextAn employee previously worked closely with a candidate and knows the quality of their work.
Action it enablesAsk for an informed referral.
OpportunityA promising hire.
Relational contextA mutual contact knows a founder’s work and has an active relationship with an investor whose thesis fits.
Action it enablesRequest a credible introduction.
OpportunityA relevant investor conversation.
Relational contextA former customer champion joins a company facing a problem your product helped them solve.
Action it enablesReconnect with shared history and a relevant reason.
OpportunityA new customer conversation.
In each case, knowing who someone is provides a starting point. Understanding the relationships around them helps determine what to do next.
This context also changes. People move companies, priorities shift, collaborations deepen, and relationships become less active. Useful recommendations must reflect those changes.
Introducing the Dynamic Trust Graph
DeepVista is building the Dynamic Trust Graph: a durable, structured, and continuously updated representation of human relationships and the evidence behind them.
Our goal is to make relational context available to AI agents so they can discover relevant opportunities, explain their recommendations, and help people take informed action.
Dynamic: relationships evolve
The graph is designed to preserve relationship history while incorporating new information. As agents process information from connected sources, they update what is known about people, organizations, interactions, and shared experiences. New evidence can strengthen, qualify, or contradict an earlier understanding.
Trust: preserve the evidence behind the relationship
Trust depends on context. Someone may trust a former colleague’s technical judgment without knowing whether they would be comfortable making an investor introduction. The graph is designed to preserve the evidence needed to reason about those distinctions: what happened, when it happened, where the information came from, and what remains uncertain. An observed interaction and an inferred relationship should remain distinguishable. A shared affiliation can suggest a connection; a history of collaboration provides different evidence.
Graph: connect context to action
Representing people, organizations, and interactions as a connected network makes it possible to trace paths across relationships. An agent could identify a potential introduction, explain the history supporting that path, and surface gaps that require human judgment. The goal is to help people recognize opportunities within their networks and understand why a particular next step may make sense.
The next infrastructure layer for AI
As AI agents take on more responsibility, they will need to reason about the human relationships surrounding their tasks.
Finding the right person, recognizing the right moment, and choosing an appropriate way to engage all depend on context accumulated over time.
We believe a durable representation of that context will become an essential infrastructure layer for AI.
DeepVista is building toward a future where agents help people discover opportunities grounded in their relationships—and where every recommendation has a history you can examine.
It's time for relational intelligence.
Turn your network into outcomes.
Your network already holds the opportunities. We help you find them and act on them.
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