AI support operations workspace for high-pressure ticket handling.
SupportOps AI helps support teams prioritize risky tickets, improve response consistency, and reduce handling friction across operational support workflows.
- Live Demo: https://supportops-ai-phi.vercel.app
- GitHub: https://github.com/iveteamorim/supportops-ai
Support teams often lose time and consistency when urgent tickets, SLA pressure, policy references, and repetitive replies compete for attention at the same time.
SupportOps AI introduces an operational support layer that helps teams:
- Prioritize high-risk tickets
- Surface urgency through explicit signals
- Generate structured reply suggestions
- Keep policy context visible during ticket handling
Support teams handling operational ticket queues where speed and consistency matter, such as:
- Billing and plan-change support
- Access and account issues
- Subscription-related customer operations
-
Ticket ingestion
Tickets enter the queue with customer context, wait time, and SLA-related metadata. -
Priority evaluation
The support engine classifies urgency, queue pressure, and likely risk. -
AI assistance
The workspace surfaces a summary, suggested reply, and relevant knowledge references. -
Operational actions
Agents can assign, escalate, close, or respond with structured support context. -
Feedback loop
Suggestion quality can be reviewed to support future iteration of the support logic.
- Frontend / Backend: Next.js (App Router + Route Handlers)
- Language: TypeScript
- Data Layer: Local support dataset
- AI Layer: Deterministic mock support engine
- Deployment: Vercel
This system is designed as an AI-assisted support operations workspace, not as a full helpdesk platform.
-
Deterministic mock AI layer for the current stage
The current AI behavior is simulated so the product remains reproducible, inspectable, and demo-friendly without relying on paid inference at runtime. -
Operational workflow before automation depth
The product prioritizes queue handling, ticket context, and agent action flow before deeper automation or multi-system execution. -
State-driven support UI
Ticket handling actions are modeled explicitly so agents can move through assign, escalate, respond, and close flows predictably. -
Policy context inside the workspace
Support guidance is surfaced where the decision happens instead of being treated as separate documentation outside the ticket flow.
This system focuses on:
- Ticket prioritization
- AI-assisted support context
- Agent response support
- Operational handling of urgent cases
It does not attempt to solve:
- Full omnichannel helpdesk infrastructure
- Full CRM or account lifecycle management
- Fully autonomous support resolution
-
Deterministic mock AI vs live model dependency
The current implementation favors reproducibility and demo stability over real model variability. -
Operational support layer vs full helpdesk scope
The product goes deep on triage and action support, but does not attempt to replace full ticketing infrastructure. -
Single-workspace clarity vs broader workflow coverage
A focused workspace improves legibility, but reduces breadth compared with enterprise support suites. -
Local dataset vs production integrations
The current product demonstrates workflow quality through controlled data rather than live system connections.
This system is designed to:
- Improve prioritization in support queues
- Reduce response inconsistency across agents
- Surface risky tickets earlier
- Lower friction in repetitive support handling
Note: Impact is based on system design and expected workflow behavior, not measured production data.
This system is currently designed as a functional support-operations demo with a clear path to production hardening.
- Support dashboard
- Prioritized inbox
- Ticket workspace with AI summary and reply suggestion
- Deterministic support engine
- Agent action flow for assign, escalate, and close states
- No real external ticket ingestion
- No persistent database or tenant model
- No provider-isolated AI integration
- No audit logging for agent decisions
- Real support channel ingestion
- Persistent database-backed state
- Provider-backed AI layer
- Audit and analytics layer for operational review
As the system moves beyond demo use, several risks become important:
-
Misprioritized tickets
Weak scoring or poor urgency inference can push the wrong tickets to the top of the queue. -
Low-quality reply suggestions
AI-generated or simulated replies can sound plausible while missing policy nuance. -
Policy-reference mismatch
Surfaced guidance may not align with the actual ticket context if retrieval or mapping is weak. -
Queue-state inconsistency
Assignment, escalation, and closure flows can drift if state transitions are not enforced consistently. -
Lack of auditability
Without persistent logs, agent actions and support reasoning become difficult to review.
- Calibrate queue prioritization against real support outcomes
- Add confidence-aware handling for suggestions
- Harden policy/reference retrieval against ticket context
- Add durable state and audit logs
- Validate support actions through explicit workflow rules
SupportOps AI is not a generic chatbot for support teams.
It is an AI support operations workspace, designed to help agents make faster, more consistent decisions while keeping ticket context, urgency, and policy guidance visible in one place.
The goal is not full automation —
but better operational support handling under pressure.
src/app/dashboard– support operations overviewsrc/app/inbox– prioritized ticket queuesrc/app/ticket/[id]– ticket workspacesrc/app/api– route handlers for tickets and support logicsrc/lib/demo-data.ts– demo datasetsrc/lib/support-engine.ts– support prioritization and suggestion logic
Use Node 20:
source ~/.nvm/nvm.sh
nvm use 20Install dependencies:
npm installRun the app:
npm run devOpen:
http://localhost:3000/dashboardhttp://localhost:3000/inboxhttp://localhost:3000/ticket/t-1001
npm run devnpm run buildnpm run startnpm run lint