Blue Machines AI's Floe Detects Genuine Language Switches

Blue Machines AI has introduced *Floe*, a proprietary context-aware language detection model built to help AI agents navigate India's multilingual conversations with greater nuance. Rather than simply identifying the language a customer is speaking, Floe is designed to determine whether the customer genuinely intends to switch languages during an interaction, a distinction the company says is critical for enterprise voice and chat systems.

The model differentiates between everyday code-mixing, such as using an English word like "credit card" within a Hindi sentence, and an actual shift in conversational language. By analysing grammar, sentence structure, short utterances like *"haan"* or *"theek hai"*, and prior context, Floe aims to prevent AI agents from unnecessarily changing languages mid-conversation.

Currently supporting English and ten Indic languages, including Hindi, Tamil, Telugu, Gujarati and Kannada, Floe has demonstrated an internally measured latency of under 10 milliseconds, allowing it to operate within live conversational flows without perceptible delay. The model's outputs feed into broader orchestration decisions across speech recognition, text-to-speech, compliance and routing within Blue Machines AI's platform.