Will our data be used to train OpenAI models?

No. We use API-based models or private instances (Azure/AWS) where data training is explicitly disabled.

Can it handle complex tables in PDFs?

Yes, we use advanced 'Unstructured' data parsers to convert complex PDF logic into LLM-friendly formats.

Service

Enterprise LLM Integration & RAG

Name: Enterprise LLM Integration & RAG
Brand: Magehire
Availability: InStock

Connect your private enterprise data to modern LLMs without sacrificing security. Specialized in RAG (Retrieval-Augmented Generation) for exact results.

Quick Answer

"Magehire specializes in Enterprise LLM integration using Retrieval-Augmented Generation (RAG) to provide AI models with secure, instant access to your private corporate knowledge. We build vector database architectures that ensure AI outputs are citable, accurate, and kept entirely within your private security perimeter."

PineconeMilvusOpenAIAzure AIAWS BedrockUnstructured.io

Chat with Your Data View Case Studies

Secure RAG pipelines using Pinecone, Milvus, and Weaviate

Private LLM hosting for zero data leakage to public models

Automated document parsing for complex PDFs and databases

Citation tracking to ensure 100% accurate AI responses

Scope

What's Included

Retrieval-Augmented Generation (RAG) Setup

Vector Database Architecture & Scaling

Secure Document Parsing & Ingestion Loops

Private LLM Deployment (Llama 3 / Mistral)

Prompt Engineering for Enterprise Accuracy

Ideal Clients

Who This Is For

Legal and accounting firms with massive documentation
HR and Operations teams managing internal knowledge
Healthcare organizations needing HIPAA-compliant AI
Customer success teams with deep knowledge bases

Applications

Use Cases

Instant document discovery across millions of pages

Policy and compliance automated checking

Personalized internal employee support bots

Technical documentation 'expert' interfaces

Methodology

Our Process

1
Data Audit
Analyzing the structure and volume of your internal data sources.
2
Vector Pipeline
Designing the chunking, embedding, and indexing pipeline for high-speed retrieval.
3
LLM Orchestration
Connecting the data layer to the LLM with custom-tuned system prompts.
4
Feedback Loop
Implementing citation tracking and RLHF to ensure 100% data accuracy.