[ 01 ]
Document ingestion
Parsing, cleanup, chunking, metadata, versioning, and repeatable pipelines for the sources that matter.
SERVICE / 02 · Grounded answers · connected knowledge
RAG + Knowledge
Graphs
We turn fragmented documents and domain knowledge into retrieval systems that answer with evidence, preserve context, and show where every important claim came from.
RAGNeo4jVector searchHybrid retrievalCitations
Cited answers
Claims grounded in source passages
Graph context
Entities and relationships preserved
Observable
Retrieval quality you can inspect
/01 — The system
Retrieval that earns trust.
A production RAG system is an information product, not a vector database with a chat box. The ingestion, retrieval, ranking, grounding, citation, evaluation, and update paths all need deliberate design.
[ 02 ]
Hybrid retrieval
Vector, keyword, metadata, reranking, and domain filters combined around the questions users actually ask.
[ 03 ]
Knowledge graphs
Entity and relationship extraction into Neo4j or another graph layer for connected, multi-hop context.
[ 04 ]
Citation systems
Passage-level provenance, source previews, and answer structures that keep evidence visible.
[ 05 ]
RAG evaluation
Retrieval recall, grounding, citation accuracy, response quality, and regression datasets tracked over time.
[ 06 ]
Access control
Tenant, team, document, and field-level boundaries carried from storage through retrieval and response.
/02 — Delivery system
Build the corpus, then the answer.
We start with representative documents and real questions. That keeps architecture choices tied to retrieval quality rather than to whichever database happens to be fashionable.
01
Audit
Inspect the corpus, permissions, update cycle, query types, and the evidence users need to see.
02
Retrieve
Prototype chunking, metadata, search, reranking, and graph enrichment against a test question set.
03
Ground
Design answer generation, citations, confidence behavior, and safe handling of missing evidence.
04
Measure
Ship with evaluations and traces so retrieval failures become diagnosable product work.
/03 — Questions
Before we build.
The useful constraints are usually about reliability, access, ownership, and operating the system—not whether a model can produce a convincing demo.
When does a knowledge graph help beyond vector search?
A graph helps when relationships, identity, hierarchy, chronology, or multi-hop reasoning are central to the questions. If those properties do not materially improve answers, we keep the system simpler.
Can the system preserve document permissions?
Yes. Permission metadata and tenant boundaries can be enforced during retrieval so users never receive context from sources they are not allowed to access.
How do you evaluate RAG quality?
We test retrieval, grounding, citation correctness, completeness, and response usefulness separately. That makes it possible to identify whether a failure came from ingestion, search, ranking, context, or generation.
Can you work with our existing vector database?
Usually. We first verify that it supports the required filters, scale, latency, and operational constraints, then improve the surrounding pipeline rather than replacing infrastructure without a reason.
/04 — Related proof
See the system in context.
Explore the products and adjacent capabilities that show how this work connects to a complete, launchable experience.
Ready to make it
real?
Send the problem, the context, and the honest budget. We will reply within one business day with useful next steps—even when the right answer is not us yet.
hello@windroselabs.com ↗