Retrieval-Augmented Generation that runs on your infrastructure. Query thousands of documents in natural language, with answers grounded in your actual data.
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European enterprises sit on vast document archives, but extracting value from them is painfully slow and increasingly risky with public AI tools.
Critical information buried across SharePoint, file servers, email, and Confluence. Employees spend 20% of their workweek searching for information they need.
Keyword search fails with complex queries. Teams miss connections across documents because traditional search cannot understand context or meaning.
Uploading confidential documents to ChatGPT or other public AI tools violates GDPR and exposes trade secrets. 65% of employees admit to using unauthorized AI tools at work.
Ironum deploys a complete Retrieval-Augmented Generation pipeline on infrastructure you control. Your employees query your entire document base in natural language and get accurate, source-cited answers, without any data leaving your environment.
Documents are chunked, embedded, and stored in a vector database on your infrastructure.
User queries are matched against your document embeddings using semantic similarity.
The LLM synthesizes an answer from retrieved passages, citing sources.
Users click through to original documents to verify and build trust.
Deploy on your own infrastructure: Azure EU, Hetzner, or on-premises. Your documents never leave your control. Zero data shared with model providers.
Ingest PDFs, Word documents, Excel spreadsheets, emails, Confluence pages, SharePoint files, and more. Automatic OCR for scanned documents.
Automatic document indexing when files change. Connect to SharePoint, Confluence, Google Drive, or internal file systems for always up-to-date answers.
Enforce existing permission structures. Users only see answers from documents they are authorized to access. Integrates with Active Directory and SSO.
Run Llama, Mistral, or other open-source models. No per-token fees, no data sent to third parties. Full control over model selection and updates.
Fine-tune embedding and generation models on your domain vocabulary. Improve accuracy for industry-specific terminology and document structures.
faster document review
A mid-size German law firm processes thousands of contracts monthly. With Ironum RAG, attorneys query the entire contract database in natural language, finding relevant clauses, identifying risks, and comparing terms across agreements in seconds instead of hours.
reduction in internal tickets
A European manufacturer with 2,000+ employees deploys RAG across their technical documentation, HR policies, and training materials. New employees get instant, accurate answers. Support tickets drop as teams self-serve from a unified knowledge layer.
faster compliance checks
A financial services firm uses RAG to monitor regulatory documents and cross-reference internal policies. Compliance officers get instant alerts when regulations change and can verify policy alignment across hundreds of documents automatically.
Contract review, compliance analysis, and legal research across thousands of privileged documents.
Learn moreSearchable technical documentation, maintenance logs, and quality standards for production teams.
Learn moreClinical protocol search and institutional knowledge management with patient data sovereignty.
Learn moreRegulatory document analysis, DIN standards search, and compliance archive retrieval for banking.
Learn moreRetrieval-Augmented Generation (RAG) connects an LLM to your document base at query time. Instead of training the model on your data (fine-tuning), RAG retrieves relevant passages and feeds them to the model as context. This means your answers are always grounded in current documents, hallucinations are drastically reduced, and you can update knowledge instantly without retraining. RAG is the right choice when you need accurate, source-cited answers from a changing document base.
Your documents are processed and stored exclusively on infrastructure you control: either your own servers, Azure EU regions, or German-hosted Hetzner infrastructure. We never send your data to third-party model providers. When using open-source LLMs, the entire pipeline runs on your hardware. All data processing is covered under a standard GDPR Data Processing Agreement (DPA).
A production-ready RAG system typically takes 4 to 8 weeks from kickoff to launch. This includes document ingestion pipeline setup, embedding model configuration, retrieval tuning, UI deployment, and user acceptance testing. A working proof-of-concept with your actual documents can be ready in as little as 2 weeks.
We support PDF, DOCX, XLSX, PPTX, HTML, Markdown, plain text, emails (EML, MSG), and structured data formats (CSV, JSON). We also handle scanned documents via OCR. Connectors are available for SharePoint, Confluence, Google Drive, Notion, and custom APIs.
Pricing depends on deployment model, document volume, and the LLM provider you choose. On-premises deployments with open-source models have no per-query costs. You only pay for infrastructure and our setup and maintenance services. Cloud API deployments include pass-through token costs. We provide transparent, itemized quotes after an initial scoping call.
Free 30-minute strategy call with Gerrit: no sales pitch, just a concrete roadmap for your business.