[{"data":1,"prerenderedAt":14},["ShallowReactive",2],{"blog-why-more-data-alone-is-not-enough":3},{"category":4,"content":5,"date":6,"description":7,"faq":8,"ogTitle":9,"readTime":10,"slug":11,"tags":12,"title":9},"Signal Intelligence","\u003Ch2 class=\"text-3xl font-bold mb-6\">CI's Data Infrastructure\u003C\u002Fh2>\n            \u003Cp class=\"text-slate-600 dark:text-slate-400 mb-6 leading-relaxed\">\n              Contextual Intelligence aggregates data daily from global public sources. These fall into\n              five categories:\n            \u003C\u002Fp>\n\n            \u003Cdiv class=\"overflow-x-auto mb-8\">\n              \u003Ctable class=\"w-full border-collapse\">\n                \u003Cthead>\n                  \u003Ctr class=\"bg-slate-100 dark:bg-slate-800\">\n                    \u003Cth class=\"text-left font-semibold px-4 py-3 border border-slate-200 dark:border-slate-700\">\n                      Category\n                    \u003C\u002Fth>\n                    \u003Cth class=\"text-left font-semibold px-4 py-3 border border-slate-200 dark:border-slate-700\">\n                      Examples\n                    \u003C\u002Fth>\n                    \u003Cth class=\"text-left font-semibold px-4 py-3 border border-slate-200 dark:border-slate-700\">\n                      Update Frequency\n                    \u003C\u002Fth>\n                  \u003C\u002Ftr>\n                \u003C\u002Fthead>\n                \u003Ctbody>\n                  \u003Ctr class=\"border-b border-slate-200 dark:border-slate-700\">\n                    \u003Ctd class=\"px-4 py-3 border border-slate-200 dark:border-slate-700\">\n                      Regulatory Databases\n                    \u003C\u002Ftd>\n                    \u003Ctd class=\"px-4 py-3 border border-slate-200 dark:border-slate-700\">\n                      FDA 510(k), EUDAMED, ClinicalTrials.gov\n                    \u003C\u002Ftd>\n                    \u003Ctd class=\"px-4 py-3 border border-slate-200 dark:border-slate-700\">\n                      Daily\n                    \u003C\u002Ftd>\n                  \u003C\u002Ftr>\n                  \u003Ctr class=\"border-b border-slate-200 dark:border-slate-700\">\n                    \u003Ctd class=\"px-4 py-3 border border-slate-200 dark:border-slate-700\">\n                      Patents\n                    \u003C\u002Ftd>\n                    \u003Ctd class=\"px-4 py-3 border border-slate-200 dark:border-slate-700\">\n                      USPTO, EPO, DPMA\n                    \u003C\u002Ftd>\n                    \u003Ctd class=\"px-4 py-3 border border-slate-200 dark:border-slate-700\">\n                      Weekly\n                    \u003C\u002Ftd>\n                  \u003C\u002Ftr>\n                  \u003Ctr class=\"border-b border-slate-200 dark:border-slate-700\">\n                    \u003Ctd class=\"px-4 py-3 border border-slate-200 dark:border-slate-700\">\n                      Grant Programs\n                    \u003C\u002Ftd>\n                    \u003Ctd class=\"px-4 py-3 border border-slate-200 dark:border-slate-700\">\n                      BMBF, Bundesanzeiger, EU Funding & Tenders Portal\n                    \u003C\u002Ftd>\n                    \u003Ctd class=\"px-4 py-3 border border-slate-200 dark:border-slate-700\">\n                      Daily\n                    \u003C\u002Ftd>\n                  \u003C\u002Ftr>\n                  \u003Ctr class=\"border-b border-slate-200 dark:border-slate-700\">\n                    \u003Ctd class=\"px-4 py-3 border border-slate-200 dark:border-slate-700\">\n                      Publications\n                    \u003C\u002Ftd>\n                    \u003Ctd class=\"px-4 py-3 border border-slate-200 dark:border-slate-700\">\n                      PubMed, OpenAlex, CrossRef\n                    \u003C\u002Ftd>\n                    \u003Ctd class=\"px-4 py-3 border border-slate-200 dark:border-slate-700\">\n                      Weekly\n                    \u003C\u002Ftd>\n                  \u003C\u002Ftr>\n                  \u003Ctr>\n                    \u003Ctd class=\"px-4 py-3 border border-slate-200 dark:border-slate-700\">\n                      Company Data\n                    \u003C\u002Ftd>\n                    \u003Ctd class=\"px-4 py-3 border border-slate-200 dark:border-slate-700\">\n                      Bundesanzeiger, Handelsregister, Unternehmensregister\n                    \u003C\u002Ftd>\n                    \u003Ctd class=\"px-4 py-3 border border-slate-200 dark:border-slate-700\">\n                      Monthly\n                    \u003C\u002Ftd>\n                  \u003C\u002Ftr>\n                \u003C\u002Ftbody>\n              \u003C\u002Ftable>\n            \u003C\u002Fdiv>\n\n            \u003Cp class=\"text-slate-600 dark:text-slate-400 mb-6 leading-relaxed\">\n              Altogether, the data corpus (ORSD — Open Regulatory Signal Dataset) comprises over 5,000\n              companies, ~50,000 regulatory signals, ~20,000 patents, and ~10,000 clinical\n              trials. The data is orchestrated via Apache Airflow and updated daily.\n            \u003C\u002Fp>\n\n            \u003Ch2 class=\"text-3xl font-bold mb-6 mt-12\">The Illusion of Data Volume\u003C\u002Fh2>\n            \u003Cp class=\"text-slate-600 dark:text-slate-400 mb-6 leading-relaxed\">\n              More data sounds like better results. In practice: data volume without\n              context is noise.\n            \u003C\u002Fp>\n            \u003Cp class=\"text-slate-600 dark:text-slate-400 mb-6 leading-relaxed\">\n              A simple example: the FDA database contains every 510(k) clearance since 1976 —\n              over 200,000 entries. A keyword search for \"PCR\" finds ~5,000 of them. Which\n              of these 5,000 are relevant for a manufacturer of real-time PCR kits in 2026?\n              Which are obsolete (technologically outdated)? Which signal current\n              market activity?\n            \u003C\u002Fp>\n            \u003Cp class=\"text-slate-600 dark:text-slate-400 mb-6 leading-relaxed\">\n              Without semantic context and temporal classification, the answer remains: \"You'll\n              have to figure that out yourself.\"\n            \u003C\u002Fp>\n\n            \u003Ch2 class=\"text-3xl font-bold mb-6 mt-12\">From Data Silo to Knowledge Graph\u003C\u002Fh2>\n            \u003Cp class=\"text-slate-600 dark:text-slate-400 mb-6 leading-relaxed\">\n              The key difference with CI is not the number of data sources —\n              but how they are linked in a semantic knowledge graph.\n            \u003C\u002Fp>\n            \u003Cp class=\"text-slate-600 dark:text-slate-400 mb-6 leading-relaxed\">\n              Data from all global public sources is not simply collected in a database.\n              Instead, it is modeled as a graph (Dgraph + PostgreSQL) in which every\n              entity is connected to others:\n            \u003C\u002Fp>\n            \u003Cul class=\"space-y-3 text-slate-600 dark:text-slate-400 mb-8 list-disc pl-6\">\n              \u003Cli>An FDA clearance is linked to the manufacturer\u003C\u002Fli>\n              \u003Cli>The manufacturer is linked to its patents\u003C\u002Fli>\n              \u003Cli>The patents are linked to the underlying technologies\u003C\u002Fli>\n              \u003Cli>The technologies are linked to grant programs that address them\u003C\u002Fli>\n            \u003C\u002Ful>\n            \u003Cp class=\"text-slate-600 dark:text-slate-400 mb-6 leading-relaxed\">\n              This structure enables graph traversal: instead of a flat list of results,\n              the user receives a contextual path — for example, \"FDA clearance for\n              manufacturer X → its patent Y → funding focus Z, which matches the technology\".\n            \u003C\u002Fp>\n\n            \u003Ch2 class=\"text-3xl font-bold mb-6 mt-12\">Open Data by Default\u003C\u002Fh2>\n            \u003Cp class=\"text-slate-600 dark:text-slate-400 mb-6 leading-relaxed\">\n              All raw CI data is openly licensed as ORSD (Open Regulatory Signal Dataset)\n              (ODbL v1.0 for the database, CC0 1.0 for content). Anyone can use,\n              download, and build upon the data for free.\n            \u003C\u002Fp>\n            \u003Cp class=\"text-slate-600 dark:text-slate-400 mb-6 leading-relaxed\">\n              The strategy: open raw data establishes ORSD as the standard in the\n              regulatory intelligence space. The more users that consume the data, the more\n              improvements flow back into the dataset through ODbL reciprocity.\n            \u003C\u002Fp>\n            \u003Cp class=\"text-slate-600 dark:text-slate-400 mb-6 leading-relaxed\">\n              CI's premium product lies not in the data itself — but in the\n              contextual assessment, semantic matching, and prioritization. Additionally,\n              CI runs the entire pipeline on self-hosted foundation models (NVIDIA DGX\n              Spark) — no data egress, no US cloud dependency, fully GDPR-\n              and EU AI Act compliant.\n            \u003C\u002Fp>\n\n            \u003Ch2 class=\"text-3xl font-bold mb-6 mt-12\">Conclusion\u003C\u002Fh2>\n            \u003Cp class=\"text-slate-600 dark:text-slate-400 mb-6 leading-relaxed\">\n              Global public sources are a good starting point. But only semantic linking\n              in a knowledge graph turns this data into usable business intelligence. Those who\n              merely collect data have plenty of information — but little insight.\n            \u003C\u002Fp>","2026-01-01","Contextual Intelligence aggregates data from global public sources. The critical difference lies not in the volume, but in the semantic linking.",null,"Global Data Sources — Why More Data Alone Is Not Enough",3,"why-more-data-alone-is-not-enough",[13],"signal-intelligence",1786979507459]