RAG in Parsewise
It answers by meaning
RAG is the pipeline from a question to an answer from your data. The customer asks in their own words — the platform finds the right fragments by meaning and assembles the answer from them.
Embeddings
The question and the document become vectors in one space — a match by meaning, not by words.
Vector store
The nearest fragments by meaning are found in milliseconds — even in a large base.
Context for the model
The LLM answers from the fragments it found — from your data, not from imagination.
The path of one answer
Four steps between the question and the answer. Each takes milliseconds, and together they produce an answer from your data in under a second.
Question
«Can I return it without a receipt?» — an ordinary sentence, no keywords.
Query expansion
The platform adds synonyms and rewrites — recall goes up.
Vector search
The nearest fragments by meaning from the knowledge base — in a fraction of a second.
An answer with context
The model receives the fragments and answers from them — streaming into the dialogue.
Around a single core
Every node does one thing well. Embeddings compute the meaning, the vector store searches, the LLM selects the fragments — and shows the source on request.
Dimensions. A dense meaning vector for every fragment in the base.
Stores. The catalogue and the knowledge are searched separately — the context is sharper.
From the data. The answer is assembled from the fragments that were found.
Finds by meaning, answers by fact
The model does not invent — it quotes. The fragments that were found go into the prompt and the answer rests on them. No fragments, and the assistant honestly says it does not know.
Fast — and no slower tomorrow
Vector search barely notices the base growing. From a hundred documents to tens of thousands — the answer time moves by tens of milliseconds.
Search p95
semantic, across the whole base
First token
the answer starts typing at once
File in the base
from upload to answers
Degradation
the speed holds as the base grows
Precision lives in the details
Good RAG is a discipline of small things. Query expansion, honest fragment selection and the original source on demand.
Test it on your own data
The best test of RAG is your own documents Upload a couple of files and ask a question you already know the answer to.
Tell us your task
Projects by type grow year over year
MVP, redesign, AI and support — cumulative
The studio profile across key axes
Speed, quality, transparency, engineering
Research, design and build overlap
Parallel streams — not a waterfall