Guide

How to Architect a Real-Time Deposition Monitoring System

A developer guide to building a system that provides live AI analysis during legal depositions. You will implement real-time audio/video streaming, live transcription, sentiment tracking, and a dashboard for strategic alerts.

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ARCHITECTURE GUIDE

Introduction

This guide details the architecture for a system that provides live, strategic intelligence to attorneys during depositions, enabling dynamic case strategy adjustments.

A Real-Time Deposition Monitoring System processes live audio/video streams to deliver immediate insights to co-counsel. The core architecture consists of three layers: an ingestion layer capturing streams via WebRTC, a processing layer performing live transcription with services like AssemblyAI, and an analysis layer running lightweight models for sentiment tracking and keyword flagging. This builds upon the foundational Deposition Analysis System but is optimized for sub-second latency and continuous data flow.

The system outputs alerts and strategic suggestions to a secure dashboard, allowing attorneys to react to testimony as it happens. Key technical challenges include managing streaming data pipelines, ensuring low-latency inference, and maintaining a secure, multi-tenant environment. This guide provides the actionable steps to architect this system, from selecting real-time ASR services to designing the alerting logic and integrating with existing legal workflows.

REAL-TIME PROCESSING LAYER

Technology Stack Comparison

This table compares core architectural choices for the live audio/video ingestion, transcription, and analysis pipeline. The selection dictates latency, scalability, and the complexity of your deployment.

Architectural Feature	Cloud-Native Serverless (Recommended)	Edge-First Containerized	Hybrid Cloud-Edge
Primary Latency (End-to-End)	< 2 seconds	< 500 milliseconds	< 1 second
Transcription Service	AssemblyAI / Deepgram Stream	Whisper.cpp (On-Device)	Dual-Mode: Cloud w/ Edge Fallback
Real-Time Sentiment Model	Lightweight Distilled Model (e.g., DistilBERT)	Ultra-Lite SLM (e.g., Phi-3-Mini)	Model Routing Based on Network QoS
Keyword/Contradiction Flagging	Streaming RAG with Vector Cache	Pre-loaded Case Glossary & Rule Engine	Hybrid: Local Rules + Cloud RAG
Co-Counsel Alert Delivery	WebSocket Push to Secure Dashboard	Local Network Broadcast	Multi-Channel (WebSocket, SMS)
Offline/Network Drop Resilience
Scalability (Concurrent Sessions)	High (Elastic Cloud Scaling)	Limited (Local Hardware)	Moderate (Managed Edge Nodes)
Implementation Complexity	Medium	High	Very High
Data Sovereignty & Privacy	Dependent on Cloud Provider Region	Full On-Premises Control	Configurable by Workflow Segment

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ARCHITECTURE PITFALLS

Common Mistakes

Building a real-time deposition monitoring system introduces unique challenges in streaming, latency, and reliability. These are the most frequent technical mistakes developers make and how to fix them.

High latency typically stems from inefficient audio streaming or blocking API calls. Real-time means sub-second (<500ms) processing; achieving this requires a WebSocket-based streaming client to send audio chunks as they are captured, not after a full recording.

Common Fixes:

Use a service like AssemblyAI's real-time streaming API with its WebSocket SDK.
Implement client-side buffering to send small, consistent audio packets (e.g., 200-500ms chunks).
Avoid synchronous POST requests; they create stop-and-go processing.
Process the audio stream directly from the deposition video feed using FFmpeg to pipe audio to the transcription service.

Latency kills the utility of 'live' alerts. Test your end-to-end pipeline with a stopwatch from utterance to dashboard alert.

About the author

Prasad Kumkar

CEO & MD, Inference Systems

Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.

His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.

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