RAG Systems
Retrieval-Augmented Generation pipelines that ground AI responses in your actual data for accurate, trustworthy answers.
Bir Konuşma BaşlatınWhy Choose This Service
Grounded Answers
AI responses backed by your actual documents, databases, and knowledge — no hallucinations, no made-up facts.
Source Citations
Every answer includes references to the exact documents and passages used, so users can verify and explore further.
Real-Time Updates
New documents are indexed automatically, keeping the AI's knowledge current without retraining or manual updates.
Secure Data Handling
Your data stays in your infrastructure. We support on-premise deployments and private cloud setups for sensitive information.
Nasıl Çalışıyoruz
Data Assessment
We analyze your document corpus — PDFs, databases, wikis, emails — to design the optimal chunking and embedding strategy.
Pipeline Development
Build the ingestion pipeline, vector database, retrieval logic, and response generation with quality evaluation metrics.
Integration & Tuning
Connect to your applications, fine-tune retrieval quality, and set up automated ingestion for new content.
Teknoloji Altyapımız
Frequently Asked Questions
What types of documents can RAG process?
PDFs, Word documents, web pages, Markdown, plain text, database records, emails, Confluence wikis, and more. We build custom parsers for proprietary formats when needed.
How is RAG different from fine-tuning a model?
RAG retrieves information at query time from your current data, while fine-tuning bakes knowledge into the model at training time. RAG is cheaper, faster to update, and provides source citations. Fine-tuning is better for teaching the model new behaviors or styles.
Can RAG work with private, sensitive data?
Yes. We deploy RAG systems on your own infrastructure or private cloud. Your documents never leave your control. We also implement access controls so users only see information they are authorized to access.
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