Enterprise Knowledge Search · Security and Compliance
Accenture needed a governed multitenant knowledge base that could power agentic applications across the technology delivery lifecycle without letting AI-generated SQL or Model Context Protocol components bypass client data isolation.
Reported impact
The platform now supports dozens of agentic applications and well over 100 users, scales to 300 simultaneous sessions with most queries returning in 15 seconds or less, cuts RFP response work from weeks to days, and saves an estimated $75,000 to $80,000 per RFP response while helping teams deliver client value sooner.
Accenture built its Advanced Insights Engine on Oracle Cloud Infrastructure (OCI) as a multitenant knowledge base that supports agentic applications across the technology delivery lifecycle. Curated client and delivery content is stored in Oracle Autonomous AI Database 26ai, where teams can reuse prior pursuit, requirements, fit-gap, design, code, test, and Hyper Care knowledge. AI agents and Model Context Protocol servers generate SQL dynamically against this shared corpus, while Oracle Deep Data Security applies declarative, engagement-specific authorization as each query is processed in the database. That lets Accenture automate work such as assembling RFP responses and architecture diagrams from curated documents without depending on every upstream component to enforce isolation correctly. OCI provides the cloud environment for the governed platform, and the data-layer control keeps client information isolated as the system scales to many users and agentic workflows.
Curated delivery documents and client information are stored in Oracle Autonomous AI Database 26ai on OCI, where AI agents and Model Context Protocol servers generate SQL against the multitenant knowledge base. Oracle Deep Data Security enforces engagement-specific authorization as the database processes each query before results are returned to applications supporting RFP work and later delivery phases.
Accenture also demonstrated that the platform can scale for 3X concurrent demand—300 simultaneous sessions with query latency of 15 seconds or less for the majority of queries.
Accenture built the application in a few days on Oracle Cloud Infrastructure (OCI) with Oracle Autonomous AI Database 26ai and Oracle Deep Data Security.
Early sales and requests for proposals (RFP) response development served as the first proving ground; for example, agents can assemble an automated RFP response, including architecture diagrams, from a highly curated document corpus.
Instead of relying on application code to add the appropriate access logic for every query, Oracle Deep Data Security applies authorization as SQL is processed by the database.
The earliest and most measurable gains came from RFP work: response time has dropped from weeks to days, with much higher confidence in quality because teams can draw from a large body of curated information—resulting in an estimated $75,000 to $80,000 saved per RFP response.
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.
The fact sheet reports approximately 814,000 people at fiscal 2026 year-end.
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