The rapid growth of geospatial data presents challenges in terms of data management, integration, and analysis. Generated from a diverse array of sources including remote sensing platforms, in-situ sensor networks, volunteered geographic information (VGI), and Internet of Things (IoT) devices, this data holds immense potential for scientific discovery, environmental mon-itoring, urban planning, disaster management, and countless other domains. However, realizing this potential is critically dependent on effective data management, sharing, and utiliz-ation strategies. Interoperability - the ability of different sys-tems and organizations to access, exchange, and cooperatively use data—remains a cornerstone challenge. The Open Geo-spatial Consortium (OGC) has been instrumental in addressing this challenge for decades, developing a comprehensive suite of standards that define interfaces and encodings for publishing, discovering, and accessing geospatial data and processing ser-vices over the web (Sondheim et al., 1999, Castronova et al., 2013). Adherence to OGC standards is fundamental to imple-menting the FAIR data principles—ensuring data is Findable, Accessible, Interoperable, and Reusable—within the geospatial domain. These standards provide the syntactic and, to some extent, semantic foundation necessary for machines and hu-mans to interact reliably with distributed geospatial resources (Iv´anov´a et al., 2019). While these standards successfully en-code interoperability “on paper,” their real-world deployment remains uneven. Configuring, chaining, and querying hetero-geneous OGC endpoints typically demands specialist know-ledge of XML/JSON encodings, CRS transformations, pagin-ation, and query parameters—tasks that are non-trivial even for experienced GIS analysts and virtually prohibitive for domain experts in environmental science, public health, or urban plan-ning whose primary expertise lies elsewhere. Recent breakthroughs in instruction-tuned LLMs demonstrate near-human performance at tasks involving code synthesis, se-mantic search, and multi-modal reasoning. Their proven abil-ity to translate natural language questions into structured data-base queries suggests a promising avenue for lowering the entry barrier to standards-compliant geospatial infrastructures. How-ever, generic, publicly hosted models still fall short when faced with domain-specific ontologies or standard-specific paramet-erizations (e.g., bbox, limit, crs). Moreover, the privacy con-straints that sequester many government and corporate geodata holdings preclude the option of fine-tuning public models with proprietary examples. Most organizations expose data cata-logues only on their intranets for security or licensing reas-ons; in such settings, public Large Language Models (LLMs) have no prior knowledge about the services’ endpoint URLs, data schemas, or access tokens, and therefore cannot provide the “chat-with-your-data” functionality that users increasingly expect.

To bridge this gap we present the OGC-AI, an AI middleware tool that couples organization-internal OGC endpoints with a private, retrieval-augmented LLM to deliver conversational ac-cess, visualization, and lightweight spatial analytics without ex-posing sensitive data or infrastructure details to external ser-vices. OGC-AI automatically harvests service metadata (cap-abilities documents, landing pages, OpenAPI descriptions, and sensor metadata) and indexes them in a vector database. At in-ference time, the system performs semantic similarity search on the incoming user prompt, injects the most relevant snippets into the model’s context window, and instructs the model to (1) formulate correct OGC-compliant queries, (2) execute those queries via a secure proxy, and (3) translate the machine re-sponse into human-readable answers, maps, or charts, support-ing the mixed legacy-and-modern service landscape typically found in long-standing Spatial Data Infrastructures. By sim-plifying data query, visualization, and basic analysis, OGC-AI promises to democratize access to valuable geospatial inform-ation, thereby fostering wider data utilization and advancing the goals of the FAIR data principles within the geoinformat-ics community. OGC-AI aims to significantly lower the barrier to entry for util-izing OGC-standardized geospatial data. By abstracting away the complexities of specific OGC request syntax and service protocols, it empowers a broader range of users, including do-main scientists, policy analysts, students, and citizens, to dir-ectly query and visualize geospatial information. For experi-enced GIS professionals, it can serve as a rapid assessment tool for exploring new services or performing quick data checks. Furthermore, it directly enhances the Accessibility and Usab-ility aspects of the FAIR principles for OGC-based data, com-plementing the Findability and Interoperability inherently pro-moted by the standards themselves. By enabling interaction with internal organizational data resources, OGC-AI provides a valuable capability currently unmet by public LLM platforms. This research contributes to the burgeoning field of Geospa-tial Artificial Intelligence (GeoAI) by demonstrating a practical application of LLMs to improve human-computer interaction within established geospatial data infrastructures. Future work will involve expanding the range of supported OGC standards, refining the natural language understanding for more complex geospatial queries, enhancing the analytical capabilities, and exploring robust methods for handling authentication and au-thorization for secured services.