An AI-assisted internal helpdesk platform built to help support teams triage tickets faster, surface the right context sooner, and keep AI recommendations reviewable, measurable, and safe.
This is not just a ticket CRUD app. It is a workflow product that combines operational ticket handling with an AI copilot layer, grounded support guidance, human review controls, feedback capture, and dashboard-level AI analytics.
Support teams often lose time on the same problems:
- long tickets take too long to understand
- triage quality varies from one agent to another
- support knowledge lives in scattered notes and tribal memory
- customer replies are inconsistent in tone and quality
- AI can help, but only if it is transparent, reviewable, and measurable
AI Helpdesk Copilot was built to address that gap.
It provides a complete support workflow with:
- ticket queue management
- assignment and status lifecycle
- comments and audit timeline
- AI-assisted category, priority, and team suggestion
- AI-generated summaries and draft replies
- confidence-aware UI behavior
- provider/source transparency
- grounded resolution help from internal knowledge
- similar-ticket surfacing
- ticket history summarization
- rewrite tools for agent productivity
- feedback capture for AI governance
- dashboard analytics for AI usage and outcomes
Most portfolio helpdesk apps stop at:
- create ticket
- update ticket
- assign ticket
- close ticket
This project goes further.
It treats support as an operational workflow and AI as a governed layer inside that workflow.
That means the system does not just generate AI outputs. It also:
- shows where the output came from
- indicates confidence
- allows human accept/reject decisions
- degrades safely with fallback behavior
- stores feedback events
- measures AI usage and performance over time
That is the difference between adding AI for show and designing AI as part of a real product.
The app includes the foundation expected from a real internal helpdesk tool:
- ticket creation
- ticket queue with search and filters
- status lifecycle management
- team assignment
- agent assignment
- ticket comments
- event timeline and audit trail
- dashboard overview
- recent activity feed
This gives the product a real operational backbone before AI is introduced.
The AI layer helps agents by generating:
- predicted category
- predicted priority
- suggested team
- ticket summary
- draft customer reply
The goal is to shorten the time between ticket intake and first meaningful action.
Instead of manually reading every ticket from scratch, agents can start with a structured recommendation and review it quickly.
One of the core product decisions in this project is that AI is never treated as a black box.
Every recommendation can expose its source, such as:
- OpenAI
- Ollama
- rule engine
- rule fallback
The UI also surfaces a confidence signal so that recommendations can be treated differently depending on certainty.
This makes the system more trustworthy, easier to debug, and safer to use in day-to-day support operations.
The AI layer is complemented by grounded support intelligence.
For each ticket, the system can surface:
- matched internal guidance
- recommended checks
- escalation guidance
- relevant support articles
This reduces the risk of treating LLM output as the only source of truth and makes the product feel closer to a real support copilot than a generic chatbot wrapper.
The ticket detail view can surface similar historical tickets based on:
- category overlap
- team overlap
- shared terms
- heuristic similarity scoring
This helps agents quickly understand whether the issue has appeared before and what prior resolution paths may be relevant.
Support tickets often become long and fragmented over time.
To reduce handoff friction, the product generates a structured history summary that can include:
- current state
- what happened so far
- latest meaningful update
- blockers and risks
- next recommended step
This is especially useful when a ticket is reassigned or picked up by a different agent later in the workflow.
Agents can transform AI-generated draft replies into variants better suited for specific contexts.
Supported rewrite modes include:
- Shorter
- More Formal
- More Empathetic
- Customer-Safe
These tools improve day-to-day usability by helping agents adapt communication style without rewriting from scratch.
AI systems fail in real life.
This project was designed with degraded-mode behavior in mind:
- provider switching is supported
- local model output can be validated
- poor rewrite output can be rejected
- rule-based fallback keeps the workflow usable
That means the system remains functional even when a model is unavailable, misconfigured, or returns poor output.
The app records how humans interact with AI outputs.
Examples of captured events include:
- review decisions such as accept or reject
- rewrite usage
- rewrite mode used
- source of the AI output
- timestamps tied to the ticket lifecycle
This creates a real audit trail for AI behavior and opens the door to future evaluation and retraining workflows.
The dashboard does not just show ticket metrics. It also shows AI metrics.
Examples include:
- total analyses
- accepted recommendations
- rejected recommendations
- rewrite usage count
- fallback analysis count
- acceptance rate
This gives the product a strong operations-and-governance story: AI is not just present, it is measurable.
The dashboard gives a quick operational overview of the helpdesk system.
It includes:
- total tickets
- open tickets
- in-progress tickets
- AI-analyzed ticket count
- status breakdown
- recent workflow activity
- AI analytics panel
This makes it useful both for agents and for leads who want visibility into support flow and AI usage.
The ticket queue is built for speed and clarity.
It supports:
- search
- workflow filters
- sorting
- assignment visibility
- team visibility
- readable title hierarchy
- fast entry into ticket detail
This is where the app moves from demo territory into something that feels like a real internal operations tool.
The ticket detail page is the core working surface of the product.
It brings together:
- ticket summary and metadata
- AI copilot panel
- knowledge-grounded resolution help
- ticket history summary
- feedback capture
- ticket actions
- similar tickets
- comments
- full event timeline
This page is where the product story becomes strongest because it shows the operational workflow and the AI workflow side by side.
flowchart LR
A[Angular Frontend] --> B[Spring Boot REST API]
B --> C[(Database)]
B --> D[AI Provider Layer]
D --> E[OpenAI]
D --> F[Ollama Local Model]
D --> G[Rule Engine / Fallback]
B --> H[Ticket Services]
B --> I[AI Services]
B --> J[Feedback Capture]
B --> K[Dashboard Analytics]
J --> C
K --> C
The frontend is built with Angular and provides:
- dashboard
- ticket queue
- ticket detail workspace
- reactive forms
- reusable API service layer
- toast notifications
- workflow-driven UI states
The backend is built with Spring Boot and exposes REST endpoints for:
- tickets
- status updates
- assignment
- comments
- events
- AI recommendations
- rewrite tools
- knowledge guidance
- history summary
- feedback capture
- dashboard analytics
The domain model is persisted through a repository layer and structured around real support entities.
The AI subsystem is provider-aware and can work with:
- OpenAI
- local Ollama models
- rule-based fallback logic
This flexibility was an intentional design choice to support cost control, local experimentation, and resilient degraded-mode behavior.
Core entities include:
- Ticket
- TicketComment
- TicketEvent
- AIRecommendation
- AIFeedbackEvent
- Team
- User
These entities allow the app to model both support workflow and AI workflow in a structured way.
AI suggestions are reviewable before being acted on. The product deliberately avoids silent automation in core support decisions.
Users can see whether a result came from a local model, a hosted provider, or a fallback path.
Confidence is surfaced to the UI so agents can interpret suggestions appropriately instead of treating all outputs equally.
The system remains usable even when model quality drops or a provider is unavailable.
Feedback capture and AI dashboard metrics ensure that the system can evaluate AI usefulness over time.
These decisions reflect a product mindset focused on trust, control, and operational reality.
This project demonstrates experience with:
- Angular application structure and component-driven UI
- Spring Boot REST API design
- service/repository architecture
- DTO-based response modeling
- provider-aware AI integration
- local model experimentation with Ollama
- rule-based fallback systems
- workflow state modeling
- audit trail design
- metrics-oriented product thinking
- frontend/backend contract evolution under active feature growth
Building this project involved more than styling screens.
Some of the real engineering challenges included:
- keeping frontend and backend contracts aligned as the app evolved
- integrating multiple AI provider modes cleanly
- making AI output safe enough for workflow use
- preserving UX clarity on a dense ticket detail page
- storing AI-related feedback as real product data
- turning AI usage into dashboard-level metrics
- handling fallback behavior without breaking user trust
These are the kinds of problems that make workflow products interesting to build.
A strong demo path for this project is:
- Open the dashboard
- Review ticket KPIs and AI analytics
- Open the ticket queue
- Search or filter tickets
- Open a ticket detail page
- Run AI analysis
- Review the AI summary and draft reply
- Use a rewrite tool
- Inspect grounded guidance and similar tickets
- Accept or reject the AI recommendation
- Review feedback capture
- Return to the dashboard and observe analytics changes
This flow shows the full lifecycle from operational intake to AI measurement.
- Angular
- TypeScript
- Reactive Forms
- Router
- Component-based UI architecture
- Spring Boot
- Java
- Spring Web
- Spring Data JPA
- OpenAI integration
- Ollama local model integration
- rule-based fallback logic
- relational database via JPA repositories
Adjust commands if your local folder names differ.
cd helpdesk-backend
./mvnw spring-boot:runcd helpdesk-frontend
npx ng serve --proxy-config proxy.conf.json --openRun Ollama locally and point the backend to a supported model if you want local AI behavior.
Example environment variables:
HELPDESK_AI_PROVIDER=ollama
OLLAMA_MODEL=qwen2.5:7b-instructFor hosted-provider usage:
HELPDESK_AI_PROVIDER=openai
OPENAI_API_KEY=your_key_here
OPENAI_MODEL=your_model_hereThe system is designed to remain usable with fallback behavior when provider output is unavailable or unsuitable.
Planned or possible next steps include:
- semantic similar-ticket matching using embeddings and vector search
- richer AI trend visualizations on the dashboard
- stronger auth and role enforcement
- larger internal knowledge base for grounding
- attachment support
- SLA tracking
- notification workflows
- evaluation views for comparing providers and acceptance outcomes over time
This project demonstrates more than the ability to build a CRUD interface.
It shows the ability to:
- design a full workflow product
- model a realistic support domain
- integrate AI responsibly into operations
- expose human control over AI recommendations
- capture feedback as structured product data
- measure AI behavior with operational analytics
- present the result as a coherent system rather than a collection of disconnected pages
AI Helpdesk Copilot was built to show what happens when a standard workflow product is extended with AI in a disciplined way.
Instead of treating AI as decoration, this project treats it as a governed operational layer:
- useful to agents
- visible to reviewers
- grounded in workflow
- resilient under fallback
- measurable over time
That combination is what makes the project feel like a real product and not just a demo.
Built by Mountadem Badr as a portfolio project focused on workflow product design, AI-assisted operations, and measurable human-in-the-loop support tooling.