Blue Machines AI Launches Floe, a Context-Aware Language Detection Model for Multilingual Enterprise Conversations in India

Blue Machines AI, an advanced enterprise-grade Conversational AI platform, today announced the launch of Floe, its proprietary context-aware language detection model, designed to help AI agents determine not only which languages a customer is using, but whether the customer actually intends to change the language of the conversation.

Built for India’s multilingual communication patterns, the model currently supports English, Hindi, Tamil, Telugu, Gujarati, Kannada, Malayalam, Marathi, Bengali, Odia, and Punjabi.

In India, customers frequently combine English product names, financial terminology, acronyms, and business vocabulary with regional-language grammar. This everyday code-mixing can create challenges for conventional language-detection systems, which may interpret the presence of English words as a signal to switch the entire conversation to English.

Blue Machines AI’s model is designed to distinguish between language presence and actual language preference. It analyses words, parts of speech, sentence structure, short utterances, and prior conversational context before deciding whether the AI agent should maintain its current response language or initiate a switch.

For example, the phrase “Mera credit card block ho gaya hai” contains the English term “credit card”, but the grammatical structure and conversational intent remain primarily Hindi. A keyword-led system may treat this as a reason to respond in English. Blue Machines AI’s model uses broader conversational context to help the agent continue in Hindi unless there is sufficient evidence that the customer genuinely intends to switch languages.

The system is also designed to interpret short responses such as “haan”, “okay”, “correct”, and “theek hai” without unnecessarily changing the conversation language. Rather than classifying each utterance independently, the model maintains awareness of the language established across previous turns and looks for explicit or sustained evidence of a genuine transition.

Mr. Nirmit Parikh, Founder and CEO, Blue Machines AI, said: “In enterprise conversations, language is not a static setting; it is a decision that can change during an interaction. The challenge is not merely to identify the languages being spoken, but to understand which language the customer expects the agent to use. By bringing that decision into the real-time orchestration layer, enterprises can offer multilingual experiences that remain natural while preserving the reliability, compliance, and performance required in production.”

The model has demonstrated internally measured latency of less than 10 milliseconds under production-scale conditions. This allows language decisions to occur within the real-time conversational path without introducing perceptible delay, while reducing dependency on GPU infrastructure for routine inference.

Within the Blue Machines AI platform, the model’s output informs orchestration decisions across speech recognition, conversational models, text-to-speech voice and pronunciation, regional terminology, language-specific prompts, compliance disclosures, escalation, routing, and conversation analytics.

By evaluating language throughout an interaction rather than only at its beginning, the system is designed to reduce unnecessary clarifications, repeated language switching, and inconsistent agent behaviour while supporting smoother workflow completion and business outcomes.

Mr. Abhishek Ranjan, Chief Technology Officer, Blue Machines AI, said: “For enterprise AI, language switching has to work inside the live conversational path without slowing the interaction down. Our model is optimised for CPU inference and has demonstrated latency of less than 10 milliseconds under internally measured production-scale conditions. This allows the language decision to feed directly into speech, voice, compliance, and routing workflows while the conversation is happening.”