Voyager: Easy Graph Traversal for AI Workloads at Enterprise Scale
The Problem
Most query languages weren’t designed for AI. They treat data as tables or flat records, forcing developers to stitch together complex joins and brittle hacks.
But AI workloads demand more. You don’t just need to query data—you need to traverse the relationships between data, moving quickly, natively, and without piles of custom code.
AI doesn’t operate on isolated records. When you ask a question of your data, the right answer often lies several hops away: from a document, to its vector embedding, to the people and projects connected to it.
Why It Matters
If your query language can’t move across those relationships natively, you’re forced into workarounds. Developers end up writing glue logic, maintaining complex joins, or flattening everything into static pipelines. Each shortcut slows teams down, adds risk, and piles up technical debt.
Meanwhile, business users suffer because results lack depth. An AI system that only sees fragments can’t deliver insights with the context leaders need to make critical decisions.
Modern workloads require a language that reflects how data is actually connected. Without it, enterprises are stuck with blind spots and brittle systems.
The FlexVertex Answer
Voyager is FlexVertex’s traversal language, purpose-built to navigate across objects, embeddings, and relationships as naturally as SQL navigates tables.
With Voyager, you can start from an embedding and hop directly to related documents, media, or people—no glue code, no custom joins, no bolt-ons. The connections are built in. The language makes them easy to traverse. And you can start your journey in either direction—from an object to its relationships, or from a relationship back to its objects.
That design makes Voyager a natural fit for AI use cases. Instead of flattening the complexity of enterprise data, it embraces it, making traversals simple, powerful, and native.
An Example
Imagine asking: “Which contracts cite this case, and who were the attorneys of record?”
In a relational system, answering that question might take multiple joins, external lookups, and inelastic code. In Voyager, it’s a single, native traversal: contract → case → attorney objects.
The difference is like moving through a map. Instead of just seeing disconnected points, you can walk the roads between them—discovering new connections and new insights along the way.
That ability to traverse naturally isn’t just more efficient—it makes results more trustworthy. Business users can follow the chain of connections themselves, seeing not just the answer, but how the system arrived at it.
The Takeaway
AI workloads live in the connections between objects, not just the objects themselves.
Voyager gives you a language designed for that reality—making traversals across embeddings, documents, and graphs simple, powerful, and native.
With Voyager, your AI stops working with fragments and starts working with context. And when context is preserved, enterprises gain insights they can actually trust.