Building Roshni: A Real-Time, Multi-Agent Financial Voice AI for Bharat 🇮🇳

Building Roshni: An Ultra-Low Latency, Multi-Agent Financial Voice Assistant for Bharat 🇮🇳 How I built an end-to-end, multilingual financial voice AI using Murf Falcon, LiveKit Agents, Deepgram Nova-3, Google Gemini, and Next.js during the 10 Days of AI Voice Agents Challenge. 🌟 1. The Problem & Why Voice Matters for Bharat In India, financial inclusion has accelerated rapidly with UPI, digital banking, and government-backed credit initiatives. However, navigating complex interest rates, eligibility criteria for government schemes (like PM Mudra or Sukanya Samriddhi Yojana), and understanding formal banking terms remains intimidating for millions of citizens—especially in regional and tier-2/3 heartlands where digital interfaces can be overwhelming. Text-first interfaces fail where voice thrives. When rural entrepreneurs or first-time bank customers have questions, they don't want to navigate complex web forms or read dense PDFs. They want to ask a direct question in their language and get an immediate, clear, spoken answer. To solve this, I built Roshni AI (and her specialist counterpart, Vikram) — an ultra-low latency, conversational financial assistant engineered for natural voice interactions in English, Hindi (Devanagari script), and Hinglish. 🏗️ 2. High-Level Architecture & Tech Stack Building a real-time conversational agent requires synchronizing four core pipelines with sub-second latency: [ 👤 User Microphone ] │ (WebRTC Audio Stream) ▼ ┌─────────────────────────────┐ │ LiveKit Agents Worker │ └──────────────┬──────────────┘ │ ┌───────────────────────┼───────────────────────┐ ▼ ▼ ▼ ┌─────────────┐ ┌─────────────┐ ┌─────────────────┐ │ Deepgram │ ────► │Google Gemini│ ────► │ Murf Falcon │ │ Nova-3 │ │ (LLM) │ │ Fast TTS │ │ (Fast STT) │ │ │ │ (Anisha / Samar)│ └─────────────┘ └──────┬──────┘ └────────┬────────┘ │ (Tool / Handoff) │ ▼ ▼ ┌───────────────┐ [ 🔊 Audio Output ] │ SQLite Memory │ │ & Analytics │ └───────────────┘ The Stack: TTS (Text-to-Speech): Murf Falcon — Delivering conversational Indian voices (Anisha for general banking and Samar for specialist schemes) with lightning-fast Time-To-First-Byte (TTFB ~95ms). STT (Speech-to-Text): Deepgram Nova-3 (language="multi") for instantaneous multi-language speech recognition. LLM Engine: Google Gemini (gemini-3.5-flash-lite / gemini-2.0-flash) with strict prompt guardrails. Real-time Transport & Orchestration: LiveKit Agents Framework + Silero VAD for natural turn-taking and interruption handling. Frontend Portal & Dashboard: Next.js 15, Tailwind CSS, and LiveKit Components React. Persistence & Call Analytics: SQLite with instant synchronous write pipelines. 🚀 3. Key Capabilities Built 🎙️ 1. Ultra-Realistic Indian Voices via Murf Falcon Using Murf Falcon’s conversational voices transformed the agent from a robotic IVR into a warm, approachable advisor. By pacing sentences naturally and configuring TTFB optimization, conversational latency stayed under 1 second. 🔤 2. Native Devanagari Script Enforcement A common flaw in bilingual voice bots is sending romanized Hindi (e.g., "namaste, aapka swagat hai") to TTS engines, causing English phonetic engines to mispronounce Indian syllables. I enforced strict system prompt constraints: Always write Hindi in Devanagari script (e.g., "नमस्ते, आपका स्वागत है"). Never write romanized Hindi. This simple prompt architecture ensured flawless Indian accent pronunciation every single turn. 🧠 3. Persistent Memory & User Recognition Using SQLite (agent_memory.db), Roshni stores caller identity, preferred languages, and previous queries. Returning users are greeted by name with tailored suggestions based on their recorded financial profile. 🛠️ 4. Tool Execution & Dynamic Rate Lookups Roshni accesses custom tools like check_scheme_rates to provide live, structured interest rate calculations for Fixed Deposits, Senior Citizen schemes, and Savings accounts without LLM hallucinations. 🤝 5. Multi-Agent Specialist Handoff (Day 9 Milestone) One agent should not try to be an expert in everything. I split responsibilities between two distinct agents: Roshni (General Banking): Powered by Murf Falcon Anisha. Handles general banking and savings queries. Vikram (Government Scheme Specialist): Powered by Murf Falcon Samar. Specializes in PM Mudra loans, subsidies, and government welfare programs. When a caller asks about government schemes, Roshni announces: "I will connect you to Vikram, our Government Scheme Specialist", and hands over the full conversation history to Vikram in real time. python # Multi-Agent Handoff in LiveKit @function_tool async def transfer_to_scheme_specialist(self, context: RunContext, query: str) -> str: """Handoff caller to Vikram for Government Scheme queries.""" self.session_state["is_success"] = True record_call_outcome(self.call_id, "SUCCESS", "Transferred to Scheme Specialist") return "I will connect you to Vikram, our Government Scheme Specialist."