Name: My TravelPlanner Tagline: AI-powered travel planning platform — plan personalized trips with intelligent recommendations Current status: Active development — WordPress plugin + iOS/macOS native app in parallel (Build 23, March 2026) First commit / project start: March 2026 (initial monorepo commit 3a82f49a on March 10, 2026)
My TravelPlanner is a multi-platform AI-powered travel planning product that helps leisure travelers plan personalized trips through conversational AI. It combines a WordPress plugin with an AI chatbot, a native iOS/macOS companion app built in SwiftUI, and a Chrome browser extension for saving places from travel sites. The system uses 17 specialist AI advisor agents for travel, scuba, expat, RV, culinary, hotel, weather, airport, transit, rental, navigation, remote work, retirement, civil unrest, and FIFA 2026 World Cup planning. The platform is backed by a 193-file knowledgebase covering destinations, activities, and specialized travel domains, with n8n workflow orchestration and Dify-managed vector retrieval via pgvector.
Platform: WordPress plugin (web) + native iOS/macOS app (SwiftUI) + Chrome browser extension (MV3) Deployment: WordPress self-hosted, iOS App Store, Mac App Store (planned) Primary interface: Conversational chatbot (web widget + native app chat), Kanban itinerary boards, route planning maps
Primary user: Leisure travelers seeking intelligent trip planning assistance Secondary users: Expats/relocators, RV travelers, scuba divers, digital nomads, FIFA 2026 attendees, retirees considering relocation User environment: Web browsers (WordPress site), iOS devices, macOS desktop
Trip planning involves coordinating multiple complex, interrelated decisions (destinations, lodging, dining, activities, budgets, weather, safety) across fragmented information sources. Travelers currently piece together plans from dozens of websites, review platforms, and travel guides with no unified intelligence layer. My TravelPlanner consolidates this into a single AI-powered advisor that understands the user’s preferences, constraints, and context.
| Need | How the product addresses it | Source of evidence |
|---|---|---|
| Personalized multi-domain travel advice | 17 specialist agents for different travel domains | CLAUDE.md agent inventory |
| Real-time information alongside curated knowledge | Tavily web search + 193-file KB + Pinecone vector search | SystemPromptBuilder.swift, N8N-DIFY-GUIDE.md |
| Cross-device trip access | CloudKit sync between iOS/macOS + WordPress web access | .claude/CLAUDE.md feature list |
| Event-specific planning | Dedicated FIFA 2026 advisor with 25-file city-by-city knowledgebase | knowledgebase/fifa-2026/ |
| Save-from-anywhere workflow | Browser extension captures places from Google Maps, TripAdvisor, Yelp, Booking, Airbnb | browser-extension/manifest.json |
Using multiple separate tools — Google Maps, TripAdvisor, Booking.com, spreadsheets, travel blogs — with no AI-powered synthesis. Planning required manual research across siloed platforms.
Primary category: AI-powered travel planning platforms Market maturity: Emerging — many AI travel tools exist but few offer multi-agent specialist depth [CLAUDE NOTE: inferred] Key dynamics: Rapid entry of LLM-powered travel assistants; differentiation through domain depth and multi-platform delivery [CLAUDE NOTE: inferred]
| Product / Company | Approach | Strengths | Key gap this project addresses | Source |
|---|---|---|---|---|
| ⚡ Google Travel | Aggregation + search | Scale, maps integration | No conversational AI advisor, no specialist agents | General knowledge |
| ⚡ Tripit | Itinerary organization | Email parsing, calendar sync | No AI planning, no recommendations | General knowledge |
| ⚡ Wonderplan / Roam Around | AI trip generation | Quick itinerary creation | Shallow single-model responses, no domain specialization | General knowledge |
Multi-agent specialist travel advisor with cross-platform delivery (web + native) and deep domain knowledge (scuba, expat, RV, FIFA 2026). Positioned as a premium planning tool rather than a quick-itinerary generator.
Defensibility comes from the 193-file knowledgebase, 17 specialist agent configurations with tuned prompts, the multi-platform architecture (WordPress + SwiftUI), and the n8n/Dify integration layer. The budget-controlled prompt assembly system (P0–P6 priority tiers) is a reusable architectural pattern. [CLAUDE NOTE: inferred from code architecture]
Extensive requirements documentation exists across documentation/ (detailed requirements at ~2700+ lines, UI design spec, test plan) and requirements/ (17 files covering feature parity, competitor analysis, data sources, agent/skill specifications). Requirements evolved through iterative AI-assisted development sessions.
Hard constraints: WordPress 6.0+ / PHP 8.0+ (plugin); iOS 17.0+ / macOS 14.0+ (native); Swift 5.9; Mapbox Maps 11.20.0 dependency Explicit non-goals: NOT FOUND — add manually
| Decision | Alternatives considered | Rationale | Evidence source |
|---|---|---|---|
| Multi-agent architecture (17 agents) | Single general-purpose chatbot | Domain specialization yields higher quality advice per topic | CLAUDE.md agent inventory |
| n8n-first with local fallback | Direct API calls only | Centralized orchestration, easier workflow updates, graceful degradation | N8N-DIFY-GUIDE.md |
| P0–P6 budget-controlled prompts | Fixed prompt templates | Dynamic assembly maximizes context within token limits | SystemPromptBuilder.swift |
| XcodeGen for project management | Manual Xcode project | Reproducible builds, clean diffs, declarative config | project.yml |
| Mapbox Maps over Apple Maps | Apple Maps, Google Maps | Richer customization, cross-platform potential | project.yml dependency |
KB type: Curated markdown files + Dify-managed vector database (pgvector) + Pinecone embeddings + Tavily web search Location in repo: knowledgebase/ (193 markdown files), loaded as Xcode resources via project.yml Estimated size: 193 markdown content files across 10+ categories; 26-file FIFA 2026 sub-corpus (9 top-level topic files + 11 US city + 3 Mexico city + 2 Canada city + 1 tavily-domains.md); Dify indexes 183+ files
knowledgebase/
├── fifa-2026/ # FIFA 2026 World Cup (26 files)
│ ├── overview.md, schedule.md, venues.md, teams.md
│ ├── travel-logistics.md, tickets-budget.md, transportation.md
│ ├── food-entertainment.md, safety-health.md, tavily-domains.md
│ ├── us-cities/ # 11 US host city guides
│ ├── mexico-cities/ # 3 Mexico host city guides
│ └── canada-cities/ # 2 Canada host city guides
├── countries/ # Country and city guides
├── travel/ # General travel topics
├── scuba/ # Scuba diving (shared with Scuba GPT)
├── expat/ # Expat/relocation guides
├── rv-camping/ # RV and camping (incl. international)
├── airports/ # Airport guides
├── remote-work/ # Digital nomad content
├── accommodation/ # Lodging guides
├── destinations/ # Regional destination guides
├── international/ # International travel topics
├── roadtripping/ # Road trip guides
├── travel-guides/ # Compiled travel guides
└── seasonal-planner.md # Seasonal planning reference
| Category | Files / format | Purpose | Update frequency |
|---|---|---|---|
| FIFA 2026 | 26 .md files |
World Cup host city guides, logistics, scheduling, tavily domain filters | Active updates for 2026 event |
| Countries & cities | Multiple .md |
Destination profiles, cultural info, travel tips | Periodic |
| Scuba diving | Regional .md guides |
Dive site info, seasonal conditions | Shared with Scuba GPT product |
| Expat/relocation | .md guides |
Relocation logistics, visa info, cost of living | Periodic |
| RV/camping | .md guides |
Route planning, campground info, international RV | Periodic |
| Airports | .md guides |
Terminal info, transit connections, layover tips | Periodic |
| Remote work | .md guides |
Digital nomad destinations, coworking, visa programs | Periodic |
| Accommodation | .md guides |
Lodging types, booking strategies | Periodic |
Curated markdown files authored manually and through AI-assisted research sessions. FIFA 2026 corpus built as a dedicated sub-collection with city-by-city guides for all 16 host cities across US, Mexico, and Canada. Knowledge is loaded at runtime through keyword-based matching (P3 tier) and Dify semantic retrieval via pgvector embeddings.
System prompt approach: P0–P6 priority-based budget assembly in SystemPromptBuilder.swift (548 lines):
Key behavioural guardrails:
[[place:Name, City]][[itinerary:{JSON}]]Persona / tone configuration: Rich per-agent personas configured in Swift and PHP agent classes Tool use / function calling: NOT FOUND in current prompts — agents use RAG context injection rather than tool calling
AI-assisted parallel development: WordPress plugin and iOS/macOS app developed simultaneously with shared knowledgebase. Claude Code / Cursor used for code generation, architecture decisions, and documentation. n8n/Dify infrastructure added as an orchestration layer in March 2026.
| Phase | Approximate timeframe | What was built | Key commits or milestones |
|---|---|---|---|
| Initial commit | Mar 10, 2026 | Coding playbook, Claude Platform Skills, portfolio | 3a82f49a |
| Pre-migration snapshot | Mar 27, 2026 | Full workspace captured before n8n+Dify migration | e65fdaf2 |
| n8n + Dify infrastructure | Mar 27, 2026 | Docker infrastructure (Phase 1) | fc3cecce |
| Agent/skill expansion | Mar 28, 2026 | Expanded agent roster, skills library, tests | f8505ec4 |
| RV + UI + n8n integration | Mar 31, 2026 | RV lifestyle features, UI design system, n8n integration | 44dff0a8 |
| Cross-product updates | Mar 31, 2026 | n8n workflow client and agent updates across products | 82aee6a6 |
| Smoke testing | Apr 1, 2026 | Production smoke test, defects D001–D005 documented | tests/smoke-test-plan.md |
| Challenge | How resolved | Evidence |
|---|---|---|
| Token budget management across 193 KB files | P0–P6 priority-based budget assembly with per-tier character caps | SystemPromptBuilder.swift |
| Platform parity (PHP ↔ Swift) for 17 agents | Shared agent IDs and knowledgebase; parallel implementations with same prompt structure | CLAUDE.md agent inventory, requirements/feature-parity-requirements.md |
| n8n-first with graceful fallback | ITI_Workflow_Adapter (PHP) and N8nWorkflowClient (Swift) try webhook first, fall back to direct Claude |
N8N-DIFY-GUIDE.md |
Duplicate mtpConfig JS variable |
Identified as defect D001 in smoke testing | tests/smoke-test-plan.md |
| REST validation errors | Defect D002 — endpoint parameter validation fixes needed | tests/smoke-test-plan.md |
| Model / API | Provider | Role in product | Integration method |
|---|---|---|---|
Claude Sonnet 4 (claude-sonnet-4-6) |
Anthropic | Primary advisor model for all 17 agents (WordPress) | ITI Shared Library ITI_Claude_API |
Claude Haiku 4.5 (claude-haiku-4-5-20251001) |
Anthropic | Lightweight tasks (packing list, title generation) | Direct REST in class-mtp-rest.php |
| Claude (via n8n) | Anthropic | Orchestrated agent workflows | n8n webhook POST /webhook/travelplanner |
| Tavily Search API | Tavily | Real-time web search for travel information | ITI Shared Library ITI_Tavily_API |
| Pinecone | Pinecone | Vector/semantic search over knowledgebase | ITI Shared Library ITI_Pinecone_API |
| text-embedding-3-small | OpenAI | Embedding model for Pinecone + Dify indexing | Dify High Quality indexing mode |
| Google Places API | Place search, details, autocomplete | iOS GooglePlacesService |
|
| Google Earth Engine | Scenery scoring | iOS GEEService |
|
| Mapbox Maps (11.20.0) | Mapbox | Map display and routing | iOS/macOS via project.yml |
| Tool | Category | Purpose |
|---|---|---|
| n8n | Workflow orchestration | Primary dispatch: classifier → router → specialist AI Agent nodes |
| Dify | Knowledge management | Vector retrieval over 183+ files via pgvector, semantic search top_k: 3 |
| ITI Workflow Adapter | Integration layer | PHP/Swift bridge to n8n with automatic fallback to direct Claude |
| ITI Shared Library | Component library | Reusable Claude, Tavily, Pinecone clients + agents + chat handlers |
[[place:]], [[itinerary:{}]])| Tool | How used in build |
|---|---|
| Cursor IDE | Primary development environment for PHP, Swift, and documentation |
| Claude Code | CLAUDE.md stewardship, cross-file refactoring |
| Antigravity | Autonomous test execution, browser QA, visual regression testing — used per global CLAUDE.md tool lane |
| n8n MCP SDK | Workflow creation and management |
| XcodeGen | iOS/macOS project generation from declarative YAML |
| pytest | Integration test suite (6 test suites, live API markers) |
| Version / Phase | Date | Summary of changes | Significance |
|---|---|---|---|
| Plugin v1.0.0 / Build 23 | Mar 2026 | Full WordPress plugin + iOS/macOS app with 17 agents | Current release |
| n8n integration | Mar 27, 2026 | Docker infrastructure for n8n + Dify orchestration | Phase 1 infrastructure |
| RV + UI features | Mar 31, 2026 | RV lifestyle features, design system, n8n workflow client | Feature expansion |
| Smoke test | Apr 1, 2026 | Production validation, 5 defects documented (D001–D005) | QA milestone |
| Artifact | Path | Type | What it shows |
|---|---|---|---|
| UI Design Spec | documentation/UI-DESIGN-SPEC.md |
Markdown | Full UI specification |
| UI Design Requirements | documentation/UI-DESIGN-REQUIREMENTS.md |
Markdown | Design requirements |
| UI Design Build Plan | documentation/UI-DESIGN-BUILD-PLAN.md |
Markdown | Implementation plan |
| Document | Path | Type | Status |
|---|---|---|---|
| CLAUDE.md (root) | CLAUDE.md |
Project context | Active (March 2026) |
| CLAUDE.md (product) | .claude/CLAUDE.md |
Detailed product context | Active (March 24, 2026) |
| Detailed requirements | documentation/travel-planner-requirements-detailed.md |
Spec (~2700+ lines) | Active |
| N8N-Dify Guide | documentation/N8N-DIFY-GUIDE.md |
Architecture guide (1018 lines) | Active (March 30, 2026) |
| Test plan | documentation/test-plan.md |
QA plan | Active |
| Smoke test results | tests/smoke-test-plan.md |
Test results (206 lines) | April 1, 2026 |
| Shortcode reference | documentation/shortcode-reference.html |
HTML reference | Active |
| Privacy policy | documentation/privacy-policy.html |
Legal | Active |
| Feature parity requirements | requirements/feature-parity-requirements.md |
Cross-platform spec | Active |
| Skills and agents spec | requirements/skills-and-agents.md |
Agent architecture | Active |
| Competitor analysis | requirements/competitor-analysis.md |
Market research | Active |
| Artifact | Path | Description |
|---|---|---|
| Knowledgebase (193 files) | knowledgebase/ |
Full travel knowledge corpus |
| FIFA 2026 corpus (26 files) | knowledgebase/fifa-2026/ |
World Cup host city guides + tavily domain filter reference |
| Browser extension | browser-extension/ |
MV3 Chrome extension for saving places |
| Plugin install packages | plugin-installs/ |
WordPress plugin zip files |
| iOS/macOS build archives | build/ |
Xcode build outputs |
The product evolved from personal research into travel, expatriation, and RV living (documented in the sibling staging workspace at /Users/peterwesterman/Cursor/ITI/products/my-travelplanner/, now mostly empty as content migrated to this workspace). Initial research sessions using Cursor produced decision matrices and curated reference materials that revealed the potential for an AI-powered travel advisor. [CLAUDE NOTE: inferred from staging workspace content]
Competitor analysis exists in requirements/competitor-analysis.md. First-person travel research informed the product’s multi-domain approach. The decision to support multiple travel personas (expat, RV, scuba, culinary, etc.) came from recognizing that no existing tool covered the full breadth of travel planning decisions. [CLAUDE NOTE: partially inferred]
That a multi-agent AI system with deep domain knowledge (193 curated files) can provide meaningfully better travel advice than generic AI chatbots or traditional travel planning tools, and that this value is worth delivering across web, mobile, and desktop platforms simultaneously.
Personal lifestyle research (Jan 2026) → structured decision frameworks → concept for AI travel advisor → WordPress chatbot plugin → multi-agent architecture with 17 specialists → iOS/macOS native app with SwiftUI → n8n/Dify orchestration layer → browser extension for save-from-anywhere → FIFA 2026 dedicated agent and corpus.
What works well: Multi-agent system with 17 specialists; 193-file knowledgebase; n8n/Dify orchestration; iOS/macOS + WordPress parity; budget-controlled prompt assembly Current limitations: 5 defects from smoke testing (D001–D005); Cesium flyover not complete; some API keys not configured; AI Engine integration disabled Estimated completeness: 70% [CLAUDE NOTE: inferred from active tasks list and defect count]
_Manual input required — this section cannot be populated automatically._
| File / Path | What it contributed |
|---|---|
CLAUDE.md (root) |
Product overview, agent inventory, KB structure, development status, n8n/Dify integration ref |
.claude/CLAUDE.md |
Detailed platform specs, features, architecture, file locations, build commands |
my-travelplanner/my-travelplanner.php |
Plugin header (v1.0.0), bootstrap logic, 17 agent class loads, DB tables |
my-travelplanner/composer.json |
Package metadata, dev dependency (wordpress-stubs 6.7) |
MyTravelPlanner/project.yml |
iOS/macOS build config: Swift 5.9, Mapbox 11.20.0, iOS 17.0, macOS 14.0, Build 23 |
documentation/N8N-DIFY-GUIDE.md |
n8n/Dify architecture (1018 lines), webhook config, Dify retrieval params |
requirements/skills-and-agents.md |
Model preferences (Opus 4.6 vs Sonnet 4.6), orchestration expectations, bug/feature backlog |
MyTravelPlanner/MyTravelPlanner/Services/SystemPromptBuilder.swift |
P0–P6 budget assembly (548 lines), place hotlink syntax, agent personas |
.claude/settings.json |
Permission baselines (bash, web fetch domains) |
tests/pytest.ini |
Test suite structure (6 suites), markers |
tests/requirements.txt |
Python test dependencies (pytest, httpx, requests) |
browser-extension/manifest.json |
“Save Places” extension v1.0.0, content scripts for travel sites |
tests/smoke-test-plan.md |
Smoke test results (Apr 1, 2026), defects D001–D005 |
| Git log (13 commits) | Full commit history with dates and scopes |
The AI-powered travel planning space has undergone a structural shift since My TravelPlanner’s initial build in March 2026. The vibe coding ecosystem — now a $9.4 billion funded category with 41% of global code AI-generated — has lowered barriers to entry for travel app competitors. Tools like Bolt.new and Lovable mean a motivated non-developer can stand up a basic AI trip planner in days. This is the environment My TravelPlanner now competes in: not a scarcity of AI travel tools, but a surplus.
More consequentially, Model Context Protocol (MCP) has emerged as a new distribution channel for travel services. Expedia, Booking.com, and airlines have published MCP servers. Startups like aitrips.io and Lambus now let travelers plan trips inside Claude, ChatGPT, or Gemini — never visiting a dedicated travel app at all. This is a fundamental channel shift: the “app as destination” model is being supplemented (and in some cases replaced) by “agent as storefront.” MTP must decide whether to fight this pattern or adopt it.
Meanwhile, frontier LLM convergence means “AI-powered” is no longer a differentiator. Claude 4, GPT-5, and Gemini 2.5 score within 10% of each other on reasoning benchmarks. Every travel app can integrate the same models at similar cost. The differentiator is no longer the model — it’s the domain expertise, orchestration architecture, and curated knowledge embedded in the product.
| Competitor | Type | Key Threat |
|---|---|---|
| Travo | AI-first mobile trip planner | Free AI itinerary generation in seconds; native iOS/Android with free offline access |
| Layla.ai | AI trip planner (web) | Complete itineraries with flights, hotels, activities, dining; $49.99/yr |
| aitrips.io | MCP-native trip persistence | Plans trips inside any AI assistant with persistent storage |
| Lambus | AI Connector multi-LLM planner | Works with ChatGPT, Claude, Gemini via MCP |
| Tripomatic 26 | Full-featured planner refresh | AI assistant, 50M places, offline maps, multi-modal routing |
| Expedia MCP Server | Infrastructure/distribution | Open-source MCP server for hotels, flights, activities, car rentals |
| Feature | Who Added It | Impact on MTP |
|---|---|---|
| MCP server integration | aitrips.io, Lambus, Expedia | New distribution channel MTP doesn’t participate in |
| Free AI itinerary generation | Travo (free), Layla ($49/yr) | Raises baseline expectation for AI trip planning |
| Calendar event import | Tripsy 3.8.0 | Table-stakes feature MTP lacks |
| Multi-modal routing (bike, taxi, scooter, transit) | Tripomatic 26 | Extends beyond driving/walking that MTP supports |
| Offline maps with auto-expiry | Tripomatic 26 | Smart offline approach MTP can learn from |
| Differentiator | Status |
|---|---|
| “AI-powered conversational trip planning” | Eroded — Travo, Layla, Wanderlog, Tripomatic, and raw LLMs all offer this |
| “Cross-platform delivery” | Eroded — Tripomatic 26 now has iOS + Android + web with sync |
| 17 specialist AI agents with domain routing | Still unique — no competitor offers scuba, expat, RV, culinary specialist routing |
| 193-file curated RAG knowledgebase | Still unique — competitors use generic LLM knowledge or partner APIs |
| Multiple itineraries per trip | Still unique |
| Kanban board for trip planning | Still unique |
| Google Earth Engine scenery scoring | Still unique |
| FIFA 2026 dedicated advisor + 26-file corpus | Time-limited — value peaks June-July 2026 |
The roadmap is organized into four tiers reflecting urgency and strategic value.
Tier 1 — Critical (4-6 weeks): Close quality gaps and establish a market-ready baseline. Fix the 5 smoke-test defects (D001-D005). Connect the disconnected TodayItineraryWidget. Ship table-stakes features users now expect: public shareable trip links, PDF export, iCal export, and offline data access. Most importantly, build full AI itinerary generation from a natural language prompt — orchestrating across the 17 specialist agents to produce itineraries richer than any single-model competitor can generate.
Tier 2 — High Value (6-8 weeks): Capture the MCP distribution opportunity by publishing MTP’s 17 specialist agents as an MCP server — enabling Claude, ChatGPT, and Gemini users to access scuba, RV, expat, and culinary travel advisors from inside their preferred AI tool. No travel app currently exposes specialist AI agents via MCP. Also: route optimization (TSP solver), collaborative editing, weather in itinerary, and calendar event import.
Tier 3 — Strategic (8-12 weeks): Deepen the moat with email booking auto-parse, expat scouting trip dashboard, hotel price comparison, flight search integration, community destination guides, and major knowledgebase expansion (Asia, Middle East, Oceania — ~40-50 new files).
Tier 4 — Exploratory: Apple Watch companion, full web app, Android app, adaptive real-time itinerary adjustments, and multi-language support. Deferred until core experience is polished.
Deprioritized: Cesium flyover view (low user-facing value vs. effort), AI Engine integration (unresolved API incompatibility — direct Claude API + n8n is the strategic path), and full WordPress-iOS feature parity (iOS/macOS is the primary platform).
The following Skills have been added to the ITI skill library since the showcase was written, directly supporting roadmap execution:
| Skill | Relevance to My TravelPlanner |
|---|---|
mcp-server-development |
Core infrastructure for T2-01: publishing 17 specialist agents as an MCP server |
mapbox-offline-packs |
Offline tile download management for T1-07 offline data access |
icalendar-ics-generation |
Standards-compliant .ics feed generation for T1-06 calendar export |
tsp-route-optimization |
TSP solver and Mapbox Optimization API integration for T2-02 route optimization |
email-parsing-travel-bookings |
Parsing airline/hotel/restaurant confirmations for T3-01 email auto-parse |
scouting-trip-planning |
Structured evaluation trip methodology for T3-02 expat scouting dashboard |
fifa-2026-travel |
FIFA 2026 World Cup travel advisory for the dedicated FIFA agent’s time-limited window |
Strengths: The 17-specialist multi-agent architecture with 193-file curated RAG remains genuinely unique in the travel planning market. No competitor orchestrates across domain-specific advisors (scuba, expat, RV, culinary) — they all use single generic LLMs. The n8n/Dify orchestration layer provides a flexible integration backbone. The FIFA 2026 corpus is timely and irreplaceable for the tournament window.
Gaps: Five smoke-test defects remain open. Table-stakes features (shareable links, offline access, PDF export, calendar sync) that every competitor offers are missing. The TodayItineraryWidget is disconnected. Several data source adapters remain stubs. Estimated completeness is 65-70%.
What we’re watching: The MCP distribution shift is the most consequential change. If travelers routinely plan trips inside Claude or ChatGPT using MCP-connected travel services, apps that don’t participate in this channel risk irrelevance. The T2-01 MCP server is the single most strategic item on the roadmap. We’re also watching Apple’s Siri overhaul (iOS 27, WWDC June 2026) — if Siri gains meaningful travel planning capability via third-party AI extensions, the iOS app’s competitive position changes.
Portfolio context: My TravelPlanner demonstrates ITI’s ability to build and orchestrate complex multi-agent AI systems across platforms (WordPress, iOS, macOS). The multi-agent routing, budget-controlled prompt assembly, and n8n workflow patterns are directly reusable in consulting engagements. The product is portfolio evidence of AI systems architecture — not a consumer play competing on marketing spend.