NSF CONVERGENCE ACCELERATOR: AI & EDNA SEAFOOD TRACEABILITY
Strategic Partnerships: National Science Foundation (NSF) & Loyola Marymount University
Focus Areas
Intechgambit collaborated on Seafood Tracer, a $750,000 NSF-funded convergence research initiative designed to pioneer scalable, affordable technology to combat illegal fishing and fraud in global supply chains. The Core Tech: Built an online computer vision AI tool trained on ~6,000 expert-validated photographs to automate species-level identification and eliminate fraudulent species substitution. The Genomic Infrastructure: Portable, battery-powered environmental DNA (eDNA) sampling prototype that successfully reduced the timeline from wastewater sequencing to data readout to just ~12 hours. The Scientific Output: Led to a body of work that yielded two published scientific manuscripts, completed full genome sequencing for four commercially vital species, and secured a provisional U.S. Patent. The Driving Impact: Designed as a foundational blueprint to bridge academic research with federal compliance, establishing high-confidence biological checkpoints that protect consumer markets and support sustainable global trade.
Research Portfolio
Predictive AI Modeling for Climate Adaptation & Regional Resilience
Project Profile: Designed to translate global Earth systems data into hyper-local climate adaptation strategies across the Mid-Atlantic.
Exploratory Architecture for Early Alzheimer's Detection
Project Profile: Proposed as an exploratory and developmental framework to test the technical feasibility of a prototype data infrastructure for early neurodegenerative tracking.
Multimodal AI & Data Infrastructure for Neurodegenerative Care
Project Profile: Structured as a commercialization and engineering initiative to build a proactive, personalized disease-management ecosystem.
The Innovation
Engineered a layered AI and environmental monitoring framework to analyze regional climate patterns and optimize urban land management.
The Approach
Integrate and Validate advanced machine learning models against non-invasive, behavioral data modalities and clinical records to isolate early biological signatures.
The Platform
The Innovation: Conceptualized a cloud-agnostic, HIPAA-compliant neural data hub using advanced deep learning architectures to capture and model early-stage disease trajectories.
Core Objectives: Structured to deploy predictive crop models for community food security, construct ecological early-warning systems, and establish AI-optimized micro-forests to mitigate localized pollution.
Strategic Intent: Intended to establish scalable, open-source technological blueprints that bridge federal research with community action, creating frameworks for climate resilience and public health equity.
Core Objectives: Focused on testing the technical viability of host infrastructures for the ANCHOR-AIDE mobile app and the Integrated Neuro-Research (INR) Hub to generate robust, early-stage preliminary data.
Strategic Intent: Aimed at establishing a secure, methodologically sound foundation to inform future biomedical research and seed a full-scale, preemptive commercial care pipeline.
Core Objectives: Geared toward developing high-performance predictive models to track cognitive changes, automate provider workflows, and leverage agentic language models to deliver personalized, research-backed lifestyle interventions.
Strategic Intent: Positioned to establish a bidirectional health informatics framework that optimizes data flow between clinical settings and active research, driving down long-term public healthcare costs.
Validated Innovation: Peer-Reviewed Excellence
Before entering active procurement, our enterprise blueprints undergo rigorous evaluation by top-tier federal research panels. The following highlights showcase the panel feedback and recognized potential of our advanced technology architectures as evaluated by the National Science Foundation (NSF).
R2I2: Resilience Incubators