NEW YORK, NY, UNITED STATES, June 2, 2026 /EINPresswire.com/ — AI Engineer Sri Bhanu Gundu helped a construction AI system move from below 70% retrieval accuracy to 90%+ by replacing vector-only RAG with a hybrid Neo4j knowledge graph architecture, cutting reliance on external vector databases by 50% and exposing a repeatable pattern for enterprise AI teams.
The story focuses on a problem many enterprise AI teams now face: vector-only RAG works for broad semantic search, but breaks when the data depends on relationships. In construction, a part number may connect to a specification, a standard, a project phase, and a compliance record. Treating those documents as isolated chunks caused wrong-context retrieval, missing relationship chains, and rising vector database costs.
Sri Bhanu Gundu’s solution combined Neo4j graph traversal, construction-specific entity embeddings, LangChain, LlamaIndex, and PageRank algorithms to preserve the relationships vector search discarded. The graph narrowed retrieval to the right project, standard, and specification context; vector search then found the most relevant document inside that narrowed context.
“The breakthrough was not choosing between graph and vector retrieval — it was combining them,” Sri Bhanu Gundu wrote in the Careery feature. “A graph query narrows the search to the right context, such as the correct project and correct standard. A vector query within that context finds the most relevant document. Together, retrieval accuracy exceeded 90%.”
The feature also details how Sri Bhanu Gundu evaluated CrewAI, LangGraph, and Amazon Bedrock Agents for multi-agent RAG orchestration. She selected CrewAI for the production system because it offered the best balance of development speed, model flexibility, and infrastructure cost. The resulting multi-agent pipeline improved part-number retrieval accuracy by 75% using specialized extraction, validation, verification, and reconciliation agents.
For recruiters and hiring managers, the feature positions Sri Bhanu Gundu as an AI engineer who does more than assemble LLM tools. She benchmarks retrieval architectures, identifies where generic RAG fails, builds graph-aware systems for domain-specific data, and makes cost-performance decisions that matter in production.
The full Careery Insight is available here: https://careery.pro/insights/knowledge-graph-rag-neo4j-construction-ai-sri-bhanu-gundu
Bogdan Serebryakov
Careery
email us here
Legal Disclaimer:
EIN Presswire provides this news content “as is” without warranty of any kind. We do not accept any responsibility or liability
for the accuracy, content, images, videos, licenses, completeness, legality, or reliability of the information contained in this
article. If you have any complaints or copyright issues related to this article, kindly contact the author above.
Media gallery
