Blue Machines AI has launched Floe, a language detection model for multilingual customer conversations aimed at enterprise use in India.
Floe is designed to determine whether a customer intends to change the language of a conversation, rather than reacting simply to the presence of words from another language. It supports English, Hindi, Tamil, Telugu, Gujarati, Kannada, Malayalam, Marathi, Bengali, Odia and Punjabi across voice calls, WhatsApp and SMS.
The focus reflects a common feature of Indian customer interactions, where speakers often mix English product terms, acronyms and financial vocabulary into sentences built around a regional language. In such exchanges, conventional detection systems can shift the conversation into English even when the customer has not asked for that change.
According to Blue Machines, the model analyses words, parts of speech, sentence structure, short utterances and prior conversational context before deciding whether an AI agent should stay in the current language or switch. The goal is to prevent unnecessary changes in the middle of a live interaction.
One example is the Hindi sentence "Mera credit card block ho gaya hai", which includes an English product term but remains Hindi in grammar and intent. In that case, the system is designed to keep the response in Hindi unless there is stronger evidence that the speaker wants to move to English.
It is also built to interpret short replies such as "haan", "okay", "correct" and "theek hai" without treating those words alone as a prompt for a language change. Rather than classifying each line in isolation, the model tracks the language established earlier in the exchange and looks for sustained signs of a genuine transition.
Real-time use
Blue Machines said internal tests showed latency of less than 10 milliseconds under production-scale conditions. Floe also runs on CPU infrastructure rather than relying on GPUs for routine inference.
That matters because language decisions in voice and chat systems must happen during a live exchange, not after it has finished. Delays at that point can disrupt speech recognition, text-to-speech output, routing and compliance disclosures.
Within Blue Machines' platform, Floe feeds into orchestration decisions covering speech recognition, conversational models, pronunciation, regional terminology, prompts, escalation, routing and analytics. The approach allows language to be assessed throughout a conversation instead of only at the start.
India focus
The launch reflects the challenge of building automated customer service systems for a market where multilingual communication is routine. India's contact centre, financial services and consumer internet sectors often serve users who move between English and regional languages within the same conversation.
For businesses, the challenge is not only identifying which languages appear in a sentence, but determining which language the customer expects in reply. An incorrect switch can force a customer to repeat information or continue in a less comfortable language.
Blue Machines said Floe is intended to reduce repeated clarifications, unnecessary switching and inconsistent behaviour by AI agents. It described the model as part of the decision layer inside active voice and messaging interactions.
"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," said Nirmit Parikh, Founder and Chief Executive Officer of Blue Machines AI.
Blue Machines is part of Apna Group and describes itself as a voice AI platform for organisations deploying multilingual automated agents. Its infrastructure is built for compliant, low-latency voice AI use cases and supports integrations for enterprise customers.
The launch places language selection closer to the operational core of automated customer conversations, where a mistaken inference can affect everything from pronunciation to escalation pathways. "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," said Ranjan.