This study presents a hybrid analytical framework that enhances project selection by achieving reasonable predictive accuracy through the integration of expert judgment and modern artificial intelligence (AI) techniques. Using an enterprise-level dataset of 10,000 completed software projects with verified real-world statistical characteristics, we develop a three-step architecture for intelligent decision support. First, we introduce an extended Analytic Hierarchy Process (AHP) that incorporates organizational learning patterns to compute expert-validated criteria weights with a consistent level of reliability ($CR=0.04$), and Linear Programming is used for portfolio optimization. Second, we propose a machine learning architecture that integrates expert knowledge derived from AHP into models such as Transformers, TabNet, and Neural Oblivious Decision Ensembles through mechanisms including attention modulation, split criterion weighting, and differentiable tree regularization. Third, the hybrid AHP-Stacking classifier generates a meta-ensemble that adaptively balances expert-derived information with data-driven patterns. The analysis shows that the model achieves 97.5% accuracy, a 96.9% F1-score, and a 0.989 AUC-ROC, representing a 25% improvement compared to baseline methods. The framework also indicates a projected 68.2% improvement in portfolio value (estimated incremental value of USD 83.5 M) based on post factum financial results from the enterprise’s ventures.This study is evaluated retrospectively using data from a single enterprise, and while the results demonstrate strong robustness, generalizability to other organizational contexts requires further validation. This research contributes a structured approach to hybrid intelligent systems and demonstrates that combining expert knowledge with machine learning can provide reliable, transparent, and high-performing decision-support capabilities for project portfolio management.

Retrieval-Augmented Generation (RAG) systems face substantial challenges when navigating large volumes of complex scientific literature while maintaining reliable semantic retrieval, a critical limitation for automated scientific discovery where models must connect multiple research findings and identify genuine knowledge gaps. This study introduces a question-based knowledge encoding method that enhances RAG without fine-tuning to address these challenges. Recognizing the lack of syntactic understanding in major Large Language Models, we generate syntactic and semantic-aligned questions and apply a syntactic reranker without training. Our method improves both single-hop and multi-hop retrieval with Recall@3 to 0.84, representing a 60% gain over standard chunking techniques on scientific papers. On LongBenchQA v1 and 2WikiMultihopQA, which contain 2000 documents each averaging 2k-10k words, the syntactic reranker with LLaMA2-Chat-7B achieves F1 = 0.52, surpassing chunking (0.328) and fine-tuned baselines (0.412). The approach additionally reduces vector storage by 80%, lowers retrieval latency, and enables scalable, question-driven knowledge access for efficient RAG pipelines. To our knowledge, this is the first work to combine question-based knowledge compression with explicit syntactic reranking for RAG systems without requiring fine-tuning, offering a promising path toward reducing hallucinations and improving retrieval reliability across scientific domains.