Ragie - Multimodal RAG for Audio & Video

Product Introduction

1. Ragie is a fully managed multimodal RAG-as-a-Service platform designed to transcribe, index, and retrieve answers from audio, video, and visual content with precise timestamps and streaming playback.
2. The core value of Ragie lies in its ability to transform unstructured spoken and visual data into searchable, context-rich insights, enabling users to instantly locate and interact with specific moments in multimedia content through AI-powered retrieval.

Main Features

1. Ragie processes audio and video files by transcribing speech, extracting visual elements, and indexing them with timestamped metadata for frame-accurate retrieval.
2. The platform supports hybrid search capabilities that combine semantic understanding of spoken content, optical character recognition (OCR) for visual text, and keyword matching to deliver highly relevant results.
3. Enterprise-grade features include SOC 2-compliant security, multi-tenant data isolation via partitions, and scalable infrastructure capable of handling large-scale multimedia datasets with automatic syncing.

Problems Solved

1. Ragie addresses the challenge of efficiently searching through hours of audio/video recordings or visual content to find specific spoken phrases, on-screen text, or visual references without manual scrubbing.
2. The product targets developers and enterprises building AI applications that require multimodal retrieval, such as legal tech platforms, e-commerce intelligence tools, and media analysis systems.
3. Typical use cases include analyzing customer support calls for compliance verification, extracting product mentions from video demos, and identifying contractual terms in recorded negotiations with timestamped evidence.

Unique Advantages

1. Unlike text-only RAG systems, Ragie natively processes audio/video through dedicated pipelines that preserve temporal context and synchronize transcribed speech with visual frames.
2. The platform innovates with LLM-aware chunking strategies that maintain conversational context across speaker changes and scene transitions, coupled with re-ranking algorithms optimized for multimodal recall.
3. Competitive advantages include pre-built connectors for enterprise data sources, sub-second retrieval latency at scale, and proprietary indexing techniques that achieve 99.4% recall accuracy in benchmark tests across multimodal datasets.

Frequently Asked Questions (FAQ)

1. How does Ragie handle different audio/video formats? Ragie automatically normalizes inputs from various codecs and containers (MP4, MOV, WAV, etc.) into standardized transcripts with frame-accurate timestamps, while preserving original quality for playback.
2. Can Ragie integrate with existing enterprise data storage? Yes, Ragie provides secure API endpoints and pre-built connectors for cloud storage platforms, CMS systems, and collaboration tools like Google Drive and Notion, with optional VPC deployment for sensitive data.
3. What makes Ragie's retrieval more accurate than basic transcription search? Ragie employs multimodal embedding models that analyze speech patterns, visual context, and temporal relationships simultaneously, combined with LLM re-ranking that understands query intent beyond keyword matching.