2026 Demo Update
This ShopAI.ca demo walks from project creation to exportable code across requirements, use cases, data models, architecture, and deployment. The experience looks simple on the surface because the system is carrying project context, compliance, and artifact history underneath every generation.
The example project is a Canadian sports game-tape analysis platform with PIPEDA, role-based access, and WCAG 2.1 AA requirements. The page shows both what Sarah sees in the product and how the private admin workspace keeps the output consistent, exportable, and ready for coaching, analyst, and development teams.
The magic is not one giant prompt. It is context-aware routing that changes output by view, artifacts, and compliance needs.
How the Demo Opens
The first screen defines the project metadata that the generation system will reuse across every later stage.
What Sarah sees
Sarah creates a game-tape analysis project, sets the industry, description, tech stack, and access requirements, then clicks create once.
Welcome to ShopAI.ca
Create your first project
[Project Name]
"Game Tape Analysis Platform"
[Industry]
"Sports / Video Analysis"
[Description]
"Review game tape, tag possessions,
detect breakdowns, generate coach notes"
[Tech Stack]
"Node.js + React + PostgreSQL + Python"
[Compliance]
[x] PIPEDA (Canada)
[x] Role-based access
[x] WCAG 2.1 AA (Accessibility)
[Create Project]
Demo Stages
The same project keeps evolving, but the output format changes by view so the user never has to restate the entire system.
Create the project and lock in industry, tech stack, and Canadian compliance requirements.
Generate functional requirements from short prompts like breakdown tagging, clip search, or coach notes.
Switch context and generate PlantUML use case diagrams and linked use case specs for analysts, coordinators, and coaches.
Infer entities, relationships, ER diagrams, DDL, and migrations for games, clips, players, possessions, and notes.
Generate architecture, Terraform, checklists, and rollout guidance for video ingestion and secure analyst workspaces.
Produce runnable code, docs, migrations, CI/CD files, and optional GitHub push.
Stage 1
Sarah types a short capability and gets a structured requirement with acceptance criteria, priority, and linked operational constraints.
What Sarah sees
She types "tag defensive breakdowns from game tape" and the system produces FR-001 with acceptance criteria, priority, and links to related non-functional requirements. A second prompt like "search clips by possession, player, and play type" creates FR-002 immediately after.
Prompt
"tag defensive breakdowns from game tape"
Result
FR-001: Defensive Breakdown Tagging
Description
System tags each defensive breakdown clip in sequence.
Acceptance Criteria
- Tag quarter, clock, possession, and lineup
- Mark missed rotations, late closeouts, and coverage gaps
- Attach a coaching note to each flagged clip
- Allow analyst review before save
Related prompt
"search clips by possession, player, and play type"
Stage 2
Once Sarah clicks into Use Cases, the system switches context and the same natural-language style prompt becomes diagram generation.
What Sarah sees
Sarah types "video analyst reviewing third-down breakdowns and exporting clips" and gets a PlantUML use case diagram, detected actors, related functional requirements, and a structured use case spec for flows like Review Clip and Export Cutup.
Prompt
"video analyst reviewing third-down breakdowns and exporting clips"
Result
Actors
- Analyst
- Coordinator
- Coach
Use Cases
- Review clip
- Tag breakdown
- Export cutup
- Share coach notes
Artifacts Found
- FR-001 linked
- FR-002 linked
- New actors detected automatically
Stage 3
The system moves from screen-level flows into the entities, relationships, and schema required to make the application real.
What Sarah sees
Sarah asks what entities are needed for games, clips, players, possessions, tags, and coaching notes. The app infers Game, Clip, Player, Possession, Tag, and Staff Note, then generates the conceptual ER view, the logical model, and the physical PostgreSQL schema.
Prompt
"what entities do we need for games, clips, players, possessions, tags, and notes?"
Result
Core Entities
- Game
- Possession
- Clip
- Player
- Tag
- Staff Note
Relationships
- Game -> Possessions -> Clips
- Clip -> Tags
- Clip -> Staff Notes
- Player -> Tagged events
Exports
- Conceptual ER view
- Schema outline
- Migration-ready table set
Stage 4
For infrastructure work, ShopAI.ca can shift from live streaming to background generation for diagrams, Terraform, and checklists in parallel.
What Sarah sees
Sarah asks for an AWS production deployment that ingests game tape, generates clips, and serves secure analyst workspaces. The result includes an architecture diagram, Terraform, a deployment checklist, scaling notes, and configuration with PIPEDA compliance noted clearly.
Prompt
"aws architecture for game tape ingestion, clip generation, and analyst review"
Result
- Secure upload for raw game footage
- Processing workers for clip extraction
- Analyst workspace for review and tagging
- Role-based access to team footage
- Searchable clip library
- Export-ready deployment checklist
Stage 5
At the end of the flow, the user chooses how far the system should go: code only, infrastructure only, or a full repository with docs and deployment assets.
What Sarah sees
Sarah exports a package that includes application code, infrastructure, migrations, docs, CI/CD workflows, and a README. From there, the repo can be cloned, run locally, and pushed to production services like Vercel, Railway, and AWS.
Export Selection
- Frontend
- Services
- Data model
- Infrastructure
- Documentation
- GitHub repository
Result Package
game-tape-analysis-v1.zip
Contents
- analyst-workspace/
- services/
- video-workers/
- schema/
- infrastructure/
- docs/
- .github/
Admin Login
This is the only place the internal mechanics are exposed. The demo pages stay focused on prompts and results.
See every active game, analysis project, and workspace in one admin list.
Review prior prompts, saved outputs, and the sequence of generated artifacts.
Manage requirements, use cases, data models, exports, and revisions from one place.
Set who can view game tape, who can edit notes, and who can export packages.
Choose what gets packaged for teams, environments, or repositories.
Track changes, prompt history, and review status without exposing the build details on the demo page.
Differentiators
The pitch is not just speed. It is structured output, reusable context, and a shorter path from idea to deployable system.
Core product promise
Move from idea to architecture and exportable implementation without restarting the design process at every step.
Canadian angle
PIPEDA, role-based access, and accessibility requirements are set as part of the project, not bolted on at the end.
Operational value
Generated artifacts are structured, exportable, and ready for teams to review, build, and deploy.
Call to action
This page works as a walkthrough for technical buyers who need to understand both the user experience and the architecture behind it.
Walk through the full 60-minute flow with a live project scenario.
Map the views, compliance, and export path for your own application type.
Use this demo as the entry point for a more tailored product or sector-specific workflow.
2026 Demo Positioning
Context-aware AI system design for Canadian businesses.
The value is not just generation speed. It is the ability to keep requirements, diagrams, schema, infrastructure, and code aligned while the user keeps moving.