No accuracy number on your content
Competing tools cannot tell you what percentage of their extractions were right on your taxonomy and your edge cases. A dashboard of predictions is not a measure of whether they were correct.
Wave · Eval Harness
Don't trust the AI. Read the accuracy score.
Enterprise AI adoption stalls on one question: how do I know it is getting it right? Most platforms answer we think so. Wave answers with a number. It measures its own extraction accuracy on your content, lets your team label and improve it, and refuses to promote any AI configuration that does not clear the bar.
Quick answer
The Eval Harness is a Wave capability that continuously measures the accuracy of Wave's own AI content extraction on your taxonomy and your content. Operators can label extractions to sharpen future precision, a challenger model runs in parallel for a side-by-side comparison, and a pass gate blocks any AI configuration that does not clear the accuracy bar from ever reaching production.
Key capabilities
- Continuous AI extraction accuracy scoring
- Multi-dimensional accuracy scoring on your taxonomy
- One-click operator labeling of outputs
- Challenger model for side-by-side comparison
- Accuracy pass gate before any change goes live
- Separate eval budget from production AI
- Accuracy reports readable in the console
Last updated July 2026
The problem
If you cannot measure the AI, you are running on hope.
Buyers have been burned by AI tools that looked great in the demo and went sideways on their real content. The core problem is that black-box platforms cannot show you how accurate their AI actually is on your data.
It went sideways after the demo
AI that performs in a controlled demo can degrade on real, messy content. Without ongoing measurement, you find out from a bad campaign, not from a score.
No way to compare or improve it
When a tool picks one AI provider, you cannot see how an alternative would have done, and you have no lever to make the outputs better as your team works.
How Wave does it
Measure it, let your team teach it, gate every change.
Wave builds measurement into the AI pipeline itself, so accuracy is a number you can read, a thing your team can improve, and a bar every configuration has to clear.
- 01
Score every extraction for accuracy
Wave scores every extraction against your taxonomy, checks whether it is correct and well-formed, and classifies what went wrong when it misses, so accuracy is a number you can read on your own content rather than a claim you have to trust.
- 02
Let operators label outputs
From any content asset in the console, an operator marks an extraction correct or wrong and supplies the right value in one click. Those labels flow into a training set that scores future runs. Using the platform makes it better.
- 03
Run a challenger in parallel
Wave can run a second AI model alongside the one in production, scored on your own content, so you can compare options and switch on evidence instead of staying locked to a single vendor.
- 04
Gate before it goes live
No change to Wave's AI, whether a new provider, model, or prompt, reaches production until it clears the accuracy bar on your content. If it falls short, Wave refuses to use it until it passes.
Where it fits
Built into the AI pipeline, not bolted onto the deck.
The Eval Harness is part of Wave's core architecture, not a feature added after the AI was built. It sits alongside the production extraction pipeline that powers Content Intelligence and the rest of Wave's person-level data layer.
Your content
Wave
Scored accuracy
Wave runs measurement per tenant against your own content, so the accuracy you see reflects your taxonomy and your edge cases, not a generic benchmark. The harness ships with a validated baseline from Wave's own demo corpus, so a new tenant's first report shows real performance from day one rather than a cold-start zero. Labeled examples, accuracy scores, and run reports are all readable in the operator console, with no separate analytics tool required.
Works alongside Content Intelligence, Channel Affinity, The full Wave platform.
Why Wave is different
Auditable AI by design, not by request.
No martech vendor today leads with here is how we prove our AI accuracy to you. Wave can own that narrative because measurement was in the product from the start, not added under security review.
Most tools
Wave
AI accuracy is a dashboard of predictions with no correctness score.
Wave reports an accuracy score, the failure modes, and the threshold the AI had to clear on your content.
You are locked to whatever single AI provider the tool chose.
Wave runs a challenger provider in parallel and lets you switch with one flag, no re-ingestion.
The AI cannot be improved by the people who use it.
Operators label extractions in one click, and those labels sharpen future accuracy.
A new model or prompt ships with no accuracy guardrail.
Wave blocks any configuration that does not clear the accuracy bar on your content from reaching production.
FAQ
Questions buyers ask about the Eval Harness.
What is the Eval Harness in Wave?
The Eval Harness is a Wave capability that continuously measures the accuracy of Wave's own AI content extraction on your taxonomy and content. It lets operators label extractions to sharpen precision, runs a challenger model in parallel for comparison, and blocks any AI configuration that fails to clear the accuracy bar from reaching production.
How does Wave measure its AI accuracy?
Wave scores every extraction against your taxonomy for whether it is correct and well-formed, and classifies the kind of error when it misses. Those scores run continuously on your own content, so accuracy is a number you can read rather than a feeling you have to take on faith.
How do operators improve the AI?
From any content asset in the operator console, an operator can mark an extraction correct or incorrect and supply the right value in a single click. Those labels flow into a structured training set that is used to score future extraction runs. The act of using the platform generates signal that improves it.
What is the challenger model?
Wave can run a second AI model in parallel with the one in production. Both run on your own content, the production model's answer is what gets used, and the challenger is scored alongside it. After enough runs you can compare them and switch on evidence, with no re-ingestion.
What stops a worse AI config from going live?
Wave has a pass gate on AI configuration changes. A new provider, model, or prompt cannot be promoted to production until it clears the accuracy bar on your content. If it does not meet the bar, Wave refuses to use it until it does.
Will my first accuracy report be empty?
No. The harness ships with a validated baseline from Wave's own demo content corpus, so a new tenant's first accuracy report shows real extraction performance from day one instead of a cold-start zero. As your team labels real content, the picture sharpens to your own data.
Does the eval work eat into my AI budget?
No. The evaluation work runs against its own separate budget, distinct from your production extraction budget. Measuring accuracy never competes with or consumes the budget your live content tagging depends on.
How do I see the accuracy score on my own content?
Book a 20-minute walkthrough. We will configure a tenant against your content and show the accuracy score, the failure modes, and the shadow comparison Wave would produce on your real extractions.
See it on your data
See your AI's accuracy score on your own content.
Book a 20-minute walkthrough. We will run the Eval Harness against your content and show the accuracy score, failure modes, and shadow comparison Wave would produce on your real extractions.
Request a demo Download the one-pager (PDF)