Dialogflow CX

CX is built for enterprise conversation design:

Visual flow builder: Design conversations as flows with states, transitions, and conditions. Teams can see and understand the structure.

Gemini integration: Leverage Gemini 3's advanced reasoning for natural language understanding and response generation within structured flows.

State machine approach: Conversations are managed as proper state machines with clear transitions. Reduces confusion about conversation state.

Page-based structure: Organise conversations into pages representing different contexts or tasks. Scales to complex applications.

Generative AI features: Use Gemini for data store agents, generative fallbacks, and natural language summarisation.

Built-in testing: Simulation and testing tools help verify behaviour before deployment.

Version control: Manage changes with environments and versions. Deploy safely with rollback capability.

CX suits organisations that:

Need predictable behaviour across multi-turn journeys. You want complex flows that remain understandable and testable.

Want Gemini capability within an auditable framework. You want LLM flexibility without losing governance and control.

Value visual tooling. You want a structure that non-developers can understand and collaborate on.

Use Google Cloud. You want native integration with the GCP ecosystem.

Need enterprise release management. You want versioning, environments, access controls, and rollback paths.

CX imposes more structure than pure LLM prompting but rewards that discipline with maintainability and reliability at scale.

Dialogflow CX now integrates generative AI throughout:

Data store agents: Connect knowledge bases and let Gemini generate responses grounded in your content.

Generative fallbacks: When structured flows do not match, Gemini provides intelligent fallback responses.

Conversation summarisation: AI-powered summaries for agent handoff and analytics.

Response rephrasing: Dynamic response generation while maintaining structured flow control.

This hybrid approach gives you LLM flexibility within a controlled framework.

We build Dialogflow CX applications for:

Customer service: Handling structured processes like booking, account management, and support triage.

Contact centres: Virtual agents that integrate with Google CCAI for voice and chat support.

Transactional conversations: Flows involving forms, validation, and system interactions where reliability matters.

Multi-language support: Serving customers across languages with consistent experiences.

Knowledge-grounded assistants: Combining structured flows with Gemini-powered responses from your content.

Effective CX development requires thinking in flows:

Page design: Breaking conversations into logical pages representing different states or tasks.

Transition logic: Defining when and how conversations move between pages.

Intent handling: Recognising user inputs and routing appropriately within flows.

Webhook integration: Connecting to external systems for data and actions.

Generative fallbacks: Using Gemini when structured responses do not apply.

Fallback management: Handling unexpected inputs without breaking the experience.

We design flows that are understandable, testable, and maintainable.

Good CX work is less about “writing clever prompts” and more about defining the states that matter, the data you need to collect, and the rules that protect users from dead ends. That is where CX’s structure pays off.

Dialogflow CX connects to:

Google Cloud services: Vertex AI, Cloud Functions, BigQuery, and other GCP components.

Telephony: Integration with voice systems through Google CCAI or partners.

Messaging channels: Deployment to web, mobile, and messaging platforms.

Business systems: Custom webhooks connecting to CRM, helpdesk, and other applications.

As a Google Cloud Partner, we have deep Dialogflow CX experience:

Solution design: Architecting CX applications that balance structure with Gemini flexibility.

Flow development: Building conversation flows with proper structure and testing.

Generative configuration: Setting up data store agents and AI-powered responses effectively.

Integration: Connecting CX to your systems through webhooks and APIs.

Operations: Supporting production deployments with monitoring and improvement.

Use these prompts to pressure-test flow structure and production readiness.

“What are the main user journeys we need to support, and what states/pages should represent each?”

“Where should we use generative fallbacks, and where do we need strict deterministic routing?”

“What are the failure modes: missing data, webhook timeouts, and escalation to humans?”