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Welcome to SoundHopper!
I can help you find schedules, discover fares, check terminal traffic cameras, and more.
What can I help you with today?
CONVERSATIONAL AI CASE STUDY
Coverage Coach.
Coverage Coach offers auto policy shoppers a faster way to understand what coverage they need, what they can skip, and how to buy a smarter policy online with confidence.
- Powered by Gemini 2.5 Flash, React & Typescript
- Responsive 3-panel Architecture
- Visual Storyboard Synced with Conversation State
- Dynamic Prompt Shifting
- Structured JSON Output with Schema Enforcement
- Multi-turn State Management
- Chirp 3: HD Voice from Google
CONVERSATIONAL AI CASE STUDY
Patient Intake Agent.
The Patient Intake Agent guides patients through drug testing prep and paperwork. Patients arrive ready, admin work decreases, and clinic capacity increases.
- Powered by Gemini 2.5 Flash, React & Typescript
- Structured 8-Step Intake with Embedded Conversational Q&A
- Integrated Document AI, Calendar, Maps & QR Code Generation
- Visual Storyboard Synced with Conversation State
- Templated Architecture for Multiple Screening Use Cases
- Structured JSON Output with Schema Enforcement
- Chirp 3: HD Voice from Google
services.
Poorly managed AI tends to flex your product or service prowess with all the charisma of a dry meatloaf.
Let’s kickstart your online interactions with smart, unique, and efficient approaches to AI that fuse your brand persona with sales intellect.
Create intelligent conversational AI assistants that understand context, handle complex queries, and provide natural interactions using Google Conversational Agents.
Build sophisticated conversation flows with rich content and enterprise-grade tools that scale with your ambitions.
Integrate the latest voice, imaging, video, and generative AI products from Google directly into your conversations.
Build, manage, and optimize content with AI, leveraging advanced models from Google and Anthropic to solve complex challenges and tedious tasks.
Seduce eyeballs, boost engagement, and move products out the door with gimmickless, clean, conversational copy.
Boost comprehension with crisp, conversational copy that bolsters performance and accelerates the learning process.
Move clients efficiently through complex processes with clear, concise copy.
Even better, inject personality to build trust and fortify your brand.
Make your intelligence less artificial.
Tap into a decade of UX copywriting and content design experience to power your AI conversations.
sighs matter.
dialogflow cx + Playbooks
v1. The Rich Media Experience
v1 showcases the full DialogFlow CX + Playbooks experience, including seagull-themed rich media content, voice integration, and multimodal interactions.
Aside from traditional training phrases, entities, and flows, v1 integrates Google Places API for terminal town services and features a proxy LLM API setup where Claude Sonnet 3.5 generates vessel histories and local recommendations for restaurants and attractions near ferry terminals.
Click the chat icon to explore v1!
THIS SEAGULL DARES YOU.
(Want sound? Click the speaker and refresh the agent!)
Washington State Ferries
the llm experience
v2. Pure AI Architecture
Upcoming integrations: Google Places API, local recommendations, ferry terminal wait times and traffic cameras.
Before the paint was dry on my first WSF agent build, I realized why Google was transitioning to Conversational Agents: LLMs can handle intents naturally, eliminating the often complex task of training phrase mapping. Why not let AI do the heavy lifting?
V2 runs entirely on large language models with a single orchestration Playbook handling intents. Native entity support wasn’t available yet, so I had Python manage terminal synonyms while micro prompts capture time expressions like ‘now,’ ‘ASAP,’ and ‘first available’.
This approach works well for an experimental build, with room to grow as Google’s conversational tools continue to evlove.
Feel free to interact with the live agent below. To start, try any of these four ways to ask for the exact same ferry going in the same direction. The agent should handle each one smoothly, while a Python function interjects to autocomplete your destination:
“Next Bainbridge ferry ….”
“Next ferry to Seattle …”
“When does the ferry leave Bainbridge?”
“Bainbridge departures.”
google conversational agents
v2. Pure AI Architecture
A cutting-edge agent powered entirely by large language models for intent recognition and conversational routing. Custom Python functions handle complex terminal synonyms and time parsing, while advanced prompt engineering manages the conversational flow.
- Google Conversational Agents
- Multi-Agent Orchestration
- Zero Training Phrase Architecture
- Custom API Integrations
- Advanced Custom Python Functions
- Custom RAG Implementation
- Pinecone Vector Database Integration
- Google Chirp 3: HD Voice Integration
- Dynamic Image Display
dialogflow cx + Playbooks
v1. The Rich Media Experience
The full conversational experience – rich content, voice integration, and multimodal output. Showcasing advanced DialogFlow CX & Playbooks capabilities with custom Node.js functions and Google Cloud integration.
- Multi-Agent Orchestration
- Dialogflow Playbooks
- Custom API Integrations
- Custom Python Functions
- Seagull Feeding and Noise Management
- External LLM Integrations (Claude, Gemini)
- Generative Voice Response from Vertex AI
SoundHopper
Online
LANGGRAPH CUSTOM ARCHITECTURE
v2. LangGraph Custom Architecture
Built from scratch with LangGraph’s multi-agent state machine architecture. Demonstrates advanced prompt engineering, deferred tool execution, and zero training phrase design—letting LLMs handle intent recognition, conversation routing, and intelligent information gathering.
- Multi-Agent Orchestration
- Multi-Intent Handling with Deferred Execution
- Real-Time WSDOT Ferry API Integration
- Google Maps Places API for Local Search
- Dynamic Terminal Camera & Image Display
- Prompt-Based Conversation Intelligence
- FastAPI Backend with WebSocket Streaming
- Gemini 3.0 Flash For Intent Recognition and Routing
- Zero Training Phrase Architecture
google conversational agents
v1. Google Conversational Agents
A cutting-edge Dialogflow CX-based agent powered entirely by large language models for intent recognition and conversational routing. Custom Python functions handle complex terminal synonyms and time parsing, while advanced prompt engineering manages the conversational flow.
- Google Conversational Agents
- Multi-Agent Orchestration
- Zero Training Phrase Architecture
- Custom API Integrations
- Advanced Custom Python Functions
- Custom RAG Implementation
- Pinecone Vector Database Integration
- Google Chirp 3: HD Voice Integration
- Dynamic Image Display
the llm experience
v2. LangGraph Architecture
Structure agents to deliver a single comprehensive response when a user makes multiple requests
Before the paint was even dry on my Dialogflow CX-based WSF agents, I began exploring new challenges: Most chatbots work great when a user signals one intent, but natural requests often contain two different asks at once.
How could I build an agent that identifies both requests and delivers one complete answer?
LangGraph’s custom state machine architecture made this possible.
Unlike platform-based agents that route to immediate responses, LangGraph lets you design exactly how the agent collects information, when it calls tools, and how it assembles the final response. The agent tracks all required information on a whiteboard, waits until it has everything it needs, then calls multiple tools simultaneously to formulate one complete, natural response.
You can try it below. Ask the agent something like:
“I’d like to take my wife to dinner tomorrow night on Orcas. Can you please get me a schedule and recommend a few restaurants?”
The agent will collect the missing information it needs, then deliver everything in one cohesive package. Sound easy? It isn’t! Carefully orchestrated prompts guide the agent on how to gather, organize, and present the information, right down to the format.
You can also try blunt requests like:
“Ferry to Bainbridge tomorrow 6 pm and fares please.”
“When is the next ferry to Kingston and can you check the traffic cam for me?”
the llm experience
v2. Google Conversational Agents
Build agents that deliver one complete response when a user makes multiple requests
After spending several months learning the ins and outs of Dialogflow CX, I wanted to explore Google’s new Playbooks architecture: What if I could eliminate training phrases entirely and let an LLM handle intent recognition?
v2 (Google Conversational Agents) is that experiment. Zero training phrases. Custom Python functions manage terminal synonyms and time parsing. Advanced prompts guide the conversation.
The agent delivers rich, multimodal responses including dynamic images, voice integration, and intelligent follow-up questions all powered by LLM-based path matching and routing.
Try it below with prompts like:
“I need a ferry to Southworth”
Select a departure terminal and any time of day (like “afternoon” or “4pm”). The agent will output a curated list of departure times, dynamically display an image of your arrival terminal, and offer contextual follow-up options based on your conversation history.
Need to search for services at your destination?
The agent integrates Google Places API to help you find restaurants, gas stations, coffee shops, and more:
You can try:
“Are there any coffee shops near the Anacortes terminal?”