I propose a real-time cross-source kernel adaptation framework for dynamic data ingestion, specifically designed to address the challenges of non-stationary and sparse data streams in AI-driven development entrepreneurship, particularly within African agricultural contexts. The proposed module replaces conventional static pipelines with an adaptive architecture which continuously monitors and recalibrates dependencies across heterogeneous information sources, including satellite imagery, weather forecasts, and farmer-reported data. At its core, the Bayesian online change-point detection algorithm identifies abrupt structural shifts in inter-variable relationships by modeling the joint distribution of features as a multivariate Gaussian with a time-varying covariance structure. Upon detection of a change point, a lightweight stochastic optimization procedure adjusts kernel parameters—specifically, the per-stream length-scale parameters in a sum of radial basis function kernels—without the need for batch retraining on historical data. This optimization reduces the negative log-marginal likelihood of recent observations, an approximation efficiently achieved via a Hilbert space Gaussian process projection that lowers computational complexity from cubic to quadratic relative to the count of basis functions. The dynamically refined kernel subsequently reweights incoming multi-modal inputs, according greater importance to streams with shorter length-scales that suggest more informative local structure. The resulting weighted representation is supplied to downstream AI inference engines, thereby maintaining consistent alignment with current data dynamics. I introduce a computationally feasible, online approach that preserves informative data representations amidst rapid environmental change. This framework holds importance because it permits robust AI deployment in data-scarce, non-stationary contexts, thereby bolstering entrepreneurial decision-making where traditional static pipelines would prove inadequate.