Functional Design XPLR
Last updated
Last updated
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.
To advance an AI solution from strategic planning to detailed design, users can seamlessly transfer solutions from Portfolio XPLR to Hubble design:
Navigate to the Portfolio XPLR interface
Locate the desired AI solution in your portfolio list
Click the telescope icon 🔠adjacent to the solution name
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.
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.
Status: Current development phase (Not Started, In Progress, Completed)
Result: Implementation outcome (Pass/Fail)
Solution details: Click on the dropdown arrow next to any solution to expand and view associated agents and components.
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.
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.
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.
Define data inputs, update frequency, integration sources, and formats.
Table format display
Each row includes:
Knowledge data – Identify data categories (e.g., customer data, transaction logs).
Integration requirements – Define system APIs or interfaces required.
Format – Specify the format (e.g., JSON, CSV, XML).
Type – Classify data (structured, unstructured, semi-structured).
Frequency of update – Choose how often the data refreshes.
Source systems – Indicate databases or third-party systems.
Attachments – Upload supporting documents for clarity.
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.
Upload relevant attachments.
Click ‘Next’ to continue.
Evaluate suggestions in the popup.
Approve AI-generated fields or manually revise and continue.
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.
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.
Generate and review time and cost estimates for completing the solution.
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.
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 the design document’ to preview it in the browser.
Click ‘Download document’ to save the PDF locally.
Deploy and finalize
Click ‘Build in ZBrain’ to begin deployment; you will be redirected to the Agents page in ZBrain Builder.
Click ‘Save’ to finalize and exit the configuration process.
Upon completion of the configuration workflow, organizations can choose between:
Direct ZBrain implementation:
Use the ‘Build in ZBrain’ option for immediate deployment
Leverage ZBrain's integrated development environment
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.
Configuration access: Select the Edit action button to enter the detailed configuration workflow.
