Document Processing · Enterprise Knowledge Search
Sphere needed a cost-effective but enterprise-grade data platform for its OrgBrain AI offering that could handle massive mixed-format data sets, support AI retrieval and generation, and satisfy stringent customer security and data residency requirements without forcing a small startup team to stitch together multiple specialty databases.
Reported impact
Sphere launched an enterprise AI platform without assembling multiple specialized databases, kept a lean development model, and sped regulated customer workflows such as grant proposal drafting from as long as one to two weeks to about five minutes while maintaining strong security and data residency controls.
Sphere built its OrgBrain platform on Oracle Database running in Oracle Autonomous Database so one converged system could store and query relational, vector, spatial, JSON, and graph data without the operational burden of separate engines. Enterprise knowledge sources and diverse file types are centralized in Autonomous Database, where Oracle AI Vector Search and native large language model integration power retrieval across documents, audio, video, and structured records. Oracle REST Data Services exposes the database model to applications quickly, while Oracle JSON Relational Duality Views lets developers work with JSON representations backed by relational storage. The result is a secure, scalable AI platform with one data-layer security architecture that can support regulated enterprise workflows such as grant proposal drafting and compliance-oriented decision support.
Enterprise documents, audio, video, and other knowledge sources are stored together in Oracle Autonomous Database; Oracle AI Vector Search and integrated LLM capabilities retrieve and rank relevant content; Oracle REST Data Services and JSON Relational Duality Views expose the results to OrgBrain applications that generate outputs such as evidence-based grant proposal drafts and governance workflows.
Sphere created an AI platform using a single database that can draw on different data formats, including relational, vector, spatial, JSON, and graph.
The database’s AI Vector Search capability processes thousands of substeps in queries, reducing processing time from minutes to seconds.
government agencies using Sphere could cut the time to create a first draft of a notice of funding opportunity from a week or two to about five minutes.
Review how this reference was scored and trace each normalized claim back to its documented source.
Scoring methodology
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Traceability
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
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