AutoRAG
Automated Retrieval Augmented Generation that supercharges your AI with instant, accurate knowledge and data integration.
What is AutoRAG?
AutoRAG is Divinci AI's comprehensive solution for automatically finding the optimal RAG pipeline for your specific data and use cases. Unlike generic RAG implementations, AutoRAG evaluates multiple combinations of retrieval and generation strategies to determine what works best with your unique content.
Traditional RAG implementations require extensive manual configuration, document preprocessing, and continuous tuning to remain effective. Many organizations struggle with selecting the right RAG modules and pipelines for their specific data, wasting valuable time and resources on suboptimal configurations. AutoRAG eliminates these barriers by automatically evaluating various RAG module combinations, handling document parsing, chunking optimization, retrieval strategy selection, and response generation—all while continuously learning and improving from evaluation metrics.
With AutoRAG, your enterprise AI applications gain instant access to your organization's proprietary information with unprecedented accuracy and relevance. The system automatically creates QA datasets from your corpus, evaluates multiple retrieval and generation strategies, and identifies the optimal pipeline configuration—significantly reducing hallucinations and providing fully-sourced responses that build trust with your users.
Key Benefits
AutoRAG
Automated Retrieval Augmented Generation that seamlessly connects your AI to your organization's knowledge with minimal setup and maximum accuracy.
Rapid Integration
Connect your knowledge base in minutes, not months, with automatic document processing and indexing.
Adaptive Retrieval
Our system automatically selects the optimal retrieval strategy for each query for maximum relevance.
Reduced Hallucinations
Reduces AI hallucinations by up to 97% with accurate context and real-time fact-checking.
Self-Improving Performance
Continuously optimizes retrieval patterns and response generation based on user interactions.
Multi-Format Support
Processes diverse content types including documents, databases, wikis, and structured data sources.
Feature Details
Smart Document Processing & Data Creation
AutoRAG's document processing pipeline transforms your raw content into optimized datasets through a comprehensive four-stage process: document parsing, intelligent chunking, corpus creation, and automated QA dataset generation. This end-to-end approach ensures both optimal knowledge extraction and accurate evaluation data for pipeline optimization.
AutoRAG's comprehensive data creation process transforms raw documents into optimized corpus and QA datasets
Key Capabilities of Our Document Processing Pipeline
Advanced Parsing Modules
Multiple parsing methods for different document types including PDFMiner, PyPDF, Unstructured, and custom parsers for specialized formats
Optimized Chunking Strategies
Multiple chunking methods (token-based, sentence-based, paragraph-based, semantic) with configurable chunk size and overlap parameters
QA Dataset Creation
Automatically generates high-quality question-answer pairs from your processed documents, creating evaluation datasets that enable accurate measurement of RAG pipeline performance
Metadata Enrichment
Automatically extracts and indexes document metadata for enhanced retrieval precision and ground truth generation
Corpus Creation & Optimization
Transforms chunked documents into an optimized corpus with metadata enrichment, deduplication, and indexing for efficient retrieval and evaluation
Multilingual Support
Seamlessly processes content in 95+ languages with consistent performance across parsing and chunking modules
Comprehensive Retrieval Evaluation
Our AutoRAG system automatically evaluates multiple retrieval strategies to find the optimal approach for your specific data and use case, measuring performance with comprehensive metrics to ensure the best possible results.
Comprehensive Retrieval Module Evaluation
Multiple Retrieval Methods
Evaluates various retrieval approaches including BM25, dense retrievers, hybrid search, and reranking strategies to find the optimal combination
Comprehensive Metrics
Measures performance using precision, recall, F1, MRR, NDCG, and other specialized metrics to ensure optimal retrieval quality
Pipeline Optimization
Automatically tests different combinations of retrieval modules to identify the most effective pipeline for your specific data
Vector Database Integration
Supports multiple vector databases and embedding models to find the optimal combination for your specific use case
Advanced Retrieval Capabilities
- Hybrid Search: Combines dense vector embeddings with sparse representations and keyword matching for comprehensive retrieval
- Query Analysis: Automatically decomposes complex queries into sub-queries to retrieve comprehensive information
- Cross-Document Connections: Identifies relationships between documents to provide comprehensive context
- Structured Data Integration: Seamlessly combines results from databases, APIs, and unstructured content
- Real-time Relevance Ranking: Dynamically evaluates and ranks retrieved content based on query relevance and information quality
Generation Optimization & Evaluation
AutoRAG's advanced optimization system evaluates multiple generation strategies and context handling approaches to find the optimal configuration for your specific data and use case, ensuring the highest quality AI responses.
Comprehensive Evaluation Metrics
Retrieval Metrics
Evaluates retrieval performance using precision, recall, F1, MRR, NDCG, and hit rate to ensure optimal document selection
Generation Metrics
Measures response quality using ROUGE, BLEU, BERTScore, and other semantic similarity metrics to ensure accurate and relevant answers
LLM-based Evaluation
Uses advanced LLM-based evaluation to assess factual accuracy, coherence, and relevance of generated responses
Performance Benchmarking
Automatically benchmarks different RAG pipeline configurations to identify the optimal setup for your specific use case
Advanced Context Optimization
- Content Distillation: Intelligently summarizes lengthy content to extract key information while preserving context
- Context Window Management: Optimizes token usage based on LLM capabilities and query complexity
- Information Hierarchy: Structures retrieved information based on relevance and importance
- Citation Generation: Automatically tracks and attributes information sources for transparent, verifiable responses
- Feedback-Based Learning: Continuously improves context selection based on response quality and user feedback
Implementation
Knowledge Source Connection
Connect your existing knowledge repositories through our simple integration interface. AutoRAG supports direct connections to document storage systems, databases, knowledge bases, wikis, and internal tools via secure API connections or direct document uploads.
Data Creation & Pipeline Optimization
Our system transforms your raw documents into optimized datasets through our comprehensive four-stage process: document parsing, intelligent chunking, corpus creation, and QA dataset generation. These datasets are then used to evaluate multiple RAG pipeline configurations, automatically identifying the optimal approach for your specific data and use case.
API Integration & Deployment
Integrate AutoRAG with your existing applications through our REST API or use our pre-built connectors for popular LLM platforms. Simple configuration options let you customize retrieval settings, authentication, and user permission models to match your organizational requirements.
Success Stories
Global Financial Services Firm
87% reduction in AI hallucinations while handling 15,000+ client queries daily
A leading financial services firm needed to incorporate 200,000+ regulatory documents and internal policies into their client-facing AI assistant. Manual RAG implementation was estimated at 8+ months. Using AutoRAG, they completed the integration in 3 weeks and achieved unprecedented accuracy for regulatory compliance questions.
Request Case Study →"AutoRAG transformed our AI implementation timeline from quarters to weeks. The system's ability to accurately retrieve regulatory information while providing proper citations has been game-changing for our compliance team."
— Sarah Chen, CTO, Financial Services Leader
Healthcare Provider Network
Integrated 50+ disparate knowledge bases in 2 weeks, enabling accurate medical information retrieval with 99.8% accuracy.
Request Details →Manufacturing Conglomerate
Reduced technical support resolution time by 73% by connecting AutoRAG to 15 years of equipment documentation and maintenance records.
Request Details →Global Legal Firm
Enabled paralegals to process 3x more case research by implementing AutoRAG across 12M+ legal documents and precedents.
Request Details →Frequently Asked Questions
AutoRAG can process virtually any text-based content including PDFs, Word documents, PowerPoint presentations, Excel spreadsheets, HTML pages, Markdown files, code repositories, databases, wikis, knowledge bases, and structured data from APIs. The system also handles images with text content through OCR and can extract data from tables, diagrams, and other visual elements.
For specialized data formats or proprietary systems, our team can develop custom connectors to ensure seamless integration with your existing knowledge infrastructure.
AutoRAG is designed with enterprise-grade security at its core. All data processing occurs within your security perimeter, either in your cloud environment or on-premises. The system supports:
- End-to-end encryption for all data at rest and in transit
- Role-based access controls for document visibility
- Data residency options for regional compliance requirements
- Audit logging for all system operations and data access
- Compliance with GDPR, HIPAA, SOC 2, and other regulatory frameworks
Additionally, our deployment options include air-gapped environments for the highest security requirements.
AutoRAG employs a comprehensive optimization process to find the best RAG pipeline for your specific data and use case:
- Data Preparation: The system processes your documents using multiple parsing methods and chunking strategies to find the optimal approach
- QA Dataset Creation: AutoRAG automatically generates high-quality question-answer pairs from your corpus to serve as evaluation data
- Module Evaluation: The system tests various combinations of retrieval methods, embedding models, and generation strategies
- Comprehensive Metrics: Performance is measured using multiple metrics for retrieval (precision, recall, F1) and generation (ROUGE, BLEU, semantic similarity)
- Pipeline Selection: Based on evaluation results, AutoRAG identifies the optimal pipeline configuration for your specific data
This optimization process is fully automated and can be run periodically as your data evolves. The system also employs continuous learning mechanisms to further improve performance over time:
- Usage Pattern Analysis: The system monitors which retrieved documents lead to successful responses and adjusts retrieval patterns accordingly
- Explicit Feedback Loops: Optional user feedback on response quality helps train the system
- Query-Result Pairing: The system builds an understanding of which document sections best answer specific question types
AutoRAG is model-agnostic and works with virtually any LLM, including:
- OpenAI models (GPT-4, GPT-3.5, etc.)
- Anthropic models (Claude series)
- Google models (Gemini series)
- Meta models (Llama series)
- Mistral models
- Open source models (deployable on your infrastructure)
- Custom fine-tuned models
The system automatically optimizes its output for each model's specific context window limitations and capabilities. Our management console allows easy switching between models and A/B testing for optimal performance.
AutoRAG's data creation process transforms your raw documents into optimized datasets through four key stages:
1. Document Parsing
Raw documents are processed using specialized parsers for each format (PDF, Word, HTML, etc.) to extract text while preserving structure, formatting, and metadata. Multiple parsing methods are evaluated to find the optimal approach for each document type.
2. Intelligent Chunking
Parsed documents are divided into chunks using various strategies (token-based, sentence-based, paragraph-based, semantic) with configurable parameters for chunk size and overlap. The system evaluates different chunking approaches to find what works best for your specific content.
3. Corpus Creation
Chunked documents are transformed into an optimized corpus with metadata enrichment, deduplication, and indexing. This corpus serves as the knowledge base for retrieval and provides the foundation for evaluation.
4. QA Dataset Generation
The system automatically generates high-quality question-answer pairs from your corpus, creating an evaluation dataset that enables accurate measurement of RAG pipeline performance. This includes establishing ground truth by identifying which document chunks should be retrieved for each question.
The resulting datasets are used for two key purposes:
- Corpus Dataset: Your organization's processed knowledge base that will be used for retrieval in the final RAG system.
- QA Evaluation Dataset: Question-answer pairs with ground truth annotations used to evaluate and optimize different RAG pipeline configurations.
This comprehensive data creation process ensures that both your knowledge base and evaluation data are optimally prepared for finding the best RAG pipeline for your specific use case.
AutoRAG's QA dataset generation is a sophisticated process that creates high-quality evaluation data from your corpus:
- Content Analysis: The system analyzes your processed documents to identify information-rich sections that contain factual content suitable for question generation
- Question Generation: Using advanced LLM techniques, the system generates diverse question types including factoid, descriptive, comparative, and reasoning questions based on the document content
- Answer Extraction: For each question, the system identifies the precise answer spans within the documents, ensuring accurate ground truth
- Ground Truth Mapping: The system establishes which specific document chunks should be retrieved for each question, creating a comprehensive evaluation reference
- Quality Filtering: Generated QA pairs undergo quality checks to ensure they're challenging, relevant, and representative of real-world queries
The resulting QA dataset typically includes:
- Hundreds to thousands of diverse question-answer pairs covering your corpus
- Ground truth annotations linking questions to relevant document chunks
- Metadata about question types, difficulty levels, and topic categories
- Coverage analysis to ensure comprehensive evaluation across your knowledge domain
This evaluation dataset is crucial for accurately measuring the performance of different RAG pipeline configurations and identifying the optimal approach for your specific data and use case.
Most organizations can implement AutoRAG and begin seeing results within 1-3 weeks, depending on the complexity and volume of knowledge sources. Our implementation timeline typically follows this schedule:
- Days 1-2: Initial setup and connection to first knowledge sources
- Days 3-7: Document processing, indexing, and initial configuration
- Days 8-14: Integration with applications, testing, and optimization
- Days 15+: Rollout, user training, and ongoing refinement
For organizations with extremely large document collections (millions of documents) or complex security requirements, implementation may take up to 4-6 weeks, but initial functionality is typically available much sooner with a phased approach.
Ready to Supercharge Your AI with AutoRAG?
Schedule a demo to see how AutoRAG can transform your enterprise AI with accurate, reliable knowledge integration.