Blue Machines AI Launches Floe to Enable Context-Aware Language Detection for Multilingual Enterprise Conversations

Blue Machines AI, Apna Group's enterprise-grade Conversational AI platform, has launched Floe, a proprietary context-aware language detection model designed to help AI agents determine not only which languages are present in a conversation, but whether a customer actually intends to switch languages.

Built for India's multilingual communication patterns, Floe currently supports English, Hindi, Tamil, Telugu, Gujarati, Kannada, Malayalam, Marathi, Bengali, Odia, and Punjabi across multimodal voice and chat interactions, including channels such as WhatsApp and SMS.

Moving Beyond Keyword-Based Language Detection

In everyday Indian conversations, customers frequently combine English product names, financial terminology, acronyms, and business vocabulary with regional-language grammar.

For conventional language detection systems, this can create ambiguity. The presence of a few English words, for example, may incorrectly trigger a switch to English even when the customer continues communicating primarily in Hindi or another regional language.

Floe is designed to address this challenge by distinguishing language presence from actual language preference.

The model analyses words, parts of speech, sentence structure, short utterances, and previous conversational context before determining whether an AI agent should maintain its current response language or initiate a switch.

For instance, in the phrase "Mera credit card block ho gaya hai," the English phrase "credit card" appears within an otherwise Hindi sentence. Rather than interpreting the English terminology as an instruction to switch languages, Floe considers the broader grammatical and conversational context.

Maintaining Context Across Conversations

Floe is also designed to handle short conversational responses such as "haan," "okay," "correct," and "theek hai" without unnecessarily changing the established language of an interaction.

Instead of treating every utterance as an independent language-detection event, the model maintains awareness of the language established across previous turns and looks for sustained or explicit evidence that the customer intends to change languages.

This approach is designed to help enterprise AI agents maintain more consistent conversational behaviour while reducing unnecessary language switching and clarification requests.

Designed for Real-Time Enterprise AI

According to Blue Machines AI, Floe has demonstrated internally measured latency of less than 10 milliseconds under production-scale conditions.

The model is designed for CPU inference, allowing language decisions to be made within the real-time conversational path without introducing perceptible delays while reducing reliance on GPU infrastructure for routine inference.

Within the Blue Machines AI platform, Floe's output can inform orchestration decisions across multiple components of an AI conversation, including speech recognition, conversational models, text-to-speech, 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 support smoother multilingual workflows and more consistent AI-agent behaviour.

Leadership Perspective

Nirmit Parikh, Founder and CEO of Blue Machines AI, said that: "language in enterprise conversations should not be treated as a static setting, but as a decision that can change throughout an interaction."

He emphasized that the challenge is not simply identifying which languages are being used, but understanding which language the customer expects the AI agent to use, with that decision incorporated into the real-time orchestration layer.

Abhishek Ranjan, Chief Technology Officer at Blue Machines AI, highlighted the importance of enabling language switching within the live conversational path without slowing the interaction. He noted that Floe's CPU-optimized architecture and internally measured sub-10 millisecond latency allow its language decisions to feed directly into voice, compliance, and routing workflows.

Supporting India's Multilingual AI Landscape

As enterprises expand AI-powered customer interactions across India's diverse linguistic landscape, maintaining conversational context becomes increasingly important.

Floe represents Blue Machines AI's effort to address this challenge at the orchestration layer, helping enterprises build multilingual AI experiences that account for the way customers naturally combine languages rather than relying solely on isolated keywords.

About Blue Machines AI

Blue Machines AI is Apna Group's enterprise-grade Voice AI platform, designed to help organizations deploy compliant, low-latency, multilingual Voice AI agents at scale.

The platform combines production-ready infrastructure with Forward-Deployed Engineer (FDE) support for integration and real-time issue resolution. According to the company, organizations can deploy Voice AI in under a week and operate the systems at scale.