RAG & Custom LLM Apps — make your private data queryable.
Proper retrieval-augmented generation architecture for your internal data. Document intelligence, private knowledge bases, semantic search. Built to actually work, not just demo well.
What you get
Document ingestion pipeline — ingest, chunk, embed, and index your documents with proper pre-processing
Vector database setup and management (Pinecone, Weaviate, pgvector, or Qdrant depending on your requirements)
Retrieval system with query optimization, re-ranking, and relevance tuning
LLM application layer — the interface (API, chat UI, or integration) that queries the retrieval system and generates responses
Evaluation framework — systematic measurement of retrieval quality and answer accuracy
Data refresh pipeline — keeping the knowledge base current as your documents change
Full documentation and 30 days post-delivery support
How it works
Data assessment
Audit your document corpus: formats, quality, size, update frequency, and access patterns. This determines the ingestion pipeline design and chunking strategy.
Architecture design
Design the full RAG stack: chunking strategy, embedding model, vector DB, retrieval approach, re-ranking, and the LLM layer. Written spec with reasoning, approved before build.
Ingestion pipeline
Build and run the ingestion pipeline. Test chunking quality against your specific documents. Tune chunk size and overlap for your content type.
Retrieval + application
Build the retrieval system and LLM application layer. Demonstrate against real queries from your team. Tune retrieval parameters against actual use cases.
Evaluation + calibration
Systematic evaluation: retrieval accuracy, answer quality, hallucination rate, latency. Fix weak points before production.
Deploy + handover
Production deployment. Refresh pipeline scheduled. Documentation delivered. 30-day support begins.
Tech stack
FAQs
RAG vs. fine-tuning vs. prompting — how do you actually decide?
Prompting first: if the task can be done with a good system prompt and in-context examples, do that. RAG when the knowledge base is too large for context, changes frequently, or needs to be queryable across thousands of documents. Fine-tuning when you need to change model behavior or style, not just provide it with facts — it's expensive and usually unnecessary.
What document formats can you ingest?
PDF, Word, HTML, Markdown, plain text, structured data (CSV, JSON). Scanned PDFs require OCR preprocessing. I'll tell you during scoping if your formats create complications.
How do you handle hallucination?
Two levers: retrieval quality (the system only generates from retrieved context) and response design (the LLM is instructed to say 'I don't know' when the context doesn't contain the answer). I'll include a hallucination rate metric in the evaluation framework so you can measure it, not just assume it.
What's the difference between a naive RAG setup and a proper one?
A naive setup: dump all your documents into an embedding store, retrieve the top-k chunks, stuff them in the prompt. It works sometimes. A proper setup: document-specific chunking strategies, metadata-filtered retrieval, re-ranking for relevance, query optimization, and evaluation against real questions. The difference is whether it works reliably or just occasionally.
Can this stay on-premise for data residency requirements?
Yes — I can architect entirely on your infrastructure (self-hosted embedding models, local vector DB, on-premise LLM if required). Data residency requirements are scoped and designed at the architecture stage.