Explore all the powerful capabilities of Cogira platform. From document ingestion to AI-powered research, discover what makes our platform different.
Advanced document processing with structure-aware parsing, editable metadata, and multimodal support. Since processing quality directly impacts the effectiveness of search and research tasks, Cogira is designed to extract information from source documents with the highest possible accuracy. As a result, initial processing may take longer than in simpler systems, but it delivers significantly higher-quality results.
| Feature | Description |
|---|---|
| Multi-format Support | Process documents in multiple formats, including PDF, DOCX, TXT, Markdown, EPUB, and image files. |
| Structure-aware Chunking | Intelligent chunking that preserves document hierarchy and context, unlike naive text splitting. |
| Editable Metadata Extraction | Automatically extract and review metadata, then edit and refine it before storage—unlike fully automated systems. In addition to standard metadata (title, authors, language), the system applies AI-based extraction to capture detailed fields such as ISBN, ISSN, DOI, publisher, publication year, subject, and keywords. It also attempts to automatically reconstruct the document’s table of contents based on its structure, significantly improving search efficiency. |
| Docling Integration | Advanced PDF processing using IBM’s Docling system, with accurate layout analysis, table extraction, and document understanding. While simpler tools typically extract only raw text, Docling also detects document structure, identifies images, figures, and tables, and enables semantic boundary detection—significantly improving chunking quality. |
| Multimodal Processing | Process documents containing text, images, and tables together while preserving relationships between them. Images, figures, and tables are enriched with AI-generated descriptions and embedded into the vector space alongside text, making them fully searchable. |
| Asynchronous Processing Pipeline | Non-blocking background processing on separate infrastructure for large documents, ensuring fast search and research even during heavy ingestion workloads. |
| Adaptive Embedding | Optimized embedding strategies for different content types, improving retrieval accuracy across diverse documents. |
| Optimized Storage | Efficient S3-compatible storage with automatic compression and intelligent caching for fast access. |
Efficient search and AI-powered research capabilities with academic citations and agent-based workflows.
| Feature | Description |
|---|---|
| Semantic Search | Vector search based on semantic similarity instead of simple keyword matching across all content types. Interprets natural, human-language queries instead of rigid search expressions in any language supported by the selected large language model (LLM). |
| Dataset-level Filtering | During search operations, you can specify which datasets the system should consider. This allows isolated analysis of a topic based on specific document collections or ensures responses rely only on the most up-to-date policies. |
| Controlled Search Parameters | Provides a simple and intuitive interface to configure key parameters required for search and large language models (selected datasets, maximum token usage, top K, reranking parameters, etc.). Built-in help explains each parameter. |
| Intelligent Reranking | Improves result relevance using advanced reranking models that rescore results based on query context, making complex research more accurate and cost-efficient. |
| Academic Citations (APA 7) | Automatically formatted academic citations in APA 7 style, including full references. |
| Deep Research / Agentic RAG | Multi-step query decomposition and autonomous research workflows that gather and synthesize information across sources. |
| Streaming Responses | Real-time response generation in a streaming format, providing immediate feedback while results are being generated. |
| Query Optimization | Automatic query expansion and refinement using AI to improve search quality and discover relevant information. |
Manage conversations with thread organization, auto-summarization, and persistent context.
| Feature | Description |
|---|---|
| Thread Management | Organize conversations into threads for better topic tracking and historical reference. |
| Thread Configuration | Customize AI models parameters, and other settings per thread for specialized conversations. |
| Auto-Summarization | Automatic conversation summarization that captures key points and decisions, reducing context bloat. |
| Conversation Context | Maintain context across messages with intelligent memory management for coherent multi-turn conversations. |
Control access and manage users with granular permissions across isolated workspaces.
| Feature | Description |
|---|---|
| Granular Dataset Permissions | Control access to datasets at a fine-grained level, ensuring users only see content they're authorized to view. |
| User Groups | Organize users into groups for streamlined permission management and team-based access control. |
| Multi-Tenancy | Complete workspace isolation with separate tenants, ensuring data sovereignty and security for each organization. |
| API Key Management | Secure programmatic access with API key management, enabling safe integration with external tools and services. |
Comprehensive audit logs and observability tools for transparency and debugging.
| Feature | Description |
|---|---|
| Comprehensive Audit Logs | Track all system operations, including user activities, API calls, and data access, to ensure security and compliance. |
| Agentic Session Overview | Step-by-step visibility into AI agents’ decision-making processes for transparency and debugging complex workflows. |
| Ingestion Pipeline Logs | Detailed logs of document processing, including chunking, embedding, and storage operations to support troubleshooting. |
| Token Usage Tracking | Token-based cost tracking and analysis for all operations, enabling precise cost monitoring and optimization. |
Support for using your own API keys across multiple AI providers, with advanced error handling.
| Feature | Description |
|---|---|
| Multiple LLM Providers | Support for OpenAI, Google Vertex AI, Voyage AI, and local models — switch providers anytime without code changes. |
| Support for Multiple Embedding, Generation, and Vision Models | Choose between different providers and models for vector generation, image analysis, and response generation based on your needs and budget. |
| Multiple Reranking Models | Use Voyage AI models or disable reranking entirely based on your needs. |
| Provider Error Handling | Robust handling of provider errors with automatic retries and clear error messages for faster troubleshooting. |