Knowledge, RAG & graphs
Your agents' memory: business documents, data and relationships turned into trustworthy context.
NEO4J
QDRANT
PGVECTOR
LLAMAINDEX
DOCLING
Expertise in Knowledge, RAG & graphs
An agent is only as good as the information it can access. We build the knowledge layer that gives it context, with answers you can trace back to their source.
Document processing
PDFs, scans, and tables turned into searchable knowledge with Docling and LlamaIndex.
- DOCLING
- LLAMAINDEX
RAG on your data
Vector databases in Qdrant or pgvector on PostgreSQL so the agent answers from your documents and data.
- QDRANT
- PGVECTOR
GraphRAG and memory
A Neo4j knowledge graph that understands how customers, products, and decisions relate, and serves as the agents' long-term memory.
- NEO4J
Projects we've delivered
At BTH, each conversational agent answers through its own RAG, fed with the material from its course, and we combine structured employment data with information from the web to generate career guidance reports. At Validate Health we worked with sensitive health data on Databricks, where every result must be grounded in evidence. That experience is the foundation of our RAG and knowledge graph architectures.
