Aishwarya Reehl is an experienced software engineer specializing in artificial intelligence and machine learning, with a strong foundation in full-stack and cloud-based application development. Over the course of her career, she has built and deployed large-scale AI-driven systems within government and highly regulated environments, where reliability, security, and compliance are non-negotiable. Working with sensitive data at scale has shaped her approach to artificial intelligence, pushing her to focus not only on model capability, but on traceability, governance, and real-world accountability. Her work centers on applying modern AI techniques, including large language models, to complex engineering challenges in ways that are practical, defensible, and production-ready.
In this interview with AllTech Magazine, Reehl draws on her hands-on experience to demystify GraphRAG for technical and business readers alike. She explains how graph-based retrieval extends traditional retrieval augmented generation, why many enterprise teams are moving beyond pure vector search, and how structured reasoning paths can significantly improve explainability in high-trust systems. From compliance-heavy industries to financial risk and supply chain environments, she offers a grounded look at where GraphRAG delivers the most value and how organizations can experiment with it without dismantling their existing infrastructure.
Drawing on your experience building AI systems in government and regulated environments, how would you explain GraphRAG in simple terms to readers who are new to large language models?
To understand GraphRAG, let’s first try to understand what is RAG? Retrieval-augmented generation is basically a technique in which, given a user’s question or input, the system tries to find/search knowledge bases like documents or databases, etc. Once it finds relevant information, it processes it and generates an output using AI.
Knowledge graphs are nothing but information between nodes and relationships. For example, nodes could be cities and states, and relationships are simply which city belongs to which state.
GraphRAG is a combination of RAG and graphs from above. Instead of just searching LLMs with an input, GraphRAG first tries to understand all the entities in the Graph, finds the relationships, and retrieves a structured context for them. Using this context, it finds a relevant output for the user.
Many enterprise teams today rely on vector search to support LLM applications. From your perspective as a software engineer, what limitations did you encounter that led you to explore graph-based retrieval instead?
Many enterprise teams move from pure vector search to GraphRAG. There are many real-world limitations that cause them to move to GraphRAG:
- Vector search retrieves chunks based on semantic similarity but fails to establish a logical connection. If there are multiple relationships, it can’t traverse them easily, causing delay and hallucinations.
- Vector search fails to create explicit relationships. It fails to query when you need structured facts.
- Vector search results aren’t consistent, which causes trust issues, as well as compliance issues.
- The performance of GraphRAG is way higher compared to vector search, which impacts sources.
- Vector search isn’t scalable; however, GraphRAG performs great in this case.
How does GraphRAG help large language models better understand context when working with sensitive or highly structured enterprise data?
When dealing with sensitive, regulated, or highly structured enterprise data,, GraphRAG gives LLMs a much deeper understanding of context by encoding not just raw information, but also the relationships between entities, organizational hierarchies, access controls, time and security constraints, and business rules.
This structural awareness brings several critical advantages. As GraphRAG can trace exactly which nodes and relationships informed a given response, it also adds a layer of explainability. It also improves entity disambiguation — ensuring that each entity with multiple meanings are interpreted correctly (using unique IDs, etc.) based on their context in the graph, which is essential in regulated industries where precision is non-negotiable.
Beyond that, GraphRAG enables detailed-level governance, which allows organizations to control what data the model can access and reason over at a much deeper level. It can follow chains of relationships across multiple steps, which is known as multi-hop reasoning. It can answer complex queries that require connecting several pieces of information together. When these features are combined, these capabilities significantly reduce hallucination in high-stakes settings, where an incorrect result can carry serious consequences and damages.
Explainability is a major concern in regulated systems. How do graphs make it easier to trace and explain how an LLM arrived at a particular answer?
Explainability isn’t optional in regulated systems. GraphRAG determines the results using the following approach:
Entity A → relationship → Entity B → relationship → Entity C
When needed, GraphRAG can traverse the path and determine why the answer was derived the way it was. This chain isn’t just how the answer is derived, but it’s also the explanation. When a regulator wants to understand why a particular result occurred, GraphRAG can trace the exact path it followed. There are no black-box embeddings, no similarity scores, but just a clear, inspectable chain of reasoning.
Regulated systems are rarely simple. They often require connecting multiple pieces of information across several steps, and GraphRAG handles this efficiently by tracking explicit reasoning paths. This makes it preferable for complex, cross-domain queries that regulated environments demand.
Explainability also demands reproducibility. If the same query returns a different answer tomorrow, that inconsistency is not just inconvenient, but it’s also a compliance risk. As GraphRAG grounds its responses in structured graph traversal rather than probability, it significantly reduces this risk, providing greater confidence that answers will remain consistent over time.
Finally, in regulated systems, explanations must be reflected in their policies. GraphRAG provides a direct mapping between its outputs and the underlying rules and relationships that govern them, ensuring that every answer can be traced back to something defensible and auditable.
Based on your hands-on work applying AI to real-world engineering problems, what types of enterprise use cases benefit most from a GraphRAG approach?
GraphRAG performs the best when relationships matter and when answers must be traceable, policy-aware, and multi-step. I have had the experience of using it in regulatory and compliance systems, financial risk, and supply chain systems.
Organizations often worry that adopting new AI techniques means rebuilding their entire stack. How can teams begin experimenting with GraphRAG while keeping their existing cloud and application infrastructure intact?
It’s actually a valid concern; rebuilding an entire data stack is expensive, time-consuming, and often organizationally impractical.
The key insight is that GraphRAG can be layered on top of existing systems rather than replacing them. The first step should be to introduce the entities and then extract the data from them, rather than rebuilding. There’s no need to graph everything at once, and as a matter of fact, attempting to do so is one of the most common mistakes.
Not everything needs to be graphed. Choose priority items, keep the existing vector approach, and try to build a hybrid model where graph-based retrieval handles structured, relationship-heavy queries and the vector approach continues to serve broader semantic search.
Deploy these as microservices and reuse the existing application infrastructure. GraphRAG changes how data is retrieved, not how it is generated. So for the LLM, it’s just receiving better-structured context.
What are some common misconceptions you see when teams first consider using GraphRAG in enterprise systems?
When teams first try to implement GraphRAG, there are a few misconceptions that you generally come across:
- GraphRAG will replace your data warehouse. However, it doesn’t replace your existing data architecture; it’s a knowledge layer and not a database storage change that you implement.
- A data warehouse is not equal to a graph. Warehouse stores data differently compared to a graph. In graphs, multi-hop traversal is native and dynamic.
- Not everything needs to be graphed. Start with one bounded domain, model only high-value entities, and then expand gradually.
Looking ahead, how do you think graph-based retrieval will change what is possible with large language models in regulated and high-trust environments?
Graph-based retrieval doesn’t just improve what LLMs can do in regulated environments; it changes what they’re trusted to do.
The biggest barrier to deploying LLMs in regulated and high-trust environments isn’t capability; it’s accountability. GraphRAG directly addresses that gap by making the reasoning process visible, traceable, and auditable. As that trust builds, the scope of what these systems are permitted to handle will expand in the organization.
This means LLMs are moving from being assistive or add-on tools to actually participating in workflows that currently require human intervention. The documentation it can handle, along with its ability to maintain regulatory and compliance requirements, can provide the structural guardrails that enable higher confidence.
The major shift you see is how organizations will think about their data. Knowledge graphs are not just a retrieval mechanism, but the process of building and maintaining a graph requires organizations to code their entities, relationships, rules, and hierarchies in ways that make that knowledge inspectable and governable.
The models will keep training and getting better regardless. With graph-based retrieval, organizations can accelerate their readiness to actually deploy them where it matters most.
As large language models continue to evolve, Reehl argues that the real shift in regulated and high-stakes environments will not be about raw capability, but about trust. By making reasoning paths inspectable, auditable, and aligned with organizational rules, graph-based retrieval opens the door for AI systems to move from assistive tools to accountable participants in core workflows. Her perspective highlights a broader transformation underway, where the structure and governance of data become just as important as the intelligence layered on top of it.
