Voice agents
and apps for agents.

Voice AI —voice artificial intelligence— is the technology that lets machines listen, understand and speak with humans in natural language, without friction, in real time. We're not talking about rigid IVR commands («press 1 for sales»), nor about text chatbots dressed up with robotic TTS. We're talking about conversational voice agents that combine speech recognition (STT), language models (LLM) and neural text-to-speech (TTS) in an end-to-end pipeline that responds in under a second, with human intonation and natural turn-taking.

2026 is the year this technology stopped being experimental. OpenAI's Realtime API and Gemini Live, cloned voices from ElevenLabs and Cartesia, WebRTC transport over global infrastructure and a new generation of dialogue-optimized models have made building a conversational voice experience 90% cheaper than it was two years ago. For any company that deals with customers by phone —support, scheduling, technical assistance, billing, onboarding or service access— ignoring voice AI is the equivalent of ignoring the web in 1998.

How a conversational voice agent works

A production voice agent isn't a single model — it's six pieces fitted together: Voice Activity Detection (VAD) to know when the user is speaking and when they stop; streaming Speech-to-Text (STT) with multi-speaker diarization and support for 50+ languages; an LLM with function calling and RAG over your corporate knowledge base, which decides what to say and which actions to execute; neural Text-to-Speech (TTS) with cloned voices and chunked streaming so the response starts playing while still being generated; real-time transport via WebRTC, SIP or PSTN; and the hardest layer — turn-taking and barge-in management, which lets the user cut off the agent whenever they take the floor again.

Latency is the product. If the user waits more than 800 milliseconds between the end of their sentence and the start of the agent's response, the experience breaks: it feels like the machine doesn't understand. A well-built agent stays under 500 ms end-to-end on 4G mobile and under 300 ms on WiFi. Achieving that requires streaming at every layer, edge peering, the right model and audio codec, and fine-grained jitter buffer tuning. It isn't a technical detail: it's what separates a cool demo from a usable product.

Voice AI vs traditional IVR: why they aren't comparable

IVR of the last twenty years is based on rigid decision trees: «press 1», «say a keyword», «wait 10 seconds». It works for two or three options and collapses for any request off-script. A voice agent powered by generative AI understands intent, context and nuance. It can resolve in a single sentence what a traditional IVR takes four nested menus to handle, it can escalate to a human when it detects it can't help, and it can personalize the conversation with real-time CRM data —customer name, history, preferences, order status— without the user having to type anything.

The economic impact is measurable. Early voice AI deployments in call centers show 40-70% reductions in cost per handled call, resolution times in seconds instead of minutes, and —critically— satisfaction scores equal to or above those of human agents for structured tasks: order lookup, appointment changes, tier-1 incidents, recurring payments, restocking, onboarding. Humans are reserved for the cases where they truly add differential value: complex sales, major incidents, strategic account relationships.

When voice AI makes sense for your company

Voice AI delivers the highest return when three factors combine: volume (hundreds or thousands of interactions per month currently handled by phone or email), structure (interactions follow repeatable —though not identical— patterns) and urgency (the user values an immediate answer). 24/7 customer support, healthcare triage, drive-thru and retail, hands-free automotive assistants, conversational language tutors and accessibility for users with reduced mobility or vision are the cases with the fastest payback. In contrast, one-off interactions, those with high complexity or zero error tolerance —medical interventions, binding legal decisions, large-scale financial operations— remain firmly human territory.

At Dribba we've been shipping voice AI to production since 2024, combining the most mature APIs on the market with our experience in Flutter apps, high-performance backends and integration with CRMs, ERPs and enterprise telephony. If you have a use case —an overloaded phone line, an app that could answer by voice, a repetitive process eating hours of your team— the first step is a 45-minute session where we analyze feasibility, the recommended stack and expected return. No forms, no commitment.