## 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

#### Details
- **Name:** Daniel Perez  
- **Job Title:** Director of Procurement  
- **Company Name:** Nimbus Solutions  
- **Date:** April 12th  
- **Renewal Type:** Enterprise renewal  
- **Requested Add-Ons:** Premium support, usage credits  
- **Email Address:** sarah.malik@nimbussolutions.com

### Entity extraction

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

## 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.

### Company Overview
- **Company Logo:** Logo consisting of overlapping blue and yellow circles followed by the text “Nimbus Solutions” in large, bold font.
- **Overview Text:** “At Nimbus Solutions, we don’t just house your data—we power your business. Our state-of-the-art data centers are designed for enterprises that demand reliability, scalability, and uncompromising security.”
- **Company Address:** “Nimbus Solutions, Inc. 
Global Headquarters 
4550 Horizon Parkway, Suite 300 
San Vista, CA 94082, USA”

#### Features Table
| Feature    | Details     |
|------------|-------------|
| High Availability | Purpose-built for critical enterprise-grade applications |
| Global Infrastructure | Tier III+ data centers located across North America and Europe |

### Infrastructure Images

Visual evidence of infrastructure. Grid of 3 data center photos—servers, cables, and monitoring equipment. One image labeled “New Mexico Data Center.”

## Chunking

### Structuring content for precision recall

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

### 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.

#### Indexing techniques
- **Vector Indexing:** Transforms each content chunk into vector embeddings, enabling semantic search based on meaning rather than keyword match.
- **Keyword Indexing:** Extracted keywords from each chunk are indexed to support lightweight, high-speed keyword-based querying.
- **Summary Indexing:** Generates concise summaries for each document, enabling hierarchical search across a wide breadth of sources.

## 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.