Knowledge Retrieval
Dense Vector Search, Neo4j Knowledge Graphs & Memory
Engineering hallucination-resistant retrieval architectures combining dense semantic search (Qdrant), property knowledge graphs (Neo4j GraphRAG), and continuous agent memory (Mem0).
Grounding Intelligence in Enterprise Reality
Autonomous intelligence requires rich, verifiable enterprise context. Simple keyword search and naive vector similarity frequently hallucinate or fail to understand interconnected business entities.
My Knowledge Retrieval practice delivers hybrid retrieval-augmented generation (RAG) that pairs dense semantic embeddings with the structural rigor of property knowledge graphs.
Dense Vector Search with Qdrant
Using Qdrant, we achieve sub-millisecond similarity search across millions of documents. We configure advanced payload filtering, quantization, and hybrid sparse-dense embeddings to ensure high retrieval precision.
Relational Context with Neo4j Knowledge Graphs
Business knowledge is inherently relational. By modeling enterprise data in Neo4j, agents can traverse multi-hop relationships between products, departments, contracts, and regulations—unlocking true GraphRAG reasoning.
Continuous Agent Memory with Mem0
Agents must retain context across conversations. With Mem0, we equip agents with long-term memory that adapts to user preferences and preserves organizational continuity.
🎯Service Scope & Key Capabilities
This module covers the following targeted topics and key expert competencies:
📦Specifications: Inputs & Deliverables
A clear breakdown of the resource inputs required from your side and the concrete deliverables you will receive as the output of this service module:
Required Client Inputs
To be provided to initiate development
- Internal document repositories, PDFs, Markdown documentation, and unstructured data
- Relational databases, ERP schemas, CRM records, and product catalogs
- Corporate domain ontologies, terminology glossaries, and entity relationships
- User interaction histories, personalization requirements, and session boundaries
Guaranteed Deliverables
Outcome packages included in the scope
- Enterprise Knowledge & Retrieval Architecture Blueprint
- Configured Qdrant Vector Database Cluster with optimized HNSW indexing
- Turnkey Neo4j Property Knowledge Graph Schema & Ingestion Pipelines
- Integrated Mem0 Agent Memory Store with continuous recall hooks
- Hybrid Retrieval Benchmarking Suite & Ground-Truth Test Harness
📋Project Plan & Execution Phases
Our Knowledge Retrieval consulting grounds models in enterprise truth:
Data Source & Ontology Audit
Cataloging internal databases, document repositories, schemas, and defining domain entity ontologies.
Vector & Graph Pipeline Design
Configuring Qdrant vector collections and engineering Neo4j property knowledge graph ingestion pipelines.
Hybrid Retrieval & Memory Integration
Implementing dual-path retrieval algorithms and configuring Mem0 for cross-session agent recall.
Evaluation & Grounding Benchmarking
Testing retrieval precision, recall metrics, and verifying hallucination mitigation against ground-truth queries.
Start a Conversation
Write directly to request an inquiry about the Knowledge Retrieval module or custom bundle options.
Contact & Booking Inquiry
Send an email request to
georg.hackenberg@fh-wels.at
Typically responding within 2 business days.
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