PitchHub
Reimagining Applications with AI
Google's sales enablement team creates hundreds of MVPs each quarter, demonstrating how Vertex AI integrates with diverse applications.
As consultants, we ran a pilot project to prove we could take over this process, delivering scalable MVP development for Google.
Revamping a medical product for the US market to ensure ADA compliance and drive wider adoption.
My client, who offers a Medicare product suite for the U.S. market, was facing challenges because their offerings X not ADA-compliant. As the UX Lead on this project, I led the redesign of their flagship medical product and conducted UAT with real users of assistive technologies who have visual disabilities.
Client
Google + Airbus
Duration
2 Weeks
Industry
AI & Cloud Computing
Scope of Work
AI-Driven
Enterprise Saas
No-code
Validating Manufacturing Diagrams for Airbus.
One of the key use cases to showcase Vertex AI’s real impact was with Airbus. We applied AI to streamline the validation of complex, legacy manufacturing diagrams. The system cross-referenced each diagram with the bill of materials and part data, automatically completing missing details. When gaps couldn’t be resolved, the AI clearly highlighted them on both the diagram and validation tables, ensuring accuracy, efficiency, and production readiness.
My Process
Working on rapid MVP builds meant balancing speed with clarity. My process moved from understanding requirements to validating storyboards with stakeholders, designing high-fidelity prototypes within FlutterFlow's constraints, and closing the loop with final reviews before handoff.
Requirement Analysis
Reviewed requirement docs to understand goals, users, and constraints.
Storyboard Validation
Created and showcased storyboards to align on user flow and demo expectations.
Prototype Design
Designed interactive prototypes and collaborated with developers to build in FlutterFlow.
Final Review & Sign-off
Conducted final walkthrough with stakeholders and incorporated feedback.
My Process
Working on rapid MVP builds meant balancing speed with clarity. My process moved from understanding requirements to validating storyboards with stakeholders, designing high-fidelity prototypes within FlutterFlow's constraints, and closing the loop with final reviews before handoff.
Requirement Analysis
Reviewed requirement docs to understand goals, users, and constraints.
Storyboard Validation
Created and showcased storyboards to align on user flow and demo expectations.
Prototype Design
Designed interactive prototypes and collaborated with developers to build in FlutterFlow.
Final Review & Sign-off
Conducted final walkthrough with stakeholders and incorporated feedback.
Storyboard
The storyboard was carefully designed to map out each step of the user's actions alongside the corresponding generative AI responses. At every stage, we outlined what the user would do and how the AI would assist, whether by validating data, completing missing parts, or highlighting gaps.
*Replica of the original project artifact, unchanged to reflect real delivery under time constraints. Use the custom zoom to view research text.
Wireframe to High Fidelity
For this project, we began by creating quick wireframes to ensure alignment and shared understanding across the team. Once aligned, we developed high-fidelity Figma prototypes, all while keeping in mind the constraints of FlutterFlow, the no-code platform we used to deliver the final product. This ensured the design was both realistic and achievable from concept to final build.
Wireframe to High Fidelity
For this project, we began by creating quick wireframes to ensure alignment and shared understanding across the team. Once aligned, we developed high-fidelity Figma prototypes, all while keeping in mind the constraints of FlutterFlow, the no-code platform we used to deliver the final product. This ensured the design was both realistic and achievable from concept to final build.
Prompt to Code
For the project, we originally built it in FlutterFlow, but for the purpose of showcasing it in my portfolio, I created a working prototype using Claude.
Prototype features & interactions
Filter Part Cards → Sort by AI processed, errors, or all.
Open Part Details → Click on any card to view detailed screen.
Upload Flow → Simulate uploading diagrams and BOM data.
Inspect Diagram → Identify AI-marked missing information.
Cross-Reference Tables → See issues mapped with reference IDs.
Scrollable Tables → Navigate Pre-BOM and Post-BOM data.
View Part Details Panel → Review part metadata in the side panel.
Measurable Impact
3.3X Faster
Faster turnaround validating complex manufacturing diagrams against the bill of materials, compared to the fully manual workflow.
Value Adddition for Our Client
01.
Significant Time
Savings
Reduced validation and structuring time by ~70 percent, enabling faster decision making and improved engineering efficiency.
02.
AUTOMATED STRUCTURING AND ERROR DETECTION
AUTOMATED ERROR DETECTION
AI surfaces mismatches and missing fields against the bill of materials — catching issues long before they reach production.
03.
STANDARDIZED PROJECT OUTPUTS
Converted unstructured inputs into consistent, Gantt-ready project plans, ensuring clarity, alignment, and scalability across teams.
Design products people love.
Reimagining Applications with AI
PitchHub
Google's sales enablement team creates hundreds of MVPs each quarter, demonstrating how Vertex AI integrates with diverse applications. As consultants, we ran a pilot project to prove we could take over this process, delivering scalable MVP development for Google.
Client
Google (Vertex AI – Sales Enablement Team)
Duration
2 weeks
Industry
AI & Cloud Computing
Scope of work
SaaS
AI-Driven
No-code
Validating Manufacturing Diagrams for Airbus.
One of the key use cases to showcase Vertex AI’s real impact was with Airbus. We applied AI to streamline the validation of complex, legacy manufacturing diagrams. The system cross-referenced each diagram with the bill of materials and part data, automatically completing missing details. When gaps couldn’t be resolved, the AI clearly highlighted them on both the diagram and validation tables, ensuring accuracy, efficiency, and production readiness.
My Process
Working on rapid MVP builds meant balancing speed with clarity. My process moved from understanding requirements to validating storyboards with stakeholders, designing high-fidelity prototypes within FlutterFlow's constraints, and closing the loop with final reviews before handoff.
Requirement Analysis
Reviewed requirement docs to understand goals, users, and constraints.
Storyboard Validation
Created and showcased storyboards to align on user flow and demo expectations
Prototype Design
Designed interactive prototypes and collaborated with developers to build in FlutterFlow
Final Review & Sign-off
Conducted final walkthrough with stakeholders and incorporated feedback.
Storyboard
The storyboard was carefully designed to map out each step of the user's actions alongside the corresponding generative AI responses. At every stage, we outlined what the user would do and how the AI would assist, whether by validating data, completing missing parts, or highlighting gaps.
*Replica of the original project artifact, unchanged to reflect real delivery under time constraints. Use the custom zoom to view research text.
Wireframe to High Fidelity
For this project, we began by creating quick wireframes to ensure alignment and shared understanding across the team. Once aligned, we developed high-fidelity Figma prototypes, all while keeping in mind the constraints of FlutterFlow, the no-code platform we used to deliver the final product. This ensured the design was both realistic and achievable from concept to final build.
Prompt to Code
For the project, we originally built it in FlutterFlow, but for the purpose of showcasing it in my portfolio, I created a working prototype using Claude.
Prototype features & interactions
Filter Part Cards → Sort by AI processed, errors, or all.
Open Part Details → Click on any card to view detailed screen.
Upload Flow → Simulate uploading diagrams and BOM data.
Inspect Diagram → Identify AI-marked missing information.
Cross-Reference Tables → See issues mapped with reference IDs.
Scrollable Tables → Navigate Pre-BOM and Post-BOM data.
View Part Details Panel → Review part metadata in the side panel.
Measurable Impact
3.3X Faster
Faster turnaround validating complex manufacturing diagrams against the bill of materials — compared to the fully manual workflow.
VALUE ADDS FOR OUR CLIENT
01.
Significant Time
Savings
Reduced validation and structuring time by ~70 percent, enabling faster decision making and improved engineering efficiency.
02.
AUTOMATED STRUCTURING AND ERROR DETECTION
AI surfaces mismatches and missing fields against the bill of materials — catching issues long before they reach production.
03.
STANDARDIZED PROJECT OUTPUTS
Converted unstructured inputs into consistent, Gantt-ready project plans, ensuring clarity, alignment, and scalability across teams.