"Conversational AI" gets used loosely to describe everything from a simple FAQ bot to a fully voice-driven digital assistant. The common thread is that the system is built to hold a back-and-forth exchange in natural language, rather than just matching keywords to canned answers.
A conversational AI system generally combines a few pieces working together:
Older chatbots relied on rigid decision trees: type the wrong phrase and you'd hit a dead end. Modern conversational AI, built on LLMs, can handle open-ended questions, follow-ups, and phrasing it's never seen before.
The distinction that actually matters isn't the marketing term, it's flexibility. A basic chatbot answers questions it was scripted for. A true conversational AI can generalise - answer a question phrased in an unexpected way, hold context across several turns, and recover gracefully when it doesn't understand something the first time.
Text-based conversational AI is useful, but it's still a one-dimensional channel. Add voice, and the interaction starts to feel like a phone call rather than a form. Add a visual, animated face - a digital human - and you get something closer to an in-person conversation: expressions, eye contact, and timing all carry information that plain text can't.
This is the layer Trulience adds on top of conversational AI: rather than replacing your LLM, an interactive avatar gives it a face and a voice, so the same underlying intelligence becomes a conversation instead of a chat log.
Because Trulience is designed to plug into any LLM, you can pair it with whichever model already fits your use case and budget, and change that choice later without rebuilding your front end. The avatar layer - appearance, voice, language, embedding - stays constant even if what's happening behind the scenes evolves.