Build RAG systems that answer from your data, not from vibes.
RAG is the line between an impressive demo and an AI product customers can trust. New Empire designs retrieval systems that make answers grounded, explainable, and maintainable.
Best fit
Teams with private knowledge bases, support content, policies, documents, or internal data that need reliable AI answers with sources.
Outcomes
- A retrieval architecture matched to your documents and user questions
- Source-grounded responses with citations and clear uncertainty handling
- A maintainable ingestion path for new and updated knowledge
- Quality checks that reveal retrieval failures before users do
What we deliver
- Document audit and chunking strategy
- Embedding, vector storage, and re-ranking implementation
- Prompt and answer policy for citations and unknowns
- Evaluation set for core questions and failure cases
Proof you can inspect
- Shipped RAG-backed AI SaaS behavior with multi-provider model architecture.
- Published practical writing on why retrieval quality matters more than prompt theatrics.
- Experience delivering systems where trust, uptime, and maintainability matter.
How the sprint works
- Collect representative documents and real user questions.
- Design chunking, metadata, retrieval, and citation rules.
- Build the first end-to-end RAG flow and test it against known answers.
- Add monitoring and iteration hooks for retrieval misses and hallucination risk.
LinkedIn post angles
Use these as post hooks and point each one back to this page with UTM tags.
- Most RAG failures are retrieval failures, not model failures.
- The quickest RAG quality win: ask real user questions before designing chunks.
- Why citations are a product feature, not just an engineering detail.
Questions buyers ask
Can RAG remove hallucinations completely?
No serious implementation should promise that. The goal is to reduce risk with retrieval, citations, constraints, evaluation, and fallback behavior.
What data sources can be used?
Common sources include PDFs, support docs, Notion or CMS content, database records, uploaded files, and internal knowledge bases.
Do we need to retrain a model?
Usually no. Most business RAG systems update knowledge by changing documents and indexes, not by fine-tuning or retraining a base model.
Next step
Have a product or AI workflow worth shipping?
Send the context, the risk, and what a useful first version needs to do. We will turn it into a practical build path.
Book a project call