ENTERPRISE AI ANALYSIS
Evidence-Driven Decision Support for AI Model Selection in Research Software Engineering
Research Software Engineers (RSEs) face significant challenges in selecting optimal AI models amidst rapid proliferation and fragmented metadata. Current ad hoc approaches compromise reproducibility, transparency, and quality. This research introduces MODELSELECT, a novel framework designed to provide structured, evidence-driven decision support, addressing the critical need for systematic AI model selection aligned with specific technical and contextual requirements.
Executive Impact: What This Means for Your Enterprise
MODELSELECT delivers reliable, interpretable, and reproducible recommendations for AI model and library selection, consistently outperforming or matching generative AI systems in coverage and rationale alignment. By integrating automated data pipelines, a structured knowledge graph, and Multi-Criteria Decision-Making (MCDM) principles, enterprises can enhance decision-making accuracy, improve research software quality, and ensure greater traceability and consistency in AI deployments.
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Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
The MODELSELECT Framework: A Systematic Approach
MODELSELECT provides a rigorous, evidence-driven approach to AI model selection. It leverages automated data pipelines, a structured knowledge graph, and Multi-Criteria Decision-Making (MCDM) principles to guide Research Software Engineers (RSEs) towards optimal AI model choices. This systematic framework addresses the limitations of ad hoc methods and generic AI assistants, ensuring transparency, reproducibility, and context-awareness in research software engineering.
Enterprise Process Flow
GitHub Repository Data Extraction
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AI Library Data Extraction
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Model/Feature Identification
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Quality Assessment Data Extraction
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Knowledge Base Integration
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Inference & Recommendation
Knowledge Graph & Decision Model: Structured Insights
At the core of MODELSELECT is a structured knowledge graph that meticulously links AI base models, variations, associated libraries, features, and extracted rationales. This robust representation supports many-to-many relationships, providing traceability and transparency for recommendations. An inference mechanism, guided by MCDM principles, then identifies candidate models aligning with user-defined functional and non-functional requirements.
| AI Model Selection Approach | MODELSELECT Framework | Traditional/Ad-hoc Methods | Generative AI (GenAI) Assistants |
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Empirical Validation: Proven Effectiveness
MODELSELECT's effectiveness was rigorously assessed through controlled experiments using a curated dataset of 50 real-world case studies. The framework demonstrated high coverage and strong rationale alignment in both model and library recommendation tasks, often outperforming or matching leading generative AI systems while providing superior traceability and consistency.
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MODELSELECT Model Recommendation Coverage, aligning with expert rationales.
| Performance Metric | MODELSELECT | GPT-40 | Gemini 2.5 Flash | Claude Sonnet 4.5 |
|---|---|---|---|---|
| Model Coverage | 86.96% | 82.61% | 77.17% | 89.13% |
| Library Coverage | 82.61% | 85.87% | 90.22% | 91.30% |
| Overlap with MODELSELECT | N/A | 53.48% | 47.83% | 51.59% |
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Quantify Your AI Impact
Use our ROI calculator to estimate the potential time and cost savings for your enterprise by adopting evidence-driven AI model selection.
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Your Path to Evidence-Driven AI Selection
Implementing MODELSELECT for your enterprise involves a structured approach to integrate its capabilities into your existing RSE workflows. Here's a typical roadmap:
Phase 1: Discovery & Requirements Analysis
Conduct an initial assessment of your current AI model selection processes, identify key pain points, and define specific technical and contextual requirements for a decision support system.
Phase 2: MODELSELECT Integration & Customization
Deploy MODELSELECT pipelines, integrate with your internal repositories and documentation, and customize the knowledge graph schema to align with your enterprise's domain-specific AI models and features.
Phase 3: Data Ingestion & Knowledge Base Population
Utilize automated pipelines to ingest metadata, features, and quality assessments from diverse sources. Populate the knowledge graph with your enterprise's proprietary AI models and historical usage data.
Phase 4: Pilot & Validation
Run pilot projects with RSE teams, validate MODELSELECT's recommendations against expert choices, and refine inference rules and weighting mechanisms based on empirical feedback.
Phase 5: Full Deployment & Continuous Improvement
Roll out MODELSELECT across your organization, establish monitoring for performance and data freshness, and set up a feedback loop for continuous refinement and expansion of the knowledge base.
Ready to Optimize Your AI Model Selection?
Transition from ad hoc choices to evidence-driven decisions. Schedule a free consultation with our AI experts to explore how MODELSELECT can transform your research software engineering.
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