> For the complete documentation index, see [llms.txt](https://docs.zbrain.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.zbrain.ai/zbrain-documentation/zbrain-xplr/functional-design-xplr.md).
# Functional Design XPLR
### **Overview**
Functional design XPLR represents the final stage in the XPLR framework, transforming prioritized solutions from portfolio XPLR into detailed, implementation-ready blueprints. This final stage seamlessly bridges conceptual strategy and practical execution by decomposing complex AI solutions into specific, manageable components, from individual agents/opportunities to complete solutions. Each component is enhanced with critical planning details, including timeline estimates, impact classification, and implementation status. Through the ICE scoring methodology (Impact, Confidence, Ease), teams can strategically prioritize components and optimize resource allocation to maximize value generation.
Key features enable teams to:
* Monitor real-time implementation progress across solution elements
* Manage component timelines and identify dependencies
* Prioritize development efforts based on business impact metrics
* Generate implementation-ready documentation
Functional design XPLR ensures seamless transition from strategic planning to successful AI solution delivery, providing teams with the precision and clarity needed for effective execution.
### **Initiating solution development with Hubble design**
#### **Transferring solutions from Portfolio XPLR**
To advance an AI solution from strategic planning to detailed design, users can seamlessly transfer solutions from Portfolio XPLR to Hubble design:
1. Navigate to the Portfolio XPLR interface
2. Locate the desired AI solution in your portfolio list
3. Click the telescope icon 🔠adjacent to the solution name
4. The system automatically sends the selected solution to the Hubble design module, where users can define, refine, and plan implementation in detail.
**Hubble design overview**
The Hubble design module is an advanced configuration interface that helps organizations turn prioritized AI solutions into comprehensive, ready-for-implementation specifications. This module offers a comprehensive framework for managing active AI solutions, including feasibility assessment, requirement definition, effort estimation, and cost analysis.
### Managing active AI solutions
#### Solution dashboard overview
The Functional design XPLR interface presents a comprehensive table displaying all active AI solutions with the following key attributes:
* **AI solution (name)**: Unique identifier and project designation
* **Impact type**: Categorization of expected business value (Breakthrough/ Transformative/ Incremental)
* **Time to build**: Estimated development timeline
* **ICE score**: Prioritization metric (Impact, Confidence, Ease)
* **Actions:** Access the executive feedback analysis panel or launch the solution in ZBrain Builder.
* **Status**: Current development phase (Not Started, In Progress, Completed)
* **Result**: Implementation outcome (Pass/Fail)
#### **Interactive features**
* **Solution details**: Click on the dropdown arrow next to any solution to expand and view associated agents and components.
* **Configuration access**: Select the Edit action button to enter the detailed configuration workflow.
### Comprehensive solution configuration process
#### **Step 1: General information**
Capture and review the foundational details of the AI solution being proposed.
**Fields available**
* **Name** – Enter a descriptive and identifiable name for the AI solution.
* **Description** – Provide a comprehensive overview that outlines the purpose, capabilities, and business value of the solution.
* **ICE score** – Adjust sliders for Impact, Confidence, and Ease (scale: 0–10) to prioritize ideas based on business assessment.
* **Type of impact** – Select the appropriate category (Breakthrough/ Transformative/ Incremental) from the dropdown.
**Configuration steps**
* Review and update all pre-populated fields.
* Adjust the ICE score sliders according to your evaluation.
* Select the Type of impact from the dropdown list.
* Click ‘Next’ to move forward.
* A popup appears with the message:
*"Generate AI - Some of the data might be AI-generated. Please review and approve only if it appears accurate."*
* Click ‘Approve’ to accept and move forward to functional requirements, or ‘No’ to modify manually.
* On approval, the system navigates to the next step.
#### **Step 2: Functional requirements**
Define the core functionalities of the AI solution.
**Configuration sections**
* **User stories** – Add functional needs as agile-format stories.
* **UX/UI requirements** – Define interface behaviors and design expectations.
* **Business rules** – Document logic flows and operational constraints.
* **Outputs and reports** – Specify expected visualizations and reporting features.
**Configuration steps**
* Use the rich text editor to add or edit details in each section.
* Click the glitter icon ✨ in the top-right corner to generate AI-suggested content.
* Click ‘+' or '–' to add or remove specific requirement entries.
* Click ‘Add new section’ to create additional custom sections as needed.
* Attach supporting files for each requirement, if applicable.
* Click ‘Next’ after completing all entries.
* Review AI-generated content in the popup.
* Click ‘Approve’ to proceed, or ‘No’ to continue editing manually.
#### **Step 3: Non-functional requirements**
Capture technical constraints and expectations for system reliability, scalability, and performance.
**Configuration sections**
* **Current technology stack** – List technologies, frameworks, and platforms used.
* **Data security** – Define protocols, compliance needs.
* **Network and infrastructure** – Specify environment needs and deployment setup.
* **Usage requirements** – Document performance, load, and uptime requirements.
**Configuration steps**
* Add/edit requirements using the rich text editor.
* Click the glitter icon ✨ in the top-right corner to generate AI-suggested content.
* Use the ‘+' or '–’ icons to manage specific requirement fields.
* Click ‘Add new section’ for custom technical parameters.
* Attach any necessary technical documentation.
* Click ‘Next’ to proceed.
* Review suggestions in the popup.
* Choose ‘Approve’ to continue or ‘No’ for manual adjustments.
#### **Step 4: Inputs and data sources**
Define data inputs, update frequency, integration sources, and formats.
**Table format display**
Each row includes:
* **Data source:** The name of the input data source being assessed
* **Technology**: Type of tool or platform (e.g., Relational Database, Spreadsheet Software)
* **Essential/Optional**: Whether the source is critical for implementation
* **Access**: Whether the data is internal or external
* **Quality**: Graded as High, Medium, or Low
* **Type**: Structured, Unstructured, or Semi-Structured
* **Readiness score**: A numerical score reflecting how ready the data is for use
**Configuration steps**
* Click the ‘+ Add input or data source’ button to define data requirements for your agent.
* Click '+' to add a new row/data source.
* Fill in all fields accurately for each data item.
* Click ‘Next’ to continue.
* Evaluate suggestions in the popup.
* Approve AI-generated fields or manually revise and continue.
#### **Step 5: PoC (Proof of concept) requirements**
Outline the scope, objectives, deliverables, and success criteria for the PoC phase.
**Configuration sections**
* **Scope of work** – Define the project boundaries and focus areas.
* **Data requirements** – List the required datasets and sources.
* **Deliverables** – Clarify expected outcomes and demos.
* **Acceptance criteria** – Establish measurable conditions for PoC success.
**Configuration steps**
* Add/edit content using the rich text editor.
* Click the glitter icon ✨in the top-right corner to generate AI-suggested content.
* Click ‘+' or '–’ to manage individual requirements.
* Click ‘Add new section’ to include any additional parameters.
* Attach relevant documentation for reference.
* Click ‘Next’ when completed.
* Review and validate AI suggestions.
* Click ‘Approve’ to accept or ‘No’ to revise manually.
#### **Step 6: Integrated and scaled requirements**
Detail the production-level expectations and readiness requirements for the full deployment.
**Configuration sections**
* **Statement of work** – Provide a complete implementation plan.
* **Deliverables** – Final list of features, modules, and systems to be delivered.
* **Acceptance criteria** – Define what qualifies the solution as ready for production.
**Configuration steps**
* Use the rich text editor to enter required fields.
* Click the glitter icon ✨ in the top-right corner to generate AI-suggested content.
* Click ‘+' or '–’ to adjust field entries.
* Click ‘Add new section’ for project-specific additions.
* Attach relevant project plans or design files.
* Click ‘Next’ to proceed to effort estimation.
* Approve AI-drafted content or reject and edit manually as needed.
#### **Step 7: Effort & cost estimation**
Generate and review time and cost estimates for completing the solution. The enhanced accuracy estimator provides detailed cost breakdowns, timelines, and resource requirements, improving overall planning.
**Fields and indicators**
* **Status indicator** – Shows "No data sent yet" if estimation is pending.
* **Estimated time & cost** – System-calculated projections.
* **Embedded solution status** – Displays internal assessment status.
* **Vendor information** – External vendor estimates.
**Configuration steps**
* Click ‘Get estimates’ to initiate calculation.
* Review the detailed estimation report.
* Click ‘Approve’ to finalize the estimates.
* Click ‘Next’ to proceed to deployment handshake.
* Review AI-generated estimates.
* Choose ‘Approve’ or revise as needed.
#### **Step 8: Deployment handshake**
Finalize the deployment process and generate the design document.
**Check for an existing document**
* If no design document has been created yet, you will see: No document created yet.
**Create the design document**
* Click ‘Generate design document’ to compile your current configuration.
* A confirmation popup will appear: "Are you sure you want to generate the PDF of currently filled data?"
* Select ‘Save and generate’ to proceed.
**Preview or download the document**
* Click ‘View document’ to preview it in the browser.
* Click ‘Download document’ to save the PDF locally.
**Deploy and finalize**
* Click 'Build in ZBrain' to begin the deployment process. This will launch the ZBrain integration workflow and redirect you to the authentication interface.
#### Authentication steps
**Initial setup**
1. When the "Enable ZBrain integration" dialog appears, you will be prompted to log in to your ZBrain account.
2. Click ‘Login to ZBrain’ to authenticate using your ZBrain credentials.
Once authenticated successfully, you will gain access to solution-building and deployment capabilities. This integration establishes secure, seamless communication between your current platform and ZBrain’s services, supporting your project deployment needs.
**Note:** This authentication step is critical for security, ensuring that only authorized users can access ZBrain via the integrated interface.
After completing authentication, you will be automatically redirected to the Agents page in ZBrain Builder to start building your agents.
Click ‘Save’ to finalize and exit the configuration process.
### **Implementation pathways**
Upon completion of the configuration workflow, organizations can choose between:
1. **Direct ZBrain implementation**:
* Use the ‘Build in ZBrain’ option for immediate deployment
* Leverage ZBrain's integrated development environment
2. **Organizational implementation:**
* Generate and download the comprehensive design document tailored to your solution requirements.
* Use the detailed specifications to enable internal teams within your organization to implement the solution effectively.
The generated design document contains all configured requirements, technical specifications, and implementation guidelines necessary for successful AI solution deployment.
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