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Aabhi.ai
PROJECT 02Voice AIProduction

Real-Time Multimodal AI Meeting & Sales Agent Suite

Autonomous Full-Duplex Google Meet & Zoom Voice Agent + Copilot

Full-duplex multimodal AI meeting agent and copilot that autonomously joins Google Meet/Zoom calls via MeetStream.ai, conducting live voice sales consultations with sub-400ms latency and real-time SSE lead scoring.

< 400msGlass-to-Glass Latency
0–100Real-Time BANT Score
7-PhaseSales Consultation Engine
15+ minSliding Window Context
Live System Interface
Real-Time Multimodal AI Meeting & Sales Agent Suite live interface

The Engineering Challenge

Context & Domain

Enterprise sales consultations and discovery calls demand deep technical qualification, instantaneous objection handling, and synchronized CRM note-taking. High-value prospect calls frequently suffer from delayed follow-ups and unstandardized BANT scoring.

Core Problem

Cascaded STT→LLM→TTS architectures experience 3+ second delays, catastrophic audio buffering during turn interruptions (barge-in), and complete failure to handle 48kHz native video meeting audio across Google Meet, Zoom, and MS Teams.

Architectural Constraints
  • Sub-400ms glass-to-glass turn latency across live WebRTC audio streams
  • Zero-click autonomous Google Meet and Zoom bot injection via MeetStream.ai & LiveKit
  • Sample rate bridging between 48kHz meeting audio and Gemini 16kHz input / 24kHz output
  • Continuous sliding window context compression without losing qualification facts on 15+ min calls

System Architecture

End-to-end data pipelines and component isolation boundaries.

Interactive Component SVG
Meet / Zoom48kHz PCMNumPy DSPResamplerLiveKit VADGemini 3.1 LiveDuplex S2S7-Phase EngineSilent CopilotBANT (0-100)SSE StreamLatency<400ms

Ultra-low-latency raw binary PCM stream processed over WebSockets with vectorized NumPy DSP resamplers bridging 48kHz audio to Gemini 3.1 Flash Live (16kHz in / 24kHz out). Features a 7-phase sales consultation engine, dynamic client profiling, and a concurrent Silent Observer streaming sentiment and BANT scores via Server-Sent Events (SSE).

Key Engineering Decisions

DECISION 01

Vectorized NumPy DSP Audio Resampling

Engineered ultra-low-overhead vectorized buffer resamplers bridging 48kHz WebRTC audio streams directly to Gemini Multimodal Live API (16kHz input / 24kHz output) over raw binary WebSockets.

DECISION 02

Automated 7-Phase Sales Consultation Engine

Built a structured conversational state machine with dynamic client profiling (technical vs non-technical), automated language switching, and strict pricing/anti-hallucination guardrails.

DECISION 03

Concurrent Silent Observer & Real-Time Copilot via SSE

Decoupled analysis agent delivering live speech diarization, sentiment tracking (-1.0 to +1.0), and BANT lead scoring (0–100) streamed via Server-Sent Events to an interactive monitoring dashboard without interrupting audio.

Production Verification & Results

  • Achieved human-like conversational turn-taking with natural barge-in (<400ms response time)
  • Engineered 25k → 10k token sliding window compression enabling 15+ minute uninterrupted discovery calls
  • Delivered live speech diarization, sentiment tracking, and BANT lead scoring via real-time SSE stream
  • Successfully integrated autonomous meeting join pipeline across Google Meet, Zoom, and Teams via MeetStream.ai

Production Stack & Tooling

PythonFastAPIGemini 3.1 Flash LiveLiveKit Agents SDKWebSocketsNumPy (DSP)Server-Sent Events (SSE)DockerMeetStream.ai

Next Iterations & Roadmap

  • Real-time visual screen-sharing comprehension during live product demos
  • Automatic bidirectional CRM sync into HubSpot & Salesforce post-call

Want to build something similar?

I specialize in taking complex agentic workflows, duplex voice architectures, and high-throughput computer vision models into production.