BlueMouse.ai

RAG & OCR infrastructure

Deploy RAG and OCR where your data requires.

Choose where documents are processed, embeddings are created, indexes are stored, models run, and generated answers or agent actions are recorded.

BlueMouse.ai supports on-premise, private-cloud, commercial API, and hybrid architectures. We design the document pipeline as a whole, including OCR services, storage, vector search, language models, integrations, monitoring, and review queues.

Compare options Review security
01On-premise document stack
02Private-cloud RAG & OCR
03Approved managed services

Three core models

Place each document intelligence component inside the right boundary.

Maximum control

On-premise RAG and OCR

Run document ingestion, OCR, embeddings, vector search, and language models on infrastructure you control. This creates the clearest physical boundary for sensitive repositories and regulated workflows.

  • Best for strict data-location or network-isolation requirements
  • Supports local object storage, databases, indexes, and model servers
  • Requires capacity planning, patching, monitoring, and internal operational ownership
Balanced flexibility

Private-cloud document intelligence

Place the RAG index, OCR workers, application services, and open-source or approved models in a dedicated cloud environment with defined networking and identity controls.

  • Best when you need isolation without managing physical hardware
  • Scales ingestion and retrieval components independently
  • Supports private networking, managed keys, regional hosting, and dedicated compute
Fastest start

Managed OCR and model APIs

Use selected commercial document-processing and language-model APIs while retaining the application, access policy, retrieval index, and audit records in your controlled environment.

  • Best for rapid delivery and specialised model capability
  • Avoids operating every model and OCR service directly
  • Requires review of provider retention, training, residency, security, and availability terms

Hybrid architecture

You can separate the pipeline by data sensitivity and workload.

A hybrid architecture can keep original documents, extracted fields, and the retrieval index private while sending only approved, minimised context to an external model. OCR, embeddings, reranking, generation, and agent tools do not all need the same provider or deployment location.

Private

Keep sensitive document state controlled

Original files, personal data, extracted values, access-control metadata, vector indexes, internal policies, and audit records can remain in your environment.

Selective

Use external capability deliberately

Approved OCR or reasoning APIs can receive only the content required for a defined task, with redaction, routing, logging, and fallbacks applied around the call.

Cost and control

Deployment affects pricing, governance, and scale.

Decision factor
What changes
Document privacy
Where original files, OCR output, chunks, embeddings, indexes, prompts, and logs are stored or processed.
Usage cost
Pages processed, tokens, storage, retrieval traffic, GPU capacity, support, and managed-service charges.
Performance
OCR throughput, indexing time, retrieval latency, model response time, concurrency, and resilience.
Governance
Identity, source permissions, encryption, review gates, citations, audit logs, retention, and provider controls.

Architecture first

Choose the information boundary before the document workload scales.

We will help you compare privacy, quality, cost, performance, and operational ownership before you commit to a production RAG or OCR architecture.

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