Private RAG over your enterprise knowledge base. Employees ask, AI answers with your documentation. No data leak to public models.<\/p>\n","protected":false},"author":0,"featured_media":69807,"parent":69531,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"page-templates\/takeover.php","meta":{"_acf_changed":false,"_yoast_wpseo_focuskw":"enterprise private RAG","_yoast_wpseo_title":"Enterprise Private RAG | Knowledge Base AI | Cronuts","_yoast_wpseo_metadesc":"Private RAG over your enterprise knowledge base. Employees ask, AI answers with your docs. No data leak. Deployment in 45 days.","footnotes":"","schema_jsonld":"{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Service\",\"@id\":\"https:\/\/cronuts.digital\/en\/enterprise-private-rag\/#service\",\"name\":\"Enterprise Private RAG\",\"serviceType\":\"Secure private RAG system deployment over enterprise knowledge base\",\"description\":\"Design, training and deployment of a private RAG (Retrieval-Augmented Generation) system over your enterprise knowledge base. Vector indexing of Confluence, SharePoint, Drive, Notion and data warehouse, contextual retrieval with LLM, per-user permission control and zero data leak to public models. Deployment in 45 days on client VPC or Cronuts cloud.\",\"inLanguage\":\"en\",\"provider\":{\"@id\":\"https:\/\/cronuts.digital\/#organization\"},\"areaServed\":[{\"@type\":\"Country\",\"name\":\"United States\"},{\"@type\":\"Country\",\"name\":\"United Kingdom\"},{\"@type\":\"Country\",\"name\":\"Spain\"},{\"@type\":\"Country\",\"name\":\"Germany\"}],\"audience\":{\"@type\":\"BusinessAudience\",\"audienceType\":\"Mid-market and enterprise companies with 500+ internal docs and privacy constraints\"},\"offers\":{\"@type\":\"Offer\",\"price\":\"5500\",\"priceCurrency\":\"EUR\",\"url\":\"https:\/\/cronuts.digital\/en\/enterprise-private-rag\/\",\"availability\":\"https:\/\/schema.org\/InStock\",\"priceSpecification\":{\"@type\":\"UnitPriceSpecification\",\"price\":\"5500\",\"priceCurrency\":\"EUR\",\"unitCode\":\"MON\",\"name\":\"Monthly subscription\"}}},{\"@type\":\"SoftwareApplication\",\"@id\":\"https:\/\/cronuts.digital\/en\/enterprise-private-rag\/#software\",\"name\":\"Cronuts Enterprise Private RAG\",\"applicationCategory\":\"BusinessApplication\",\"applicationSubCategory\":\"KnowledgeManagement\",\"operatingSystem\":\"Web, Cloud, On-premise, VPC\",\"url\":\"https:\/\/cronuts.digital\/en\/enterprise-private-rag\/\",\"inLanguage\":\"en\",\"author\":{\"@id\":\"https:\/\/cronuts.digital\/#organization\"},\"offers\":{\"@type\":\"Offer\",\"price\":\"5500\",\"priceCurrency\":\"EUR\",\"availability\":\"https:\/\/schema.org\/InStock\"},\"featureList\":[\"Vector indexing of Confluence, SharePoint, Drive, Notion, GitHub, S3\",\"Hybrid retrieval (vector + keyword + reranking)\",\"Per-user and per-group permission control (native ACL)\",\"Self-hosted LLMs (Llama 3.3, Mistral) or cloud with DPA\",\"Mandatory inline citation: every answer includes source and paragraph\",\"Full audit trail of queries and results\",\"Multi-language (ES, EN, CA, PT, FR, DE) + multi-modal (text, images, tables)\"],\"aggregateRating\":{\"@type\":\"AggregateRating\",\"ratingValue\":\"4.9\",\"reviewCount\":\"34\",\"bestRating\":\"5\",\"worstRating\":\"1\"}},{\"@type\":\"FAQPage\",\"@id\":\"https:\/\/cronuts.digital\/en\/enterprise-private-rag\/#faq\",\"inLanguage\":\"en\",\"mainEntity\":[{\"@type\":\"Question\",\"name\":\"What is enterprise private RAG and how does it differ from ChatGPT?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"
Private RAG (Retrieval-Augmented Generation)<\/strong> is a system that combines vector retrieval over your internal documentation with an LLM to answer questions using your own sources. Unlike public ChatGPT: (1) data never leaves your infrastructure, (2) answers cite verifiable internal docs and (3) it respects each user\u2019s permissions on each document.<\/p>\"}},{\"@type\":\"Question\",\"name\":\"Why not just use ChatGPT Enterprise?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\" ChatGPT Enterprise does not automatically index your full enterprise knowledge base with granular permissions or cite specific internal sources. Private RAG<\/strong> guarantees: live indexing of your Confluence\/SharePoint\/Drive, native ACL control, mandatory inline citation, full audit trail and the option of self-hosted LLM for sectors with strict no-egress data policies.<\/p>\"}},{\"@type\":\"Question\",\"name\":\"How long does deployment take?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\" Full deployment takes 45 days<\/strong>. It includes discovery with IT and compliance (1 wk), source connection and ACL mapping (2 wks), initial indexing and retriever tuning (1 wk), validation with real cases and prompt engineering (1 wk) and progressive go-live. Subsequent incremental indexing is continuous.<\/p>\"}},{\"@type\":\"Question\",\"name\":\"Which documentation sources can it index?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\" Native connectors for Confluence, SharePoint, Google Drive, Notion, GitHub, GitLab, Bitbucket, Jira, Zendesk, Salesforce Knowledge, S3 and data warehouses (Snowflake, BigQuery, Redshift)<\/strong>. Support for PDF, Word, Excel, PowerPoint, Markdown, HTML, OCR\u2019d images and structured tables. Custom API connectors for vertical sources.<\/p>\"}},{\"@type\":\"Question\",\"name\":\"How are per-user permissions respected?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\" The system implements native per-document ACL<\/strong>: indexing preserves source permissions (Confluence space, SharePoint folder, Drive file). Each query filters results based on the SSO-authenticated user (Azure AD, Okta, Google Workspace). The user only sees answers from documents they already had access to.<\/p>\"}},{\"@type\":\"Question\",\"name\":\"Which LLMs can be used and how is privacy guaranteed?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\" Three options: (1) self-hosted LLMs<\/strong> (Llama 3.3 70B, Mistral, Qwen) in client VPC with no data egress, (2) cloud LLM with DPA and zero-data-retention<\/strong> (OpenAI Enterprise, Anthropic, Azure OpenAI) and (3) standard cloud LLM<\/strong> with data minimisation (PII redacted pre-send). Default recommendation for regulated sectors: option 1.<\/p>\"}},{\"@type\":\"Question\",\"name\":\"How do you prevent the system from inventing answers (hallucinating)?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\" Four layers: (1) mandatory inline citation<\/strong>: every answer must cite source and paragraph, (2) \u00abI don\u2019t know\u00bb guardrail<\/strong>: if confidence is low, the model declares no answer was found, (3) semantic reranking<\/strong> before passing context to the LLM and (4) feedback loop<\/strong>: users flag inaccurate answers and prompts\/indexing are tuned weekly.<\/p>\"}},{\"@type\":\"Question\",\"name\":\"Is it compliant with GDPR, AI Act and sector-specific regulations (health, finance)?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\" Yes. GDPR, AI Act, ENS and sector certifications<\/strong> (HDS for health, ISO 27001, SOC 2). Deployment in client VPC or European cloud with DPA. AES-256 at-rest and TLS 1.3 in-transit encryption. SIEM-compatible audit trail. Right to be forgotten implemented at document and user level.<\/p>\"}}]}]}"},"class_list":["post-69812","page","type-page","status-publish","has-post-thumbnail","hentry"],"acf":[],"yoast_head":"\n