As Retrieval-Augmented Generation (RAG) becomes a viable option for providing better legal research, GraphRAGs are emerging as powerful systems for mapping legal relationships, improving context, and extracting deeper insights. Their potential to enhance research capabilities raise an important question: Will GraphRAGs become an intrinsic feature of all LegalTech solutions.
Joseph Pookkatt, principal architect at Staram Analytics, explained, “LegalTech is essentially a confluence of data, compute and knowledgeable resources.” The LegalTech domain in India, flooded with huge loads of unstructured data, expensive computing and handling of LLMs, and inaccessibility to legal experts, is slowly tackling these issues one by one. In this context, technologies like RAG and GraphRAGs are becoming increasingly important for enabling accurate and efficient computing.
The Need for GraphRAGs
RAGs are widely used across specialised domains to improve generation by retrieving relevant content from proprietary databases and feeding it into the system, effectively forming a foundational layer for the LLM to operate upon
RAGs are being adopted in almost all Legal Tech companies. Critics, however, have pointed them out as “LLM wrappers”. It is the most cost-effective method to reduce LLM hallucinations and deliver accurate information, making it a preferred alternative to fine-tuning. They work well in specific domains like drafting, contract life cycle management and litigation.
GraphRAGs take this a step further by making use of knowledge graphs (KGs). While traditional relational databases are effective for managing and organising tabular data with strong filtering and sorting features, they handle each column as a separate attribute, which limits how well they can represent intricate, real-world relationships among entities.
In contrast, knowledge graphs enable the efficient exploration of business data by allowing users to navigate meaningful connections between entities. This approach uncovers patterns and insights that are difficult to access within isolated, tabular datasets.
Therefore, GraphRAGs help in mapping relationships and linkages within the retrieved data, allowing for a deeper understanding not just of the data itself, but also of the broader concepts it represents. This helps particularly for use cases involving interconnected legal sources, case law analysis, and understanding legal precedent.
RAGs vs GraphRAGs
RAGs give sharp and crisp results based on the query by using metadata to narrow the semantic search space. GraphRAGs, on the other hand, use metadata to travel through the graph and fetch relevant information through relationships. RAGs can’t jump between laws or interlink clauses, but GraphRAGs can.
For legal research to be truly optimised, the choice between RAG and GraphRAG should be tailored to the specific needs of the matter at hand. As Hitesh Jirawla, founder & CEO, Cubictree says,”It’s not a question of one being better, it’s a matter of matching the correct tool to the correct legal research problem.”
Why are companies not keen on switching to GraphRAGs?
GraphRAGs need heavy capital expenditure on infrastructure and high quality teams that many small startups focused on scaling might not be able to afford. Dr. Himanshu Puri, co- founder COO at Legitquest, said, “We have seen a legal research platform giving a 30% result and by using RAG, we are able to reach 80%. This is a bigger delta.”
While GraphRAGs can be used as a startup, “the kind of bandwidth, the kind of resources which are needed; whether to even invest in that and get a little lesser delta is obviously a question to us. If it is a bigger delta also, we want to be a lean team,” Puri added.
Another way to look at it is from the client’s perspective, where full access to all laws may not be necessary. Shubham Kumar Nigam, CTO of Techpeek, shared an example of a multinational accounting firm that approached them specifically for financial domain solutions. “They only wanted data related to the income tax domain, nothing else. So they want all those cases which are published only in the financial domain, and they have their own database.They’re just making their own add-ups.”
Optimizing GraphRAGs
Aniruddha Yadav, founder and CEO at CaseMine, explained that they use their own custom knowledge graphs and proprietary GraphRAGs, as standard GraphRAGs did not work for them.
“GraphRAGs is not all that attractive for us. If you’re looking at transactional work, for example, maybe it’s somewhat useful there, but then again, you could build your own knowledge extraction system that would be more useful,” he said. Having a proprietary knowledge graph tailored to a company’s requirements produces better results.
Pookkatt pointed out that the real value of GraphRAG depends entirely on how well the underlying data is structured. “The true potential of GraphRAG hinges on how the underlying knowledge is structured. Even KGs can fail if the underlying data is not structured properly to capture all the intelligence that has been locked inside of application silos for years,” he said.
To overcome this, he emphasized the role of semantic consistency across systems: “You would need to work with a good ontologist to coherently work with that shared data across the enterprise to take full advantage of KGs and maximize the full potential of a semantic layer as the foundation for data-centric intelligent apps.”
Knowledge Graphs as the Core
As Legal tech companies move from being uni-focused and vertically integrated to enterprise-level full-stack service providers, it becomes necessary to understand law as a subject and not just as backend legal workflows.
Knowledge graphs are purpose-built to traverse intricate relationships among diverse types of data across the enterprise. Pookatt explained,”KGs enable contextual understanding and reasoning — connections between facts allow enterprise systems to understand not just data, but meaning and context, which is crucial for advanced analytics and AI.”
Pookkatt highlighted that with the rise of agentic AI, companies must move towards “data plus knowledge graphs,” since “agentic AI requires you to understand processes. If you’re not in a position to define a process, then how are you going to replace workflows?”
To enable uniform offerings, the technology must support “litigation, compliance, contracts, and also practice piece [management] on top of it,” he added.
If homogenous offerings become a reality, the judicial system can also stand to gain by adopting them. Their power, interoperability, transparency, equity, and scalability can help resolve challenges like unequal access, inconsistent rulings, backlogged cases, and procedural unfairness.
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