AstroGenesis currently retrieves observational data through access to the Markarian Multiwavelength Data Center (MMDC), which provides science-ready, curated multi-wavelength and multi-messenger datasets. The retrieval process preserves metadata related to instrument, time coverage, and observational context. The system is designed in a modular way, allowing additional data centers and archives to be connected through API-based interfaces, enabling data retrieval from any compatible external repository as support is added.

AstroGenesis uses neural networks that are trained on large sets of numerical simulations generated with physically motivated radiative codes. These simulations define the behavior of the underlying theoretical models across relevant parameter spaces. Once trained, the neural networks reproduce the results of the numerical simulations with high accuracy while enabling much faster evaluation, supporting efficient parameter estimation, model comparison, and exploration of physically motivated scenarios.

AstroGenesis is designed to integrate observational datasets and theoretical models that are applicable to a specific class of astrophysical sources, rather than to individual objects or multiple unrelated classes. Multi-wavelength and multi-messenger datasets can be connected when they describe source populations in a structured, programmatic form. Likewise, theoretical models can be integrated when they are well tested, physically motivated, and formulated for a single source class, allowing consistent application and interpretation within the AstroGenesis framework.

At present, AstroGenesis operates on science-ready observational data products rather than raw instrumental data. Analysis pipelines for raw observational data are currently under development and integration. As these pipelines are implemented, AstroGenesis is designed to progressively support the analysis of raw data, enabling end-to-end workflows from initial data products to scientific interpretation in future releases.