Validation Capabilities
A clear methodology for comparing, aligning, and evaluating AI-generated or human-authored documents across independent models. Designed for professionals who require clarity before action. Systematically reduce hallucination risk through structured alignment.
Flow Visualization
Core Architecture
1. Claim-Level Review
Documents are segmented into structured claims. Each claim is evaluated for factual grounding, internal consistency, and logical coherence.
2. Multi-Model Comparison
Independent large language models assess each claim separately. Areas of alignment, divergence, and uncertainty are made explicit.
3. Zero-Retention Design
Documents are processed ephemerally in isolated environments. No retention. No secondary use. No training on client data.
Process Overview
Analysis Timeline
Document Submission
The document is securely uploaded and prepared for structured review. Word count is calculated transparently for usage.
Claim Segmentation
The document is decomposed into discrete, verifiable claims. Each claim is linked to its original location for traceability.
Independent Model Evaluation
Multiple AI models assess each claim independently, returning structured assessments and rationale.
Structured Synthesis
Model outputs are normalized and compared using MAAT's alignment framework. Consensus is categorized as Strong Alignment, Partial, Divergence, or Context Needed.
Signal Delivery
Results are structured into a clear summary and detailed claim-level review, delivered immediately upon completion.
Output Formats
Interactive Dashboard
- Real-time Executive Summary
- Claim-Level Deep Dives
- Dynamic Model Alignment Filtering
- Interactive Evidence Anchors
Structured PDF Report
- Standardized Executive Summary
- Static Claim-Level Breakdown
- Model Alignment Consensus
- Time-Stamped Audit Log
MAAT helps you evaluate AI outputs with greater clarity by systematically mapping alignment and divergence, surfacing hallucination risk to strengthen your decision confidence.