RESEARCH PRODUCTS + AI RESEARCH LAB
ArtificialGate develops interactive research products and executes project-based AI R&D for engineering teams and research organizations.
Discuss an R&D project
Interactive sandboxes, experiment tools, model comparisons, reliability analysis, benchmarks, graphs, and research reports that shorten the path from a research question to a reproducible experiment.
Explore research productsProject-based AI R&D for problems without a clear engineering answer: feasibility, baselines, model training and comparison, optimization, prototyping, code, models, and engineering reports.
Discuss an R&D projectThe lab is built for real research uncertainty: when it is not yet clear which approach will work, a previous project has stalled, or an algorithm or paper must become a working system.
We define the experiment, build the baseline, train and compare models, analyze errors, optimize results, and deliver a prototype with code, models, and an engineering report.
Fourteen forecasting procedures on six AI-related Google Trends indices, examining sparse data and predictive uncertainty.
Read PaperA machine-learning study using imbalance-aware preprocessing for requirements classification.
Read PaperA transformer encoder for accurate real-time classification from motion sensor data.
Published in IEEE Sensors JournalRead PaperA runtime controller that detects destabilizing training updates and recovers by rollback.
Read PaperA reliability metric for measuring cognitive recovery latency in multi-agent AI systems.
Read PaperAn audit of fixed-target likelihood measures that separates support shifts from preference, generation, and robustness claims.
Read PaperHow validation gating and nearest-neighbor association reshape innovation statistics in Kalman tracking.
Read PaperAn optimizer-aware audit for detecting when locally favorable PINN updates deteriorate after complete finite-step execution.
Accepted to IEEERead PaperA compact temporal model that estimates vehicle speed from smartphone acceleration alone and analyzes the role of finite sensing context.
Read PaperA runtime-stability framework for detecting cognitive drift and measuring recovery in tool-augmented reasoning systems.
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