Voice Assistants

Voice technology serves different contexts:

Conversational IVR: Replace frustrating touch-tone menus with natural speech. Customers say what they need instead of pressing numbers.

Phone automation: Handle calls entirely through AI, resolving common enquiries without human involvement.

Voice-enabled products: Add voice control to physical products, applications, or kiosks.

Smart speaker skills: Branded experiences for Alexa, Google Assistant, or similar platforms.

Internal tools: Voice interfaces for hands-free data entry, lookup, or process completion.

Voice AI has characteristics distinct from text chat:

Speed: Speaking is faster than typing. Conversations move quickly.

No screen: Users cannot read options or review history. Design must account for this.

Ambient noise: Real environments are not quiet. Systems must handle background sound.

Accents and variation: People speak differently. Recognition must handle this diversity.

Emotional cues: Voice carries tone and emotion that text does not.

Effective voice design addresses these factors deliberately.

Traditional phone menus frustrate customers. They force linear navigation through options that may not fit the enquiry. Conversational IVR changes this:

Natural language: Callers say what they want in their own words.

Direct routing: AI understands the request and routes appropriately without menu trees.

Self-service: Many enquiries resolve without human involvement.

Graceful escalation: When agents are needed, context transfers with the call.

The result is faster resolution and better caller experience.

Beyond routing, AI can handle complete phone interactions:

Account status and simple requests. Checks, updates, and common account workflows.

Appointment booking and rescheduling. Scheduling flows with validation and confirmations.

Order status and delivery updates. Lookups grounded in systems of record.

Information lookup and guidance. Common questions and step-by-step support.

Feedback collection. Surveys and post-interaction capture where appropriate.

Automation handles volume while ensuring human agents are available for complex situations.

Voice AI combines several capabilities:

Speech recognition: Converting spoken words to text accurately.

Natural language understanding: Interpreting meaning and intent from transcribed speech.

Dialogue management: Handling multi-turn conversations with appropriate context.

Text-to-speech: Generating natural-sounding spoken responses.

Integration: Connecting to business systems for data and actions.

We design systems using appropriate technology for each component.

Voice projects require attention to:

Audio quality: Infrastructure that captures clear speech.

Language and accent: Training that reflects your actual caller population.

Error handling: Graceful recovery when recognition or understanding fails.

Fallback paths: Options when voice interaction is not working.

Testing: Verification with real voices in realistic conditions.

We build voice solutions appropriate to your context:

Requirements: Understanding your use case, users, and environment.

Design: Creating voice interactions that work naturally.

Development: Building with appropriate speech and AI technology.

Integration: Connecting to telephony systems and business applications.

Testing: Verifying with diverse speakers and real scenarios.

Deployment: Launching with monitoring and optimisation.

Voice succeeds when the system is designed for real audio conditions.

Design for mishearing and ambiguity. Clarification prompts, confirmations, and safe fallbacks prevent frustrating loops.

Handle accents and background noise. Testing with diverse speakers and real call conditions is essential.

Define escalation rules. Emotional calls, regulated topics, or low confidence should route to humans quickly with context.

Use these prompts to scope a voice assistant realistically.

“Which call types should be fully automated, and which should always route to an agent?”

“What information must we capture and confirm to complete the workflow safely?”

“What are the most common failure modes (noise, accents, ambiguity) and how do we recover?”