LLM & RAG for Secure On-Premise File Management: How to Keep Total Control and Still Use GenAI

With the rapid rise of LLMs and RAG-powered automation, many organizations are eager to bring GenAI into their document ecosystems. Yet the biggest barrier remains unchanged: security. Highly regulated industries, privacy-driven companies, and enterprises handling sensitive data cannot risk sending documents to external clouds or exposing confidential information to third-party AI providers. For this reason, more and more companies are choosing on-premise GenAI deployments solutions that deliver the intelligence of LLMs and the precision of RAG while keeping every file, every vector, and every action fully contained within their own infrastructure.

This article explains how elDoc makes this possible: how you can run advanced LLMs, orchestrate RAG pipelines, and achieve enterprise-grade document intelligence without ever letting your data leave your controlled environment. No exposure. No vendor access. No cloud dependency. Just full GenAI power with 100% control.

Can You Really Run High-Performance LLM + RAG Fully On-Premise?

Yes — absolutely. But only when you understand the architectural constraints and solve a set of critical technical challenges that most traditional systems are not built for.

Running GenAI on-premise requires addressing the following considerations:

• Model Computation Load: LLMs and VLMs demand significant CPU/GPU resources, memory optimization, and efficient quantization strategies.
• Vector Search Performance: RAG needs a high-performance vector engine (like Qdrant) optimized for local storage, fast retrieval, and continuous indexing.
• Metadata & Keyword Search: Traditional file systems are not enough — you need a robust local search layer such as Apache Solr to combine semantic and keyword retrieval.
• OCR & Vision Processing: On-premise OCR and layout-aware vision models must be integrated without relying on cloud engines.
• Pipeline Orchestration: LLMs, OCR, embeddings, retrieval, and validation must work together seamlessly without external dependencies.
• Security & Access Control: The entire workflow must operate inside your perimeter with RBAC, encryption, audit logs, and no external data flow.

When these challenges are addressed holistically — not piecemeal — you get true on-premise GenAI with the same intelligence as cloud LLMs but with 100% data control, zero exposure, and full compliance.

How elDoc Solves It: A Dive Into elDoc’s On-Premise GenAI Framework

Achieving high-performance GenAI entirely on-premise requires more than simply “installing an LLM locally.” It demands a tightly integrated, fully optimized architecture where every component — models, search engines, OCR, vector storage, orchestration, and security — runs inside the organization’s own environment. This is exactly what elDoc delivers: an end-to-end, self-contained GenAI pipeline engineered for private infrastructure without compromising speed, accuracy, or intelligence. Below is how each layer works.

Local LLMs (No External Calls, No Cloud Exposure)

elDoc deploys LLMs and VLMs directly inside your perimeter, ensuring that all language processing, visual reasoning, indexing, and document understanding happens fully on-premise — without sending a single token outside your infrastructure. But unlike closed, fixed-model platforms, elDoc gives you full freedom to choose the LLMs you want.

Use Any Local / Open-Source LLM

You can run any open-source or self-hosted model, including:

• Small-footprint LLMs for CPU-only environments
• Medium-sized models optimized for speed and cost-efficiency
• Large-scale LLMs for GPU clusters and high-volume workloads
• Domain-tuned models (legal, finance, medical)
• Vision-Language Models for documents with mixed visual + textual data

Examples (not limited to):

• Llama family (Llama 3.x, Llama 2)
• Mistral & Mixtral
• Any LLM you choose to self-host

elDoc is model-agnostic and infrastructure-flexible — you control the model, the version, the updates, and the hardware.

Key technical capabilities:

• Local model hosting using optimized, quantized LLMs (Q4, Q8, GGUF, TensorRT, or GPU-native models depending on hardware).
• Hybrid CPU/GPU execution, enabling both high-performance inference and cost-efficient scaling.
• No external inference calls — elDoc does not rely on OpenAI, Anthropic, Azure, or any external LLM providers.
• Document-optimized models, fine-tuned for extraction, summarization, classification, layout reasoning, and multi-page context.
• VLM (Vision-Language Models) support for reading structured/unstructured PDFs, scanned documents, handwriting, and layout-heavy content.

🎯 Result: You get the full power of LLM and VLM document intelligence running entirely within your own infrastructure — with zero external data exposure, full model control, predictable performance, and complete data sovereignty.

Embedded RAG Pipeline Inside Your Perimeter

RAG is not just embeddings. It requires a tightly orchestrated set of components. elDoc ships with a fully on-premise RAG stack, including:

Local Embedding Generation

• Embedding models run entirely inside your infrastructure.
• Supports multi-modal embeddings for text, tables, images, and diagrams.
• Efficient batching + GPU acceleration for large-scale.

Local Vector Storage (Qdrant)

• Qdrant deployed as a local service.
• High-performance ANN search (HNSW) optimized for millions of documents.
• No cloud vector DBs (Pinecone, Weaviate Cloud, Chroma Cloud).

Local Metadata Indexing (Solr / OpenSearch)

• Full-text indexing for keyword/Boolean search.
• Metadata extraction for hybrid search (keyword + semantic).
• Distributed indexing and replication for large enterprises.

Fully Self-Contained Retrieval

• All retrieval, ranking, and context-building happens internally.
• Local RAG controller optimizes chunking, context assembly, and re-ranking.

🎯 Result: A fully self-contained RAG pipeline running entirely behind your firewall — delivering high-performance retrieval, precise document understanding, and zero reliance on any external infrastructure or cloud services.

Local OCR + Vision Models (No Cloud OCR Vendors)

OCR is often the weakest link in on-premise AI automation because many “on-premise” vendors quietly rely on cloud-based services like Google Vision, Amazon Textract, or Azure OCR for accuracy. elDoc avoids all external dependencies by providing fully integrated, on-premise OCR and document vision models that run entirely within your infrastructure.

OCR Engines Supported by elDoc

elDoc ships with multiple industry-leading, local OCR engines, allowing you to choose based on performance, language coverage, or hardware:

• PaddleOCR – high accuracy, multilingual, GPU-accelerated
• Tesseract OCR – lightweight, fast, CPU-friendly
• Qwen-VL / Qwen-VL-OCR capabilities – advanced OCR-like reasoning via VLMs
• Custom OCR pipelines – pluggable architecture for proprietary engines

These engines ensure strong coverage across:

• Latin languages
• CJK languages
• Cyrillic

Supported Document Types

Whether your files are clean or messy scans, elDoc’s local OCR and vision stack handles them all, including:

• Scanned or native PDFs
• Large multi-page TIFF files
• Images in JPG/PNG formats

Computer Vision Layer (Structural Understanding Beyond OCR)

elDoc’s Computer Vision layer goes far beyond simple text extraction. It understands the structure and visual logic of documents entirely on-premise, enabling reliable processing even when files are messy, scanned, rotated, or visually complex. Before deeper analysis, elDoc performs image preprocessing and normalization to enhance and clean the document. This includes automatic rotation correction and deskewing, orientation detection for sideways or upside-down pages, background cleaning to remove noise or shadows, contrast enhancement for faint text, denoising for low-quality scans or faxed pages, and edge/boundary normalization for more accurate segmentation. These steps dramatically increase recognition accuracy and improve downstream LLM and OCR performance.

🎯 Result: A fully on-premise visual understanding layer that normalizes, enhances, and interprets document images — identifying structure, tables, regions, and visual elements far beyond what traditional OCR can achieve.

MongoDB as the High-Performance Document & Metadata Store

At the core of elDoc’s on-premise architecture is MongoDB, which serves as the backbone for storing documents, metadata, processing states, and all AI-derived insights. Its flexible schema and natural scalability make it exceptionally well-suited for GenAI document workloads, where formats, structures, and processing requirements vary widely.

MongoDB’s schema flexibility allows elDoc to handle unstructured and semi-structured documents without the rigidity of traditional relational databases. Invoices, contracts, emails, scanned PDFs, images, and multi-page TIFFs all come in different shapes and layouts, and MongoDB accommodates this variability without the need for complex schema migrations. Large files are stored efficiently using GridFS, enabling high-throughput storage and fast retrieval of PDFs, images, and other binary assets.

Beyond raw documents, MongoDB excels at managing the high-volume metadata generated by GenAI pipelines. It supports rapid querying of OCR outputs, classification labels, workflow states, RAG metadata, page-level annotations, processing logs, and complete audit trails. This makes it ideal for real-time search, indexing, and workflow automation at scale. With built-in sharding and replication, MongoDB can comfortably support repositories containing millions of documents.

Security by Architecture (Not Just Features)

In on-premise environments, organizations require absolute control over data access, system behavior, and GenAI interactions. elDoc is built around this principle. Every component — LLMs, RAG, OCR, CV, vector search, and orchestration — operates fully inside your infrastructure with no external services involved.

Access governance is enforced through a robust combination of RBAC, giving administrators precise control over who can view, edit, share, process, or approve documents. Permissions can be defined at the level of departments, roles, workflows, sensitivity categories, or even individual files. MFA and optional OTP strengthen authentication, ensuring only verified users access sensitive documents or GenAI features.

This access model becomes especially critical when chatting with documents using GenAI. A user could theoretically ask an LLM to reveal confidential content — but elDoc prevents this by applying access rights within the AI layer. The system ensures that users can only query or generate information from documents they are authorized to access. Unauthorized users cannot retrieve, summarize, or extract insights from restricted files, even via AI chat. This is a fundamental part of elDoc’s security governance.

To support enterprise reliability, elDoc allows high-availability deployments, including clustering, failover, load balancing, and distributed architectures — ensuring continuous operation even in large-scale or mission-critical environments.

Every action performed within elDoc — document access, workflow progression, model inference, data extraction, or sharing — is captured in a full audit trail, providing traceability for compliance, internal investigations, and operational transparency. Complementing this is real-time monitoring and activity tracking, giving visibility into system performance, user actions, pipeline behavior, and model usage, with the capability to detect anomalies or unusual access patterns early.

For industries requiring stricter controls, additional protections such as optional encryption and hardened configurations can be enabled according to internal policies and regulatory frameworks.