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Wavn for Research

See the opposing evidence first.

Most AI confirms what you already believe. Wavn does the opposite — it surfaces the strongest evidence against your hypothesis before you conclude, so you're built for balance, not confirmation.

Ask an AI about your hypothesis and it tends to agree. That's the worst thing a research tool can do — confirmation dressed as analysis. You need the disconfirming evidence first, not buried.

How Wavn helps research

Built for the way you decide.

Disconfirming evidence, up front

The Perspective Engine deliberately surfaces the strongest evidence against your hypothesis alongside the support — with sources you can check. Balance by design, not by luck.

A record of how the conclusion formed

Mark findings as conclusion, next step, or open question. The path from evidence to conclusion is preserved — reproducible reasoning, not just a final claim.

Sourced research, not confident guesses

Perplexity grounds findings in real, cited sources; Cohere holds the literature you've already reviewed. You see where a claim comes from, every time.

No OpenAI

Your work stays yours

Never used to train a model, never sold. No OpenAI dependency means your unpublished work isn't feeding someone else's system.

A day in Wavn

A researcher checks a promising result.

A researcher has a result that supports their hypothesis — encouraging, and exactly when bias is most dangerous. Here's how Wavn keeps them honest.

01
She states the hypothesis and result on the canvas, plainly.
02
Perplexity surfaces the literature; the Perspective Engine pulls the strongest contradicting findings first.
03
She runs Rift — one engine defends the result, one attacks the method. A confound surfaces.
04
She marks it an open question, not a conclusion. The reasoning trail is preserved for the writeup.

Illustrative scenario. Wavn is invite-only and pre-launch; no real client data depicted.

Hypothesis — Effect of X on Y
My data shows X significantly increases Y. Find me the strongest evidence this is wrong or confounded before I get attached to it.
Perplexity · Research
Two recent studies report the same association — but both flag a confound: samples weren't controlled for Z, which independently drives Y. Your effect may be partly Z's. Worth checking whether your data controls for it.
Supports
Your result aligns with two published associations in the same direction.
Disputes
Both candidate studies flag an uncontrolled confound (Z) that independently drives Y.
Decision: Treat as open until the data is re-run controlling for Z.
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