GraphRAG is Quietly Becoming AI’s Next Big Leap

AI has long faced challenges around trust. Vector search can find documents that look similar, but it often fails when connections between entities matter most. Graph-based Retrieval Augmented Generation, or GraphRAG, is emerging as a way to fix this by combining semantic search with graph reasoning.

The approach does more than retrieve results. It shows how entities are linked, making outputs both accurate and explainable. For enterprises facing pressure to justify AI decisions, this hybrid of vectors and graphs is gaining attention.

Siddhant Agarwal, developer relations lead for APAC at Neo4j, and Bhanu Jamwal, head of India business at TiDB, believe that GraphRAG represents a shift in mindset as much as in technology. While they agree on its potential, they differ on how steep the learning curve is and how cloud platforms are shaping its adoption.

The Cloud Empowerment

Building GraphRAG in practice has often been seen as complex. Agarwal believes managed services like the ones on Google Cloud are lowering this barrier.

In a conversation with AIM, he said, “Vertex AI’s native support for embeddings, custom models and prompt orchestration makes it easier to experiment with hybrid search and GraphRAG patterns.” He explained that with Neo4j Aura and Cloud Run managing the backend, developers can focus on retrieval pipelines rather than infrastructure.

Jamwal, however, takes a more cautious stance. “Cloud platforms help, but they don’t eliminate the complexity of GraphRAG,” he said. He pointed out that design choices around graph structure and orchestration remain squarely in the hands of developers. What cloud services do well is “handle scaling, security and monitoring” so teams can move faster once the fundamentals are in place.

This divergence captures the balance where cloud services accelerate adoption, yet cannot replace the need for strong graph fundamentals.

Where GraphRAG Shines

When it comes to applications, both Agarwal and Jamwal point to areas where relationships matter more than raw content. 

Agarwal illustrated this to AIM with an example: “Graphs are everywhere, whether it’s the social connections we form, the supply chains that power global trade, the biological pathways in our bodies, or the relationships between legal clauses in a contract.”

He stressed that “if your domain has interconnected entities, chances are GraphRAG can enhance it”. He lists compliance, legal analysis, healthcare decision support, and recommendation engines as prime candidates for improvement. GraphRAG ensures retrieval paths are not only correct but also explainable—a necessity when enterprises must justify AI-driven insights.

Jamwal also discussed other use cases, such as fraud detection and risk management. “GraphRAG is suitable for apps where relationships matter instead of content only,” he said, noting that it can uncover suspicious connections that vector-only retrieval misses. 

For businesses seeking to move from generic search to actionable intelligence, these relationship-aware applications can become critical.

The use cases demonstrate that whether in legal contracts, pharmaceutical knowledge or financial transactions, GraphRAG adds value where the “why” behind a result is as important as the “what”.

The Struggles of a Developer

For both voices, the most challenging part of GraphRAG lies not in coding but in modelling and integration. 

Agarwal stresses that a poorly designed graph schema or sparse relationship network can produce retrieval results that feel odd, even if the LLM itself is highly capable.

“The learning curve for GraphRAG depends heavily on a developer’s familiarity with three key areas: graph databases, knowledge graph modelling and RAG,” he said.

Jamwal expands this further, warning that schema design can fail both ways: being too simple and missing context, and too complex and becoming unmanageable.

He also highlighted practical hurdles. “Choosing the right schema, balancing embedding with traversal results, and ensuring proper prompt structure so the model uses graph insights effectively.” 

Agarwal added that embedding inconsistency is another recurring problem, where mismatched models lead to irrelevant outputs.

Both perspectives underline the same truth, where developers are not fighting the code; they are fighting the graph design.

Towards Explainable AI

Looking ahead, Agarwal envisions GraphRAG as a “foundational component for intelligent agents capable of multi-step planning, simulation and decision-making”. 

“The future of GraphRAG is heading towards more autonomous, agentic systems that can not only retrieve and reason but also take context-driven actions,” he told AIM.

Jamwal, while less futuristic in framing, sees its importance in explainability. “GraphRAG will make reasoning paths more visible to the user, so it will be a part of the Explainable AI journey,” he said.

“We also can expect there will be more native support from cloud platforms to have graph-aware services for their LLMs,” he said.

The convergence is striking. One expert sees autonomous agents, the other sees explainable intelligence. Both agree that GraphRAG is becoming a foundation for the next wave of enterprise AI.

Graph Thinking Goes Mainstream

Together, their inputs paint a balanced picture. GraphRAG demands a mindset shift; it is made more accessible with cloud scaffolding, and it shines in applications where trust and relationships matter. The learning curve is real, but so are the rewards.

As enterprises move beyond general-purpose copilots towards systems that must justify every step, GraphRAG may quietly become the architecture of choice. It does not only retrieve. It reasons, contextualises and explains. In a landscape where AI’s credibility often hangs by a thread, that ability may prove decisive.

The post GraphRAG is Quietly Becoming AI’s Next Big Leap appeared first on Analytics India Magazine.