Private & on-prem RAG — your data never leaves your infrastructure.

RAG Development Services

We build retrieval-augmented generation systems that run entirely on your own machines — zero OpenAI or Anthropic API calls. Fixed-scope PoC in about 3 weeks, $10k–$25k, built for data-sensitive teams in pharma, finance, and legal.

✔ 0 External API Calls

✔ Fixed-Scope PoC · ~3 Weeks

Built with Local LLMs (Llama, Mistral, Qwen) LlamaIndex / LangChain On-prem vector DBs

Scope Your Private RAG PoC

Private RAG, Built for Data-Sensitive Teams

If your documents can't touch a third-party API, we build the whole stack on infrastructure you control.

Fully Local RAG Systems

LLM, embeddings, vector database, and documents all run on your machines — on-prem servers or your private cloud. No data leaves your network.

Document Ingestion Pipelines

PDFs, scans, spreadsheets, wikis, and internal systems — parsed, chunked, and indexed so answers cite the exact source page.

Local Model Selection

We benchmark Llama, Mistral, and Qwen variants on your actual documents and hardware, and pick the smallest model that meets your quality bar.

Retrieval Tuning & Evaluation

Hybrid search, reranking, and an evaluation set built from your real questions — so quality is measured, not guessed.

Access Control & Audit

Answers respect your existing permissions, and every query and source is logged for compliance review.

Handover & Training

Your team gets the code, the deployment runbook, and a working session on operating and extending the system.

Featured Case Study

Aru Pharma: RAG With Zero External API Calls

A pharmaceutical company that could not send sensitive data to cloud AI providers — so we brought the AI to their data.

Aru Pharma · Pharmaceuticals

Fully Local, On-Prem RAG System

Aru Pharma needed to query internal documents with an LLM, but regulatory and confidentiality constraints ruled out external AI APIs. We delivered a retrieval-augmented generation system that runs entirely on their local machines: local LLM, local embeddings, local vector store. There are zero OpenAI or Anthropic API calls — data never leaves their infrastructure.

100%
On-prem & local

External API calls

Private

Data stays in-house

Build Yours →

Built for Teams That Can't Use Cloud AI

Private RAG makes sense when confidentiality is non-negotiable.

Pharma & Life Sciences

Research documents, SOPs, and regulatory files queried in natural language — without exposure to third-party providers.

Finance & Accounting

Client records, contracts, and internal policies — searchable by your team, invisible to everyone else.

Legal & Compliance

Case files and precedents with cited answers, running inside your firm's own infrastructure.

Private RAG, answered

Common questions about RAG development

Straight answers on cost, privacy, and how local models compare.

Common questions

What is the difference between RAG and fine-tuning? +

RAG (retrieval-augmented generation) retrieves your documents at query time and lets the LLM answer with citations, so knowledge stays fresh and auditable. Fine-tuning bakes knowledge into model weights—better for style and format, but costly to update and hard to audit. For document Q&A and internal knowledge systems, RAG is usually cheaper, more controllable, and easier to keep current.

How much does a RAG PoC cost and how long does it take? +

A fixed-scope private RAG PoC at Essen Software runs $10k–$25k and takes about 3 weeks: week 1 for document ingestion and a baseline pipeline, week 2 for retrieval tuning and evaluation on your real questions, week 3 for the interface and deployment on your infrastructure. Price and deliverables are agreed before we start.

How does a local RAG system keep our data private? +

Everything—the LLM, the embedding model, the vector database, and your documents—runs on machines you control. There are zero calls to external AI APIs such as OpenAI or Anthropic, so nothing is sent to third parties. That is how we built Aru Pharma's system: it runs fully on their local machines and data never leaves their infrastructure.

Are local models good enough compared to API models like GPT or Claude? +

For question-answering over your own documents, well-chosen local models (Llama, Mistral, Qwen) combined with good retrieval come close to API-model quality—and when results fall short, the cause is usually retrieval, not the model. We benchmark local models on your actual documents in week 1 of the PoC, so you see the real quality before committing further.

Can a RAG chatbot answer anything about our platform — not just scripted FAQs? +

Yes — that's the core difference between a RAG assistant and an FAQ bot. We index your docs, help articles, product data, and policies; users ask anything in natural language and the assistant answers with citations from your actual content — and says “I don't know” instead of guessing when the answer isn't there. A PoC on your real content takes about 3 weeks.

Why choose Essen for private RAG

🔒
0
External API calls — fully local

⚡

~3 weeks

From kickoff to working PoC

💰

$10k–$25k

Fixed scope, agreed up front

🏭
Proven
In production at Aru Pharma

Talk Through Your Use Case →

Keep Your Data. Get the AI.

Tell us what your team needs to ask its documents — we'll propose a fixed-scope private RAG PoC.

Book a Discovery Call