Blue Machines AI, an enterprise-grade conversational AI platform, has launched Floe, a proprietary context-aware language detection model designed to help AI agents determine not only the languages customers use, but also whether they intend to switch the language of a conversation.
Built for India’s multilingual communication patterns, Floe currently supports English, Hindi, Tamil, Telugu, Gujarati, Kannada, Malayalam, Marathi, Bengali, Odia and Punjabi.
Designed for India’s code-mixed conversations
Indian customers frequently combine English product names, financial terminology, acronyms and business vocabulary with regional-language grammar. This code-mixing can pose challenges for conventional language-detection systems, which may interpret English words as a signal to shift the entire conversation to English.
Floe is designed to distinguish between the presence of a language and the customer’s actual language preference.
The model analyses words, parts of speech, sentence structure, short utterances and previous conversational context before deciding whether an AI agent should continue responding in the current language or initiate a language switch.
For instance, the statement “Mera credit card block ho gaya hai” contains the English term “credit card”, but its grammatical structure and conversational intent remain primarily Hindi. A keyword-based system could interpret the English phrase as a reason to respond in English.
Floe instead considers the broader conversational context and can help the agent continue in Hindi unless there is sufficient evidence that the customer genuinely wants to switch languages.
Context-aware language switching
The model is also designed to interpret short responses such as “haan”, “okay”, “correct” and “theek hai” without unnecessarily changing the language of the conversation.
Rather than classifying every customer utterance independently, Floe maintains awareness of the language established in previous turns and looks for explicit or sustained evidence of a genuine transition.
Blue Machines AI, Founder and CEO, Nirmit Parikh, 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.”
He said integrating the language decision into the real-time orchestration layer can help enterprises deliver multilingual experiences while maintaining the reliability, compliance and performance required in production environments.
Less than 10-millisecond latency
Blue Machines AI said Floe has demonstrated internally measured latency of less than 10 milliseconds under production-scale conditions.
The low latency allows language decisions to be made within the real-time conversational path without perceptible delays. The model is also designed to reduce dependency on GPU infrastructure for routine inference.
Within the Blue Machines AI platform, Floe’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.
Built for enterprise workflows
The company said the context-aware approach is aimed at enabling smoother multilingual customer interactions while supporting workflow completion and business outcomes.
Blue Machines AI, Chief Technology Officer, Abhishek Ranjan, said language switching in enterprise AI needs to operate within the live conversational path without slowing down interactions.
“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,” he said.