01
What is Vessel?
Vessel is a model-agnostic AI platform with built-in deterministic quality control. It routes each task to the strongest frontier model — Claude, GPT, Gemini, or Grok — and verifies every output before returning it.
02
How is Vessel different from using ChatGPT or Claude directly?
Single-vendor tools lock you into one model and one set of strengths and blind spots. Vessel routes per task across every frontier model and adds a verification layer on top, so the output you see has already been checked for accuracy — not just generated.
03
Which AI models does Vessel route across?
Today, Vessel routes across Claude, GPT, Gemini, and Grok. New frontier models are added as they're released.
04
What does "deterministic quality control" mean in practice?
The same input produces the same audited output, every time — replayable on demand. That's the precondition for putting AI inside any audit-grade workflow: every claim is traceable, every run is reproducible, every output is verifiable by someone other than the system that produced it. See the architecture →
05
Does Vessel train its own model, or is it a layer on top of others?
Vessel is a layer. It doesn't train models; it routes across the strongest ones and wraps each output in a deterministic QC harness. That's the point of being model-agnostic — Vessel gets stronger every time the underlying frontier models do.
06
Who is Vessel built for?
Finance and enterprise teams where one wrong number costs the deal, the quarter, or the reputation: FP&A, due diligence, board reporting, equity research, and adjacent functions where outputs go in front of an auditor or a board.
07
How does Vessel handle data security?
Specifics — deployment options, data isolation, retention, and compliance posture — are shared during onboarding given the private-beta scope. Request access below to discuss your environment.
08
How do I get access to the private beta?
Access opens in small groups, by invite. Request access via the form below — you'll receive a confirmation email and a position in the queue. We're prioritizing teams in finance and enterprise where the QC harness has the most impact.
