Platforms & Programs

Analytical & Methodological Foundations

Gunn Analytics systems are built on advanced analytical methodologies drawn from computational neuroscience, statistics, and machine learning. Rather than relying on surface-level correlations or opaque models, our work emphasizes inference, robustness, and interpretability across complex, high-dimensional data—whether the objective is scientific discovery, product optimization, or operational decision-making.

Core methodological strengths include:

• Multimodal modeling across neural, behavioral, genetic, media, and business process data
• Network-based analyses (connectivity, graph metrics, stability, and variance partitioning)
• Robust statistical validation (permutation testing, bootstrapping, nested cross-validation)
• Predictive modeling with explicit generalization testing, causal sensitivity, and imbalance handling
• Explainable AI (XAI) frameworks for transparent model interpretation
• Time–frequency, spectral, and latent-state modeling for dynamic systems
• Careful separation of trait-like structure from state-dependent modulation

These approaches support reliable inference on both scientific endpoints and operational KPIs, enabling decisions that generalize beyond a single dataset, cohort, or campaign rather than optimizing for short-term signal.

Engineering & Reproducibility Principles

Across platforms and programs, Gunn Analytics prioritizes engineering discipline and reproducibility as first-class design constraints.

Key principles include:

• End-to-end pipeline automation with structured inputs and deterministic outputs
• Explicit data provenance, metadata standards, and version control
• Reproducible analysis workflows with auditable logs and artifacts
• Separation of development, evaluation, and deployment environments
• Local-first and enterprise-safe architectures where required
• Modular system design to support iteration, reuse, and spin-out

These principles allow analytical systems to scale across research, product, and operational contexts without sacrificing interpretability, scientific validity, or organizational trust.

Scope of Work

• Automation platforms that replace manual workflows with reproducible infrastructure
• Applied analytics systems supporting KPIs, experimentation, and research-grade inference
• Personalized intelligence tools built with privacy, explainability, and long-term impact in mind
• Internal R&D programs exploring autonomous software development and neuro-AI integration

All systems and intellectual property described here are developed and owned by Gunn Analytics LLC. Academic affiliations are independent and non-overlapping.

Active Platforms & Programs

Media Analytics & Automation Platform 

  • AI-powered system for large-scale ingestion, multimodal analysis (vision, audio, and language), and structured metadata generation across short-form media. Designed to operate under real-world production constraints and continuously validate models related to engagement, ranking, and content optimization. 

  • Status: Operational (post-MVP) 

  • Economic role: Revenue-generating deployment environment and ongoing model validation system.

 Personalized Intelligence Platform 

  • Privacy-first system for persona modeling, autobiographical structure, and explainable inference. Designed to support individualized analysis without diagnostic claims, emphasizing user trust, interpretability, and long-term personalization frameworks. 

  • Status: MVP complete 

  • Economic role: Pilot deployments and foundation for licensed personalization and digital-identity tools.

 Neuro-AI Research & Translation Program 

  • Long-term research and commercialization program integrating neuroscience, genetics, psychology, and machine learning to model trait-like neural stability and state-dependent perturbation. Advances are guided by structured customer discovery and translational feasibility. 

  • Status: Active R&D 

  • Economic role: Grant alignment, partnership development, and defensible IP supporting future spin-outs.

 Autonomous Software Development Infrastructure 

  • Internal system for automated code generation, testing, debugging, and repair using local language models under audit-constrained and enterprise-safe conditions. Designed to accelerate development cycles and enforce reproducibility across platforms. 

  • Status: Internal platform 

  • Economic role: Development cost reduction, accelerated iteration, and potential future licensed infrastructure.