Data is used in every sector, from healthcare to finance to legal. Companies are collecting more data than ever, but extracting valuable insights from this data is a challenge. They mostly rely on manual data extraction, which can be time-consuming, expensive, and error-prone.
To address this challenge in the financial services domain, Aashish Mehta (CEO), Hrishikesh Rajpathak (CTO), and Prabhod Sunkara (COO) founded nRoad in 2021. This Massachusetts-headquartered, Pune-based company uses contextual and domain-aware AI for data extraction, helping businesses make better decisions about their products and services.
To learn more about nRoad and how it uses AI to address data extraction challenges, we interviewed Hrishikesh Rajpathak, co-founder and CTO of nRoad. Hrishikesh shared insights on nRoad’s journey, technological innovations, and the challenges and opportunities ahead.
Market Gap and Origin of nRoad
The inception of nRoad wasn’t merely a spark of inspiration. It was a response to tangible data extraction problems deeply rooted in the BFSI space.
Since Ashish and Prabodh had a BFSI background, they intimately understood the issues in the sector and the multi-billion dollar opportunity for solving them using technology. With Hrishikesh’s deep expertise in artificial intelligence and data science, the trio collaborated to start nRoad and find a solution to these problems.
With a clear problem statement and a validated idea, nRoad developed proof of concepts (POCs) to validate the product hypotheses of their AI solution.
Today, the POC has evolved into a next-generation AI platform – Convus, that abstracts and incorporates unstructured data and documents into vital business functions.
Role of Knowledge Graphs and LLMs in nRoad
In delving into the workings of nRoad and its product, our discussion centered on how technology enhances the entire data comprehension process. nRoad specializes in decoding unstructured data, including text in paragraphs and data presented in tables.
A knowledge graph is typically a format for representing knowledge taken from any source. While relational databases capture data, knowledge graphs transcend by encapsulating not just raw data but also the associated information and insights that elevate it to knowledge. This involves understanding not only individual data points but also their interconnectedness.
Hrishikesh added that the connection between different data parts or data sources and creating a singular picture out of the entire document or entire source of data is made using LLMs.
In their quest to optimize this process at nRoad, Hrishikesh found that experimenting with multiple ways of handling this problem, “a combination of ontology + LLMs + knowledge graph,” brings a lot of value to us in solving this problem.
Beyond LLMs - Multifaceted Tech Stack of nRoad
While LLMs are pivotal in accelerating the processes at nRoad, they are just one component of the comprehensive technological toolkit.
Hrishikesh also commented on the workflow, which typically involves the initial extraction of data, its conversion into a specific representation involving knowledge graphs and databases, and finally, the data analysis phase.
nRoad's USP - Context Sensitivity and Domain Awareness
The assertion that nRoad’s extraction algorithm is trained to be context-sensitive and domain-aware is a strategic necessity deeply ingrained in solving enterprise problems.
The challenge lies in infusing this domain knowledge into the models effectively. To overcome this hurdle, nRoad employs a multifaceted approach.
This approach adds a layer of sophistication to nRoad’s capabilities, elevating it to a level where it doesn’t just read data; it comprehends the context based on the industry or domain.
Product Evolution and User Feedback Integration
On addressing the intricacies of user feedback collection and its integration into the product roadmap, Hrishikesh reveals a comprehensive approach adopted by nRoad.
In response to queries about the nature of data collected, Hrishikesh elucidated that, unlike traditional surveys, nRoad relies on a distinctive approach, presenting machine-generated outputs to users.
This human-in-the-loop model ensures that qualitative aspects are thoroughly examined in sectors demanding thorough examination, such as banking and finance.
The RLHF framework not only caters to quantitative adjustments but significantly emphasizes the qualitative dimension, fostering a continuous learning loop where human insights inform and enrich machine learning models.
Key Product Metrics - Extraction Accuracy and Analysis Accuracy
In shedding light on key product metrics and analytics monitored on the nRoad platform, Hrishikesh underscored the importance of data accuracy, extraction accuracy, and extraction completeness.
This bespoke approach ensures a tailored and effective measurement framework aligned with the intricacies of individual client needs.
Delving into the nRoad’s north star metric, Hrishikesh reveals that extraction accuracy and analysis accuracy, collectively termed ‘normalization,’ stand out as the north star metrics. These metrics serve as the bedrock of nRoad’s product evaluation, reflecting the industry standards and the essence of the product’s efficacy.
Challenges in Data Curation and Technological Evolution
This transformative journey is not without its hurdles, and the Hrishikesh sheds light on the multifaceted challenges faced by nRoad.
According to Hrishikesh, the challenge lies in striking the right balance – capturing a broad industry spectrum without introducing biases.
Technology’s relentless evolution constitutes another challenge. Hrishikesh expressed that continuous improvement and rolling out of newer stock is another challenge.
Moreover, the ever-pressing need for quick turnaround times adds another layer of complexity. Hrishikesh emphasizes building technology that not only meets the rigorous demands of continuous research and development but also adeptly navigates time constraints.
In essence, nRoad confronts and conquers these challenges through a harmonious blend of data expertise, technological agility, and unwavering commitment to advancement.
nRoad's Vision for the Future - Search-based Algorithms
Our interview with Hrishikesh sheds light on nRoad’s future trajectory, outlining plans to introduce cutting-edge methodologies in data representation, knowledge representation, and knowledge search.
In our exchange, Hrishikesh also addressed the pervasive discussions surrounding LLMs on social media. He emphasizes a notable gap in the ongoing discussions where people are oversimplifying by attempting to rely exclusively on LLMs for comprehensive problem-solving.
For organizations venturing into AI adoption, Hrishikesh cautions against relying on a singular solution to address complex challenges. Instead, he advises organizations to embrace a strategic amalgamation of various technological advancements.
Drawing from practical experience, Hrishikesh explains, “We have already fine-tuned and developed our own underlying open-source and LLM models. It has made things much easier, but it’s not the only thing that can be used to solve the problem completely.”
Hrishikesh says that this multifaceted approach, combining industry trends with internal research, is a critical strategy for achieving success in AI adoption.