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Siebel Genie

Summary

For decades, Oracle’s Siebel CRM has excelled as a highly flexible, mission-critical Enterprise CRM solution, providing a trusted foundation for customer experience and to manage heavily integrated operational data. Our customers have long relied on the key strengths of our robust data model, our deep industry and specialized process capabilities across the full CRM spectrum, our best-in-class security controls, and flexible extensibility options.

The combination of Siebel CRM on AI makes for an exciting transformation from “system of record” to “system of intelligence” by activating your CRM data. Your data is a strategic asset built over decades, and it is crucial to leverage it in your organizational AI initiatives. This white paper solution introduces the concept of “Siebel Genie”, a solution that extends access to that data using natural language, while preserving the governance and security posture of Siebel CRM.

Overview

Siebel Genie is built with Oracle Select AI, Autonomous Database 23ai, and Oracle APEX. The solution converts natural-language questions into governed SQL statements, executed on the existing Siebel CRM schema. The approach allows organizations to:

  • Democratize insight – business users ask questions directly; no SQL skills needed.
  • Preserve governance – leverages Siebel view-mode security; execution is read-only and auditable.
  • Avoid core changes – implementation of Siebel Genie should not impact existing Siebel deployments or configurations.

The modular nature of Siebel Genie allows organizations to adopt similar solutions incrementally. Anything is possible, starting with a simple use-case for super users or specific personas, to scaling to additional business domains as needed. Siebel Genie is a simple solution to inspire the broader adoption of AI-driven use cases across business processes.

Full details of this solution are included in the whitepaper.

New Opportunities for Data Insights

Siebel CRM houses a wealth of high-quality operational data – opportunities, service requests, contact interactions, and more – governed by mature view-mode security. Yet, potential “insights” remain locked within data – behind screens, reports, or SQL expertise. The rising availability of generative-AI tooling turns that latent asset into an immediate opportunity: empower every authorised employee to explore Siebel data using natural language, shorten decision cycles, and amplify the return on an existing CRM investment.

Siebel’s rich metadata and view-mode rules already describe both the data relationships and the access constraints. Siebel Genie uses these assets “as-is”. It converts a plain-language question into a governed, read-only SQL statement and returns the results in the same session – without touching Siebel’s schema or security model. The same orchestration can be extended to support predictive scoring, proactive service scheduling, guided chats as needed. However, the core problem that Siebel Genie solves is to enable natural language to governed SQL queries.

Solution

Siebel Genie introduces a natural language interface for accessing Siebel CRM data – allowing users to ask questions in natural language and receive structured, secure answers. It removes the need to navigate complex UIs or understand SQL, while preserving Siebel’s access rules, metadata integrity, and governance model.

Siebel Genie can be a fantastic solution for:

  • Business users who need on-demand access to insights and depend on accurate data for decision-making.
  • IT teams looking to modernize user interaction or improve adoption for super users and senior leaders without disrupting the core Siebel CRM application.

The end-to-end process of converting plain text to data involves three stages:

  • User Prompt – A user enters a question in the Siebel Genie APEX UI
  • AI Orchestration – Modular agents built using Oracle Select AI: Identify the relevant Siebel metadata > Enforce Siebel view-mode security and > Generate the final SQL statement.
  • Query Execution – The read-only SQL query runs on Autonomous Database 23ai against the Siebel schema, and the resulting data is returned to the UI as a data table, which users can manipulate into charts as needed. Users can additionally review the underlying query if required.

Pre-requisites

    Before implementing Siebel Genie, ensure that your environment and access configurations meet the following prerequisites.

  • OCI Tenancy: Active tenancy with permissions to manage Autonomous Database, Object Storage, and IAM.
  • Oracle ADB 23ai: Siebel schema must be hosted on a 23ai instance to support Select AI and vector search.
  • Oracle APEX: Must be enabled on the same database instance to serve as the user interface.
  • Oracle Select AI: Should be enabled and connected to a supported AI provider (e.g., OCI GenAI, OpenAI).
  • OCI Object Storage: Used for storing domain-specific metadata in natural language format for RAG.

Architecture

Siebel Genie has a modular architecture and leverages Oracle Select AI, Oracle APEX, and Oracle Autonomous Database 23ai to deliver natural language querying – without modifying the core Siebel application or bypassing its governance model.

    Siebel Genie is composed of three core architectural layers:

  • User Interaction: Oracle APEX provides the user interface for entering natural language prompts and viewing query results. It also manages contextual session data, including authenticated identity, role, and position.
  • AI Orchestration: This layer governs the secure translation of user prompts into executable SQL. It includes modular agents that interact with Oracle Select AI through domain-specific profiles:
    • The Metadata Agent identifies business components, relationships, and key schema objects based on the query context.
    • The View Mode Agent enforces Siebel’s access control logic by checking user role, position, and view mode privileges using metadata and access-control mappings.
    • The Query Builder Agent constructs the final SQL using identified metadata & view-mode.
  • Query Execution: The final SQL query is executed against the Siebel schema within Oracle Autonomous Database 23ai. The results are returned to the APEX UI for visualization in tabular or chart formats.

The following figure captures the different components that are utilized to build Siebel Genie:

Tech stack

The diagram below highlights at a high level the architectural view of the solution:

Architecture Diagram

Implementation Steps

Siebel Genie is implemented in the following sequence:

  • Domain Metadata Preparation: Extract Siebel metadata and convert it into AI-consumable, natural language descriptions.
  • Select AI Configuration: Set up credentials and profiles for schema-based querying, access validation, and RAG.
  • Vector Index Creation: Build a vector store of domain metadata to support contextual enrichment using RAG.
  • AI Agent Workflow: Implement agents that handle metadata resolution, view mode identification & final SQL generation.
  • UI Integration: Build the natural language interface using Oracle APEX.
  • Validation: Test prompt processing, security enforcement, and result accuracy.

Full implementation details are included in the whitepaper.

Benefits

Siebel’s metadata depth and view-mode security already hold the ingredients for governed self-service analytics. Siebel Genie unlocks that latent value with an incremental layer: plain-language prompts converted into read-only SQL, executed on the existing schema, and delivered back to users in seconds. Within this solution and recipe, there are measureable results:

  • Why it matters: current reporting backlogs and cross-object questions slow decision-making.
  • How it works: a three-stage flow of prompt → AI orchestration → governed execution.
  • What it adds: the final capability in an evolution that began with dashboards and now culminates in open-ended, auditable NLQ.
  • How to deploy: a four-phase blueprint that starts small and scales safely.
  • How to measure success: a recommended KPI set covering speed, effort, adoption, quality, and governance.

Conclusion

While Siebel Genie makes it possible to access most Siebel data using natural language, it is not intended to replace existing screens or dashboards for data lookups. Users should continue using existing tools for routine lookups like “open opportunities”. Siebel Genie is most effective when applied to dynamic, cross-object, or exploratory questions that benefit from conversational input and flexible reasoning – helping users improve decision speed and data-driven actions without waiting on IT.

Get your copy of the whitepaper here!

For more insights or design partnerships, reach out to us at siebel_coe_grp@oracle.com