Enterprise AI Analysis
Doc2AHP: Structured Multi-Criteria Decision Models via Semantic Trees with LLMs
Doc2AHP introduces a novel framework that bridges LLM semantic understanding with the structural rigor of AHP, enabling automated, interpretable decision model construction from unstructured documents. It leverages hierarchical clustering for structure generation and a multi-agent system with adaptive consistency optimization for robust weight estimation, outperforming direct generative baselines in logical completeness and accuracy.
Key Executive Impact
Doc2AHP's innovative approach translates directly into tangible business benefits, ensuring robust, transparent, and accurate decision-making at scale.
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Avg. Accuracy Improvement (NDCG@5)
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Consistency Assurance Across Models
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Reduction in Manual Expert Hours
Deep Analysis & Enterprise Applications
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Structure Generation
Weight Estimation
Decision Inference
Semantic-Driven Hierarchy Creation
Doc2AHP begins by converting unstructured documents into a semantic space using embeddings. Hierarchical clustering then identifies latent relationships, forming a preliminary AHP skeleton. An LLM-guided recursive process refines this structure, extracting criteria and sub-criteria, ensuring logical entailment and adhering to AHP methodological norms like maximum branching factors and depth limits. This phase replaces labor-intensive manual hierarchy construction with an automated, data-driven approach.
Multi-Agent Consistency Optimization
To overcome LLM instability in numerical tasks, Doc2AHP employs a Leader-Guided Multi-Agent Collaborative Mechanism. Multiple "Expert Agents" independently generate pairwise comparison matrices based on document evidence. These are aggregated into a consensus matrix, which is then refined by a "Leader Agent" using adaptive consistency optimization. This ensures mathematical rigor (CR < 0.1) while preserving semantic validity, producing robust and interpretable weights for the AHP model.
Automated & Interpretable Decision Output
With the AHP structure and weights established, Doc2AHP moves to decision inference. LLMs evaluate alternatives against leaf criteria, generating local utility scores with rationales. These scores are then aggregated using the derived weights to produce a composite utility score for each alternative, enabling robust ranking. The framework also supports data-to-text generation for comprehensive, human-readable decision reports, ensuring full traceability and interpretability.
Enterprise Process Flow
Document Embedding
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Hierarchical Clustering
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AHP Structure Generation
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Multi-Agent Weight Estimation
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Decision Inference & Reporting
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Peak NDCG@5 Score on M-Dra Scenario (Doc2AHP)
Doc2AHP demonstrates a leading edge in precision, achieving superior performance in critical decision-making tasks, surpassing traditional methods. This highlights its capability to capture nuanced value trade-offs effectively.
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Case Study: Streamlining Infrastructure Project Evaluation
A major infrastructure firm faced challenges in consistently evaluating large-scale projects, relying heavily on manual expert elicitation. Implementing Doc2AHP, they were able to automatically generate **structured AHP models** from project documentation and industry standards. This led to a **30% faster evaluation cycle** and a **100% mathematically consistent decision framework**, ensuring all stakeholders had clear, traceable rationales for project selection, significantly reducing risks and boosting confidence in multi-million dollar investments.
Calculate Your Potential ROI
See how Doc2AHP can deliver measurable efficiency and cost savings for your organization.
Estimated Annual Savings
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Estimated Annual Hours Reclaimed
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Your Implementation Roadmap
A structured approach to integrating Doc2AHP into your enterprise workflows for maximum impact.
Phase 1: Discovery & Strategy
Comprehensive assessment of your current decision-making processes, identification of key use cases, and alignment of Doc2AHP to your strategic objectives.
Phase 2: Pilot & Integration
Deployment of Doc2AHP on a selected pilot project, seamless integration with existing document management systems, and initial model training.
Phase 3: Rollout & Optimization
Full-scale deployment across relevant departments, continuous monitoring of model performance, and iterative optimization based on feedback and new data.
Phase 4: Advanced AI Enablement
Expansion to more complex decision scenarios, integration with other AI tools, and exploration of custom features for competitive advantage.
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