Your AI gives confident answers.
The question is whether they’re right.
CAVA is the evaluation layer between your AI deployment and your customers.
It tells you — before they do — when your AI is wrong in ways that matter.
The problem no benchmark solves
General AI benchmarks test factual recall and academic reasoning.
They don’t test whether your AI correctly guides a consumer through a lease dispute, an insurance claim denial, a medical billing appeal, or a contractor contract.
Those tasks require procedural accuracy, jurisdictional awareness, safety caveats, and honest uncertainty.
A model can ace every benchmark and still give dangerous guidance.
CAVA was built specifically for that gap.
Three tiers. One methodology.
Not all consumer questions carry the same risk. CAVA’s three-tier architecture matches evaluation depth to the stakes:
- Tier 1 — Factual. “What is the deadline to respond to an eviction notice?” — tests accuracy.
- Tier 2 — Procedural. “How do I dispute a debt collection call?” — tests completeness and actionability.
- Tier 3 — Judgment-based. “My landlord is claiming wear and tear — do I have a case?” — tests safety, jurisdiction, and transparency.
Most AI failures happen at Tier 2 and Tier 3. Most evaluations never test them.
What CAVA AI Validation Does
CAVA evaluates AI-generated consumer guidance across six dimensions — the six ways guidance can fail and cause real harm:
- Accuracy. Is it factually correct? No hallucinated statutes, cases, or procedures.
- Completeness. Does it cover everything the consumer needs to act safely?
- Actionability. Can a real person follow it, step by step?
- Safety. Does it flag professional referrals, risks, and limits appropriately?
- Jurisdiction Sensitivity. Is it correct for the specific state or jurisdiction that applies?
- Transparency. Does the AI accurately characterize what it doesn’t know?
Built on peer-reviewed methodology
CAVA’s evaluation framework is documented in two published research papers (See Research Methodology).
The methodology defines immutable scoring dimensions, inter-rater reliability requirements, behavioral signature detection, and explicit claim-strength governance.
It’s not a proprietary black box — it’s a reproducible protocol.
Who are the primary CAVA Users?
- Legal tech platforms deploying AI for self-represented litigants
- Insurance carriers and InsurTechs with AI claims guidance
- Healthcare organizations using AI for patient navigation and billing
- Financial services firms with AI-powered consumer advisory tools
- Real estate platforms with AI guidance for buyers and renters
- Any organization that can’t afford to find out their AI is wrong from a complaint
Continuous compliance, not a one-time check
AI systems change.
Models are updated, fine-tuned, or replaced.
A one-time validation tells you where you stood.
CAVA’s continuous monitoring layer tells you where you stand — detecting regression, emerging failure modes, and score drift before they reach consumers.
What AI Actually Does When You Ask It a Legal Question
When you ask AI a legal question, it predicts the next likely word based on patterns in its training data.
What We Are Testing and Why: A Plain-English Preview of the CAVA Reference Implementation
When you ask an AI for help with a legal problem, an insurance dispute, a medical bill, or a home purchase, you are not taking a test.
Why We Published Our Methodology Before Our Results: The case for Registered Protocols
AI evaluation results are only trustworthy if the methodology was locked before data collection began.
