Building Voice AI for India: Where Cost and Language Collide

I used to think building a 𝗩𝗼𝗶𝗰𝗲 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 was mostly about choosing a good LLM. I was wrong. 😄 The architecture looks pretty simple: 🎙️ 𝗦𝗧𝗧 → 🧠 𝗟𝗟𝗠 → 🔊 𝗧𝗧𝗦 But making all three work together 𝗶𝗻 𝗮 𝗿𝗲𝗮𝗹 𝗰𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻 is a completely different problem. We’ve been building and testing our Voice AI agent with real conversations, and one thing surprised me: 🔊 𝗧𝗧𝗦 𝗶𝘀 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝘁𝗵𝗲 𝗯𝗶𝗴𝗴𝗲𝘀𝘁 𝗰𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲. Not just technically - 𝗳𝗶𝗻𝗮𝗻𝗰𝗶𝗮𝗹𝗹𝘆 𝘁𝗼𝗼. Our current cost breakdown is roughly: 🔊 𝗧𝗧𝗦 — 𝟳𝟮% 🧠 𝗟𝗟𝗠 — 𝟮𝟬% 🎙️ 𝗦𝗧𝗧 — 𝟳% So the obvious thought is: “Let's use a cheaper LLM.” But that's not always the answer. We tested cheaper models and found something interesting: 𝗜𝗳 𝘁𝗵𝗲 𝗔𝗜 𝘁𝗮𝗹𝗸𝘀 𝗺𝗼𝗿𝗲, 𝗧𝗧𝗦 𝗰𝗼𝘀𝘁𝘀 𝗴𝗼 𝘂𝗽. So sometimes a cheaper LLM can actually make the 𝗼𝘃𝗲𝗿𝗮𝗹𝗹 𝘃𝗼𝗶𝗰𝗲 𝗮𝗴𝗲𝗻𝘁 𝗺𝗼𝗿𝗲 𝗲𝘅𝗽𝗲𝗻𝘀𝗶𝘃𝗲. That's when you realise: 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗩𝗼𝗶𝗰𝗲 𝗔𝗜 𝗶𝘀𝗻'𝘁 𝗷𝘂𝘀𝘁 𝗮𝗯𝗼𝘂𝘁 𝗺𝗮𝗸𝗶𝗻𝗴 𝗔𝗜 𝘀𝗺𝗮𝗿𝘁𝗲𝗿. It's about making the entire conversation 𝗳𝗮𝘀𝘁𝗲𝗿, 𝘀𝗵𝗼𝗿𝘁𝗲𝗿, 𝗰𝗵𝗲𝗮𝗽𝗲𝗿 𝗮𝗻𝗱 𝗺𝗼𝗿𝗲 𝗵𝘂𝗺𝗮𝗻. We are now experimenting with: ⚡ 𝗟𝗼𝘄𝗲𝗿 𝗹𝗮𝘁𝗲𝗻𝗰𝘆 🗣️ 𝗦𝗵𝗼𝗿𝘁𝗲𝗿, 𝗻𝗮𝘁𝘂𝗿𝗮𝗹 𝗿𝗲𝘀𝗽𝗼𝗻𝘀𝗲𝘀 🔊 𝗕𝗲𝘁𝘁𝗲𝗿 𝗧𝗧𝗦 𝘃𝗼𝗶𝗰𝗲𝘀 💾 𝗔𝘂𝗱𝗶𝗼 𝗰𝗮𝗰𝗵𝗶𝗻𝗴 🌍 𝗛𝗶𝗻𝗱𝗶 + 𝗚𝘂𝗷𝗮𝗿𝗮𝘁𝗶 + 𝗘𝗻𝗴𝗹𝗶𝘀𝗵 🎯 𝗕𝗲𝘁𝘁𝗲𝗿 𝗶𝗻𝘁𝗲𝗿𝗿𝘂𝗽𝘁𝗶𝗼𝗻 𝗵𝗮𝗻𝗱𝗹𝗶𝗻𝗴 💰 𝗖𝗼𝘀𝘁 𝗼𝗽𝘁𝗶𝗺𝗶𝘀𝗮𝘁𝗶𝗼𝗻 And honestly, the hardest part is still the same: 𝗠𝗮𝗸𝗶𝗻𝗴 𝗔𝗜 𝘀𝗼𝘂𝗻𝗱 𝗹𝗶𝗸𝗲 𝘀𝗼𝗺𝗲𝗼𝗻𝗲 𝘆𝗼𝘂 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝘄𝗮𝗻𝘁 𝘁𝗼 𝘁𝗮𝗹𝗸 𝘁𝗼. Especially for India 🇮🇳 Where conversations can move between 𝗛𝗶𝗻𝗱𝗶, 𝗚𝘂𝗷𝗮𝗿𝗮𝘁𝗶, 𝗘𝗻𝗴𝗹𝗶𝘀𝗵, 𝗛𝗶𝗻𝗴𝗹𝗶𝘀𝗵, numbers, names and local pronunciation - sometimes in the same call. There is still a 𝗟𝗢𝗧 𝘁𝗼 𝘀𝗼𝗹𝘃𝗲. But that's also what makes it exciting. 🚀 We're building. Measuring real calls. Breaking things. Fixing them. And repeating. 🔁 𝗧𝗵𝗶𝘀 𝗶𝘀 𝘁𝗵𝗲 𝗸𝗶𝗻𝗱 𝗼𝗳 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 𝘄𝗲 𝗹𝗼𝘃𝗲 𝘀𝗼𝗹𝘃𝗶𝗻𝗴 𝗮𝘁 Blutec.AI From India 🇮🇳 Building AI that people can actually 𝘁𝗮𝗹𝗸 𝘁𝗼. #VoiceAI #AI #LLM #TTS #STT #AIEngineering #FoundingEngineer #BuildInPublic #BluTecAI #AIIndia

Building a voice AI agent using the standard STT → LLM → TTS architecture presents challenges beyond choosing a strong LLM, with TTS emerging as both the biggest technical and financial bottleneck in testing with real conversations. In this case, the reported cost breakdown was roughly 72% TTS, 20% LLM, and 7% STT. Testing cheaper LLMs revealed a counterintuitive effect: when the AI produces longer responses, TTS costs rise correspondingly, meaning a cheaper LLM can sometimes increase the overall cost of running the voice agent if it generates more verbose output. This points to voice AI optimization being less about making the underlying model smarter and more about making the full conversation faster, shorter, cheaper, and more natural — areas of ongoing work include reducing latency, shortening responses, improving TTS voice quality, audio caching, handling interruptions, and supporting Hindi, Gujarati, and English within the same conversation. This multilingual handling is particularly challenging in the Indian market, where calls can shift between Hindi, Gujarati, English, Hinglish, numbers, names, and local pronunciation within a single conversation.