raia Connect User Guide
Turn Your Company Knowledge Base into Living Intelligence
Overview
raia Connect serves as the intelligent knowledge management engine that transforms scattered documents, databases, and data sources into AI-ready intelligence that agents can access instantly and accurately. In most organizations, critical knowledge exists in hundreds or thousands of files—PDFs, Word documents, presentations, spreadsheets, web pages, and databases—each with its own format, structure, and quirks. This fragmented knowledge landscape creates significant challenges for AI agent training, often requiring manual reformatting, cleaning, and organization before content can be used effectively.
Connect eliminates these barriers by automating the entire knowledge preparation workflow. The platform accepts virtually any file type, intelligently extracts and structures content, converts it to AI-optimized formats, and pushes it directly to agent vector stores where it becomes immediately searchable and retrievable. This transformation process preserves semantic meaning, maintains document hierarchy, and optimizes chunking for accurate retrieval—all without requiring technical expertise or manual intervention. The result is a living knowledge base that powers accurate, context-aware AI responses while remaining synchronized with evolving business information.
How raia Connect Works
The Knowledge Transformation Pipeline
raia Connect implements a sophisticated multi-stage pipeline that converts raw documents into AI-ready knowledge. The process begins when users upload files through the intuitive drag-and-drop interface or bulk import from shared drives. Connect accepts an extensive range of formats including PDF, DOC/DOCX, PPT/PPTX, XLS/XLSX, TXT, HTML, JSON, Markdown, and web URLs. This format flexibility ensures that existing knowledge assets can be leveraged immediately without requiring conversion to specific file types.
Once uploaded, Connect's AI-powered parsing engine analyzes each file to detect its type and structure. The parser extracts both unstructured text—such as narratives, descriptions, and explanatory content—and structured data like tables, spreadsheet fields, and hierarchical information. This intelligent extraction goes beyond simple text recognition, understanding document semantics including headings, subheadings, lists, callouts, and relationships between sections. The parser identifies and preserves important contextual elements that inform meaning, such as which heading a paragraph belongs to or how table data relates to surrounding text.
Normalization and AI-Ready Conversion
After extraction, Connect's normalization engine cleans and structures the content to optimize it for AI consumption. This critical step removes formatting artifacts, layout noise, headers, footers, and other elements that don't contribute to meaning but can confuse AI models. The engine preserves document hierarchy using Markdown for text-heavy documents or JSON for structured datasets, ensuring that the semantic organization of information remains intact.
The normalization process applies semantic chunking, which divides content at logical boundaries rather than arbitrary character or word counts. This intelligent chunking ensures that each knowledge segment is coherent and self-contained, improving retrieval accuracy and response quality. For example, a section about a product feature would be kept together rather than split mid-sentence, and related information like feature descriptions and their benefits would be chunked as a unit. The system also adds metadata tags that enhance searchability and enable filtering, such as document source, creation date, topic categories, and custom labels.
Vector Store Integration and Synchronization
The final stage of the pipeline pushes converted, structured content directly to agent vector stores where it becomes immediately available for retrieval. Vector stores are specialized databases that store information as mathematical embeddings—numerical representations that capture semantic meaning. When an agent receives a question, it searches the vector store for content with similar semantic meaning, retrieving the most relevant information to inform its response.
Connect manages this entire embedding and indexing process automatically, handling the technical complexity behind a simple "push to agent" interface. Users select which agent should receive the knowledge, and Connect handles embedding generation, vector storage, and index optimization. The platform supports versioning, allowing multiple iterations of knowledge to coexist and enabling rollback if needed. Batch upload capabilities enable efficient processing of large knowledge bases, while incremental updates allow adding new information without reprocessing existing content.
Real-Time Integration and Synchronization
Beyond static document processing, Connect supports real-time integration with external data sources through MCP (Model Context Protocol) and API connections. This capability enables agents to access current information from third-party applications, databases, and services without requiring manual document updates. For example, an agent could retrieve real-time inventory levels from an ERP system, current pricing from a pricing engine, or customer account status from a CRM.
These integrations are configured through Connect's integration interface, which handles authentication, endpoint specification, request formatting, and response parsing. The platform supports various authentication methods including API keys, OAuth, and custom headers, ensuring compatibility with diverse systems. Real-time integrations complement the document-based knowledge in vector stores, giving agents access to both curated reference information and current operational data. This hybrid approach ensures agents have comprehensive knowledge while maintaining accuracy with time-sensitive information.
Key Benefits
Elimination of Manual Knowledge Preparation
Traditional AI agent training requires extensive manual effort to prepare knowledge materials. Documents must be converted to compatible formats, cleaned of formatting artifacts, restructured for optimal chunking, and manually uploaded to training systems. This process is time-consuming, error-prone, and requires technical expertise that many organizations lack. A typical enterprise knowledge base of several hundred documents might require weeks or months of manual preparation before it can be used for agent training.
raia Connect eliminates this bottleneck entirely through intelligent automation. The platform handles format conversion, cleaning, structuring, and optimization automatically, reducing preparation time from weeks to minutes. Non-technical users can upload raw documents directly from their file systems or cloud storage, and Connect transforms them into AI-ready format without intervention. This dramatic time savings accelerates agent deployment, enables rapid iteration on training materials, and removes the technical barrier that often prevents organizations from fully leveraging their knowledge assets.
Improved AI Agent Accuracy and Reliability
The quality of knowledge preparation directly impacts AI agent accuracy and reliability. Poorly structured content, inconsistent formatting, inadequate chunking, and missing context all contribute to retrieval failures, incomplete answers, and hallucinations where agents generate plausible but incorrect information. These accuracy issues undermine user trust and limit the practical utility of AI agents in business-critical applications.
Connect's intelligent processing pipeline addresses these quality challenges systematically. Semantic chunking ensures that retrieved information is complete and contextually coherent, reducing incomplete or fragmented responses. Preservation of document hierarchy maintains the relationships between concepts, enabling agents to understand context and provide more nuanced answers. Metadata enrichment improves retrieval precision, helping agents find the most relevant information for each query. The result is measurably improved accuracy, with organizations reporting 70-90% reductions in retrieval errors and hallucinations after implementing Connect for knowledge management.
Scalability and Knowledge Governance
As organizations scale their AI agent deployments, knowledge management becomes increasingly complex. Multiple agents may need access to overlapping but distinct knowledge sets, requiring careful organization and access control. Knowledge must be kept current as products, policies, and procedures evolve, necessitating efficient update mechanisms. Different teams may contribute knowledge, requiring governance to ensure quality and consistency. Managing these requirements manually becomes impractical at scale.
Connect provides the infrastructure for scalable knowledge governance through its organizational features. Training packs enable modular knowledge organization, where related documents are grouped into reusable units that can be assigned to multiple agents. This approach reduces duplication, ensures consistency, and simplifies updates—changes to a pack automatically propagate to all agents using it. Role-based access control ensures that only authorized personnel can upload, modify, or deploy knowledge, maintaining quality and security. Version control tracks all changes with complete audit trails, supporting compliance requirements and enabling rollback if issues arise.
Continuous Knowledge Evolution
Business knowledge is not static—products change, policies update, new procedures emerge, and customer questions reveal gaps in existing documentation. Traditional knowledge management approaches struggle with this continuous evolution, often resulting in outdated information that undermines agent effectiveness. Keeping knowledge current requires ongoing effort that competes with other priorities, leading to knowledge drift where agents become progressively less accurate over time.
Connect's synchronization capabilities enable continuous knowledge evolution without the traditional overhead. When source documents are updated in cloud storage systems like Google Drive, Notion, or SharePoint, Connect can automatically detect changes and re-process affected content. The re-indexing process updates agent vector stores with current information, ensuring responses reflect the latest knowledge. This automation transforms knowledge management from a periodic, labor-intensive project into a continuous, largely automated process. Organizations report that automated synchronization reduces knowledge maintenance effort by 60-80% while improving currency and accuracy.
Key Features
Universal File Format Support
Connect's comprehensive format support ensures that existing knowledge assets can be leveraged immediately without conversion or reformatting. The platform handles document formats including PDF (with OCR for scanned documents), DOC/DOCX, TXT, and RTF. Presentation formats like PPT/PPTX are processed with preservation of slide structure and speaker notes. Spreadsheet formats including XLS/XLSX and CSV are parsed with recognition of headers, data types, and relationships between sheets.
Web content can be ingested through URL import, where Connect crawls pages, extracts content, and structures it appropriately. HTML files are processed with intelligent extraction that distinguishes content from navigation, advertisements, and other non-essential elements. Structured data formats like JSON and existing Markdown files are accepted directly, with validation and optimization applied. This universal compatibility eliminates format as a barrier to knowledge utilization, enabling organizations to leverage their entire knowledge base regardless of how it was originally created.
AI-Powered Document Intelligence
Connect's document intelligence capabilities go far beyond simple text extraction, applying AI to understand document structure, semantics, and meaning. The platform recognizes document elements including titles, headings, subheadings, body text, lists, tables, callouts, footnotes, and references. This structural understanding enables preservation of hierarchy and relationships that inform meaning—for example, understanding that certain paragraphs explain a heading or that table data relates to surrounding context.
Semantic analysis identifies key concepts, entities, and relationships within content, enabling intelligent chunking and metadata generation. The system recognizes when content forms a coherent unit that should be kept together versus when natural boundaries exist for division. Table processing extracts structured data while maintaining column headers and row relationships, ensuring that tabular information remains interpretable. Image analysis can extract text from diagrams, charts, and infographics, capturing information that would otherwise be lost. This comprehensive intelligence ensures that the full meaning and context of source documents is preserved through the transformation process.
Semantic Chunking and Optimization
The chunking strategy used to divide documents into retrievable segments has profound impact on agent accuracy and response quality. Simple chunking approaches that divide text at fixed character or word counts often split coherent information mid-concept, resulting in incomplete or confusing retrieval. Connect implements semantic chunking that divides content at natural boundaries based on meaning and structure, ensuring each chunk is self-contained and contextually complete.
The chunking algorithm considers multiple factors including document structure (respecting heading and section boundaries), semantic coherence (keeping related concepts together), optimal size for embedding models (balancing specificity and context), and overlap strategy (including relevant context from adjacent chunks). The system applies different chunking strategies based on content type—narrative text is chunked differently than tables, lists, or technical specifications. Chunk metadata includes information about position in document hierarchy, related chunks, and source context, enabling more sophisticated retrieval strategies.
Training Pack Organization
Training packs provide a powerful organizational abstraction that simplifies knowledge management at scale. A pack is a curated collection of related documents, instructions, and style guidelines that can be assigned to one or multiple agents. Packs enable modular knowledge organization where content is grouped by topic, product, department, or any other logical categorization. This modularity reduces duplication—a pack about company policies can be assigned to multiple agents without copying content—and simplifies updates, where changes to a pack automatically propagate to all agents using it.
Packs support hierarchical organization where broader packs can include more specific sub-packs, enabling flexible knowledge structures that match organizational needs. Access control can be applied at the pack level, ensuring that sensitive information is only available to appropriate agents. Version control tracks pack evolution, enabling rollback and comparison between versions. Pack analytics show which content is being retrieved most frequently, informing optimization decisions and identifying gaps. The pack abstraction transforms knowledge management from a per-agent task into a centralized, reusable asset management process.
Cloud Storage Integration
Connect's integration with popular cloud storage platforms enables seamless knowledge ingestion from existing repositories. Google Drive integration allows importing documents directly from Drive folders, with support for Docs, Sheets, Slides, and PDFs. Notion integration enables importing pages and databases, preserving structure and relationships. SharePoint integration provides access to enterprise document libraries with respect for permissions and access controls.
These integrations support both one-time import and continuous synchronization. In synchronization mode, Connect monitors specified folders or spaces for changes, automatically detecting new files, updated content, or deletions. When changes are detected, affected content is re-processed and agent vector stores are updated, ensuring knowledge remains current without manual intervention. Synchronization can be configured with various schedules (real-time, hourly, daily) and filters (file types, naming patterns, modification dates), providing flexibility to match organizational needs and priorities.
Version Control and Audit Trails
Connect maintains comprehensive version history for all knowledge assets, supporting governance, compliance, and troubleshooting requirements. Every upload, modification, or deletion is tracked with timestamps, user attribution, and change descriptions. Users can view complete history for any document or pack, understanding how knowledge has evolved over time. Comparison tools highlight differences between versions, facilitating review and validation of changes.
Rollback functionality enables instant reversion to previous versions if issues are discovered, minimizing the impact of errors or unintended changes. Version tagging allows marking stable configurations for reference and reuse. The audit trail supports compliance requirements by providing complete documentation of who accessed or modified knowledge, when changes occurred, and what was changed. Export capabilities allow audit data to be extracted for analysis or integration with compliance management systems. This comprehensive versioning and auditing infrastructure provides the governance foundation necessary for enterprise knowledge management.
Real-Time Data Integration via MCP and APIs
Beyond static document processing, Connect supports integration with live data sources through Model Context Protocol (MCP) and REST API connections. MCP is an emerging standard for connecting AI systems to external data sources, tools, and services in a standardized way. Connect's MCP support enables agents to access real-time information from compatible systems without custom integration work. API integration provides connectivity to any system with a REST API, supporting various authentication methods and data formats.
These real-time integrations enable agents to access current operational data that changes too frequently for document-based knowledge management. Examples include inventory levels, pricing information, account balances, order status, appointment availability, and system status. Integrations are configured through Connect's interface with specification of endpoints, authentication, request parameters, and response parsing. The platform handles caching, rate limiting, and error handling, ensuring reliable access to external data. Real-time integration transforms agents from systems that know about your business to systems that know your business in real-time.
Getting Started with raia Connect
Organizations beginning with raia Connect typically start by identifying their most important knowledge assets—the documents, databases, and data sources that agents need to provide accurate, helpful responses. Common starting points include product documentation, policy manuals, FAQ databases, procedure guides, and customer support knowledge bases. The focused approach allows teams to achieve quick wins while learning the platform and establishing best practices.
The typical knowledge preparation workflow begins with gathering source materials in their existing formats—no pre-processing or conversion required. Users upload files to Connect through the web interface or bulk import from cloud storage. Connect processes the files automatically, with progress tracking and error reporting for any issues. Once processing completes, users review the converted content to ensure quality, making any necessary adjustments to chunking, metadata, or organization. Finally, content is pushed to agent vector stores where it becomes immediately available for retrieval.
Success with Connect comes from treating knowledge management as an ongoing process rather than a one-time project. Organizations that achieve the greatest value establish regular review cycles where they analyze which content is being retrieved most frequently, identify gaps where agents lack information, and continuously expand knowledge based on new questions and scenarios. The platform's automation and synchronization capabilities make this continuous improvement sustainable, enabling knowledge to evolve with the business without requiring proportional increases in management effort.
Conclusion
raia Connect represents a fundamental advancement in how organizations prepare and manage knowledge for AI agents. By automating the complex, technical process of converting diverse documents into AI-ready format, Connect eliminates the primary barrier that prevents organizations from fully leveraging their knowledge assets. The platform's intelligent processing, semantic optimization, and seamless integration with agent vector stores ensure that knowledge is not just accessible but optimally structured for accurate, contextual retrieval.
Organizations using raia Connect report dramatic improvements in agent accuracy, significant reductions in knowledge preparation time, and enhanced ability to keep agent knowledge current as business information evolves. The platform's combination of automation, intelligence, and governance creates a sustainable approach to knowledge management that scales with organizational needs. As AI agents become increasingly central to business operations, Connect provides the knowledge infrastructure necessary to ensure those agents are informed, accurate, and continuously improving.
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