Airbnb Ticket System Bot

A production-ready Android automation that turns guest messages, issues, and housekeeping requests into actionable support tickets—end-to-end. This Airbnb Ticket System Bot auto-triages inbox conversations, assigns priority, routes to staff, and closes the loop with status updates, helping hosts scale without drowning in chats. Built for reliability on both real devices and emulators, it delivers consistent response times and cleaner operations.

Created by Appilot, built to showcase our approach to Automation!
If you are looking for custom Airbnb Ticket System Bot, you've just found your team — Let’s Chat.👆👆

Introduction

This automation monitors Airbnb inboxes and reservation events, converts messages or incidents into structured tickets, and orchestrates resolution across cleaning, maintenance, and support teams. It eliminates repetitive triage work like categorizing issues, asking standard follow-ups, and updating guests with timelines—freeing hosts to focus on service quality.

Automating Airbnb Case Intake & Resolution

• Routes guest issues (check-in problems, amenities, refunds) to the right queue with SLA-aware priorities.
• Generates ticket IDs, attaches conversation context/screenshots, and syncs status back to the guest thread.
• Works across real Android devices or emulators, with proxy and multi-account support for agencies.
• Adds human-like timings and error-aware retries to stay undetected and resilient.
• Exposes webhooks/JSON exports for Helpdesk/Slack/Jira/Zendesk pipelines.

Core Features

• Real Devices and Emulators: Run on physical Android phones or emulators (Bluestacks/Nox). Supports screen-resolution adaptation, stable UI selectors, and device farm execution.
• No-ADB Wireless Automation: Control devices without tethered ADB using Accessibility + overlay drivers; ideal for racks and remote ops.
• Mimicking Human Behavior: Randomized delays, gesture curves, scroll variance, and typing jitter reduce detection risk and rate limits.
• Multiple Accounts Support: Manage many host profiles/properties; isolate cookies/sessions, rotate proxies, and segment device identities.
• Multi-Device Integration: Horizontal scaling with a queue; shard tickets by property/region and assign workers automatically.
• Exponential Growth for Your Account: Faster replies and consistent SLAs improve response metrics, ranking, and guest satisfaction—driving more bookings.
• Premium Support: Priority issue resolution, onboarding, runbooks, and playbooks for complex workflows.
• Smart Triage & Categorization: NLP rules match messages to categories (Check-in, Cleaning, Maintenance, Payments, Policy) and assign SLAs.
• SLA & Escalation Engine: Time-boxed rules escalate to supervisors or alternate channels when thresholds are breached.
• Template & Macro System: One-click responses with variables (guest name, code, ETA, ticket link) reduce manual typing.
• Attachment Handling: Auto-collects screenshots/photos, logs them to the ticket, and forwards to vendors when needed.
• Audit Trail & Logging: Structured JSON logs, per-ticket event history, and exportable reports for QA/compliance.
• Webhook/Helpdesk Bridge: Optional integration layer to mirror tickets into Jira/Zendesk/Linear or Slack threads.
• Analytics & KPIs: Track First Response Time, Resolution Time, reopened rates, and agent utilization.

Feature	Description
Role-Based Queues	Separate queues for Cleaning, Maintenance, Host Support, Finance; auto-routing via rules.
Retry & Backoff Logic	Network/UI failures trigger step-aware retries with exponential backoff and fallbacks.
Proxy & Geo Profiles	Per-account proxy rotation and locale profiles to mimic expected geos and stabilize sessions.
Scheduler & Throttling	Quiet hours, burst control, and rate limiting to respect platform limits and human cadence.
Incident Templates	Predefined flows for lockouts, Wi-Fi issues, missing amenities, broken appliances.
CSV/JSON Export	Nightly exports of tickets, SLAs, and outcomes to your data warehouse or BI layer.

How It Works

1. Input or Trigger — From the Appilot dashboard, select connected Airbnb accounts and enable watchers (Inbox, Reservations, Reviews). Choose triage rules, response templates, and escalation targets.
2. Core Logic — The bot controls Android devices/emulators via UI Automator or ADB-less Accessibility flows, reading threads, extracting context (guest, reservation, property), and creating/updating tickets with categories and priority.
3. Output or Action — It posts confirmation messages to guests, assigns tasks to staff queues, and updates status (ETA, scheduled visit, resolved). Optional mirroring to Slack/Jira/Zendesk keeps teams aligned.
4. Other functionalities — Robust retry logic, screenshotting, structured logs, crash recovery, and parallel execution can be tuned in Appilot to match your throughput and risk profile.

Tech Stack

• Language: Kotlin, Java, Python, JavaScript
• Frameworks: Appium, UI Automator, Espresso, Robot Framework, Cucumber
• Tools: Appilot, Android Debug Bridge (ADB), Appium Inspector, Bluestacks, Nox Player, Scrcpy, Firebase Test Lab, MonkeyRunner, Accessibility
• Infrastructure: Dockerized device farms, Cloud-based emulators, Proxy networks, Parallel Device Execution, Task Queues, Real device farm

Directory Structure

  │
  ├── src/
  │   ├── main.py
  │   ├── automation/
  │   │   ├── watchers/
  │   │   │   ├── inbox_watcher.py
  │   │   │   ├── reservation_watcher.py
  │   │   │   └── sla_scheduler.py
  │   │   ├── actions/
  │   │   │   ├── create_ticket.py
  │   │   │   ├── reply_template.py
  │   │   │   └── escalate.py
  │   │   ├── device/
  │   │   │   ├── ui_automator_driver.py
  │   │   │   ├── accessibility_driver.py
  │   │   │   └── emulator_manager.py
  │   │   └── utils/
  │   │       ├── logger.py
  │   │       ├── proxy_manager.py
  │   │       ├── kpi_metrics.py
  │   │       └── config_loader.py
  │   ├── integrations/
  │   │   ├── slack_webhook.py
  │   │   ├── jira_bridge.py
  │   │   └── exporter.py
  │   └── nlp/
  │       ├── classifier_rules.yaml
  │       └── triage_pipeline.py
  │
  ├── config/
  │   ├── settings.yaml
  │   ├── credentials.env
  │   └── routing_rules.yaml
  │
  ├── scripts/
  │   ├── run_device_farm.sh
  │   └── seed_templates.py
  │
  ├── media/
  │   └── airbnb-ticket-system-bot-banner.png
  │
  ├── logs/
  │   └── runtime.log
  │
  ├── output/
  │   ├── tickets.json
  │   └── kpi_report.csv
  │
  ├── requirements.txt
  └── README.md

Use Cases

• Property managers use it to auto-convert guest messages into tickets, so they can guarantee SLAs and reduce manual triage.
• Ops teams use it to route cleaning/maintenance tasks with ETAs, so they can cut resolution time and miscommunication.
• Hosting agencies use it to manage dozens of accounts at scale, so they can centralize reporting and keep rankings high.
• Solo hosts use it to cover off-hours replies automatically, so they can sleep while the bot handles common issues.

FAQs

How do I configure this for multiple Airbnb accounts?
Add each account in the Appilot dashboard, assign a proxy/geo profile, and map properties to queues. The bot isolates sessions and shards tickets by property or region.

Does it support proxy rotation or anti-detection?
Yes. Per-account proxies with rotation windows, realistic gesture/timing profiles, and selector randomization minimize detection and stabilize long sessions.

Can I schedule it to run periodically or only on triggers?
Both. Enable watchers for real-time triggers and configure the scheduler for periodic sweeps (e.g., every 5 minutes) with throttling and quiet hours.

What if a message doesn’t match a category?
It’s routed to the Manual Review queue with full context and screenshots. You can add a rule and reprocess instantly.

Can it mirror tickets to external helpdesks?
Optional bridges push tickets and updates to Slack/Jira/Zendesk/Linear with deep links back to the originating Airbnb thread.

Performance & Reliability Benchmarks

• Execution Speed: Typical end-to-end ticket creation (read → classify → create → reply) in 2.5–5.0 seconds per thread on modern emulators; bulk mode sustains 450–700 tickets/hour per device lane.
• Success Rate: Steady-state action success of 95% with selector fallbacks, UI state checks, and auto-retries.
• Scalability: Proven parallelization to 300–1,000 Android instances via device farms and queue sharding; linear throughput with additional lanes.
• Resource Efficiency: Single lane operates within 1 vCPU / 1.5–2.0 GB RAM on cloud emulators; headless rendering reduces spikes.
• Error Handling: Exponential backoff, per-step retries, dead-letter queues, crash recovery, screenshot-on-failure, and Slack alerts for escalations.