The most powerful multimodal RAG engine, ever.

Ragie’s battle-tested RAG engine handles it all—audio, video, PDFs, images, and text. Built for scale, tuned for accuracy, and ready for your most complex data.

PARSING

Normalizing input across formats

Ragie begins by ingesting raw data from multiple sources and converting it into structured elements. This ensures consistency and readiness for downstream processing.

Multi-modal format support

Processes PDFs, DOCs, HTML, Markdown, audio, video and more—streamlining pipelines that handle real-world, diverse data sources.

Rich metadata extraction

Automatically extracts metadata like titles, headers, authors, timestamps, and page numbers — enhancing document traceability and providing crucial retrieval context.

Extraction

Identify key signals in raw inputs

Once your data is parsed, Ragie detects and tags critical signals like metadata, entities, and document boundaries — laying the groundwork for high-quality, context-aware retrieval.

Enterprise Accounts

Spoke with Daniel Perez, Director of Procurement at Nimbus Solutions, on April 12th. He confirmed they’re moving forward with the Enterprise renewal and requested an updated quote by April 18. The revised proposal should include premium support and additional usage credits. Legal review is being handled by Erika Tanaka. Follow-up to be sent from sarah.malik@nimbus.com

Entity extraction

Uses LLM-driven entity extraction to tag names, dates, contacts, and domain-specific fields — enabling smarter chunking and high-relevance retrieval.

Multimodal signal detection

Detects structural signals like page numbers and timestamps across formats like PDFs, transcripts, audio, and video — helping Ragie segment content more intelligently and preserve context during chunking.

Refinement

Optimizing data for RAG with structured enhancements

Transforms parsed content into high-quality, LLM-ready inputs using a combination of formatting normalization and model-powered enrichment.

LLM-based content enhancement

Applies language and vision models to enrich raw content — including generating captions for images and video, and converting tables into structured, validated Markdown — ensuring richer context for downstream generation.

Format normalization & cleanup

Standardizes layouts, fixes messy formatting, and resolves inconsistencies across formats like HTML, transcripts, and scanned documents, — producing clean, high-signal inputs ready for indexing and retrieval.

CHUNKING

Chunking

Structuring content for precision recall

This phase segments content into logically grouped chunks that maximize retrieval quality and generation fidelity.

Nimbus Solutions maintains a globally distributed infrastructure designed for high reliability, low latency, and strict compliance with enterprise standards.

Our primary offering focuses on high-availability data storage and processing, purpose-built for critical enterprise-grade applications.

The architecture includes five Tier III+ data centers located across North America and Europe, with additional expansion into the APAC region planned for Q1 2026.

Image of a partially constructed data center showcasing empty server racks aligned in a standard hot/cold aisle layout. Overhead cable trays and visible trusses suggest active infrastructure installation. Equipment rests on wooden pallets, indicating the staging phase. Environment is industrial, with exposed ceilings and minimal cabling completed. Useful for understanding early-stage rack layout and physical setup processes in data center deployment.

Specialized chunkers by data type

Leverages purpose-built chunkers for tables, JSON, audio transcripts, video captions, and free-form text — ensuring each content type is split along semantically meaningful and retrieval-friendly boundaries.

Context-preserving techniques

Supports sliding windows and overlap strategies to maintain coherence across chunk boundaries, improving downstream recall—ideal for domains requiring continuity (e.g., legal, technical docs).

INDEXING

Embedding and organizing for scalable search

After chunking, Ragie builds multiple layers of indexes to support fast, accurate, and context-aware retrieval across use cases.

Vector

Keyword

Summary

Vector indexing for semantic search

Ragie transforms each content chunk into vector embeddings, enabling semantic search based on meaning rather than keyword match.

Keyword Indexing for fast text-based retrieval

Extracted keywords from each chunk are indexed to support lightweight, high-speed keyword-based querying.

Summary indexing for high-level recall

Generates concise summaries for each document, enabling hierarchical search across a wide breadth of sources — ideal for high-level overviews or multi-document context.

RETrIEVAL

RETRIEVAL

Delivering context-rich results for generation

At query time, Ragie intelligently retrieves the most relevant chunks from its multi-index system — providing grounded, high-quality context for your choice of LLM.

Two-pass retrieval strategy

Combines fast initial recall with an advanced precision re-ranker — filtering and reordering chunks based on semantic match, keyword signal, and summary relevance to deliver highly contextual retrievals.

Context optimization for any LLM

Selects and compresses top-ranked chunks to fit your model's input window — maximizing context density while preserving relevance, regardless of which LLM you choose.

Reliability your business can count on when it matters the most.

Optimized ingestion throughput

Ragie’s ingestion pipeline scales with your business demands, effortlessly handling bursty workloads while running processes in parallel—ensuring slow tasks never block faster ones.

SLAs

Webhooks provide real-time monitoring by instantly notifying you of connection events or changes, ensuring seamless tracking and faster response to issues.

Accurate results

Ragie’s pipeline has been battle-tested across real-world workloads to deliver consistently accurate answers. From chunking to retrieval, every layer is tuned for precision—so you can get insights with confidence.

Ragie outperforms industry benchmarks, balancing best-in-class performance and real-world scalability.

View Benchmarks

Evaluation metrics

Recall

Recall measures how effectively a RAG system retrieves all relevant information from a knowledge base without missing any critical insights.

Precision

Precision ensures a RAG system retrieves only the relevant information, filtering out noise.

Accuracy