Build self-service that understands intent—and knows when to hand off.
DCX designs voice and digital virtual-agent experiences that combine conversational design, NLU, knowledge, integrations, analytics, and clean escalation into live service.
What we build
Focused AI capabilities designed to fit into the same contact center, integration, analytics, and operating model your teams already use.
Voice bots & IVR automation
Natural-language self-service, smarter prompts, intent recognition, and clean transfer paths.
Chatbots & messaging
Digital self-service for web chat, SMS, and messaging with consistent handoff and routing.
Knowledge-driven answers
Connect curated knowledge to bot experiences so customers receive consistent, supportable responses.
Data lookups & transactions
Use APIs, Data Actions, and business systems to retrieve information and complete supported self-service tasks.
Bot analytics & milestones
Measure containment, escalation, no-match, no-input, success, failure, and other journey events.
Fallbacks & human handoff
Design safe failure behavior and clear escalation conditions so customers do not get trapped in automation.
High-impact conversational AI use cases
We focus on customer intents that are repeatable, measurable, and appropriate for automation—then design a reliable path to live service when self-service should stop.
Routing & triage
Natural-language routing, intent-based queue selection, and data collection before live handoff.
Status & information
Handle common informational requests with curated knowledge and connected business data.
Account & subscription lookups
Retrieve supported account, order, subscription, or service information through integrations.
Scheduling & guided tasks
Collect required information and support guided scheduling or service workflows where APIs are available.
Escalation to agents
Move customers to the right queue or team with context when the bot cannot or should not continue.
Intent-level optimization
Use real interaction data to identify misses, fallbacks, failure points, and expansion opportunities.
Built for production—not demos
The bot experience is only one layer. We design the surrounding integrations, failure handling, analytics, and support model needed for production use.
- Conversation design— intents, prompts, fallbacks, confirmations, and escalation.
- Knowledge & retrieval— curated sources, content readiness, and supportable answer patterns.
- Integrations— CRM, customer data, account systems, APIs, and Data Actions.
- Safety & controls— retry limits, handoff rules, approved behaviors, and data-handling expectations.
- Observability— milestones, containment, no-match/no-input, failures, and tuning cadence.
Our delivery process
Start with a focused set of intents, establish reliable measurement, and expand based on real production evidence.
Discovery & use-case selection
Identify high-value intents, customer journeys, data dependencies, and success measures.
Conversation design & NLU
Design intent structure, utterances, prompts, fallbacks, confirmations, and escalation paths.
Build, integrate, and test
Implement the bot, connect required systems, validate reporting, and test real-world edge cases.
Launch & optimize
Monitor behavior, tune intents and knowledge, review failures, and expand self-service over time.
FAQs
Quick answers to common questions about this AI capability.
Can we start with only a few intents?
Yes. A focused first release is usually the most supportable way to establish measurement and learn from real interaction data before expanding.
Can the bot pull information from our systems?
Yes, where APIs or supported integration methods exist. DCX can design lookups and transactions using platform integrations, Data Actions, middleware, or web services.
How do we know whether the bot is actually working?
We define success metrics up front and instrument the experience for containment, escalation, no-match, no-input, failures, and intent-level outcomes.
What happens when the bot cannot complete the request?
The design should include explicit fallback and human-handoff conditions so the customer has a clear path forward.
Ready to build conversational AI that works in production?
Tell us which intents you want to automate, what systems need to connect, and where customers get stuck today. We’ll help define a practical first release and the path to expand it.