Bodhala CEO Raj Goyle Speaks with Rhys Dipshan of LegalTech News

Raj Goyle recently spoke with LegalTech News about data and AI in the shifting legal sphere.

Attorneys have turned to artificial intelligence to reduce costs in tasks like e-discovery, contracts and due diligence. But more recently, AI has been getting to the heart of the matter, fueling legal spend analytics platforms that help law firms and legal departments better understand and change their spend habits.

One of the latest entrants into this market is Bodhala, which aims provide deeper insight into industry spend patterns using big data analysis. Legaltech News spoke with Raj Goyle, co-CEO at Bodhala, on how the platform’s AI works and how Bodhala aims to compete in a fast growing market.

What It Is: Bodhala is a legal spend analytics platform that analyzes e-billing invoices and other legaldata to provide insights into legal procurement market trends.

Essentially, the platform empowers legal professionals to make strategic hiring and financial decisions by comparing their spend to industry standards. For example, a legal department can tell if they are overpaying or inefficiently procuring outside counsel in a particular practice area, such as M&A work.

 Goyle explained that such insights can help “an in-house department to make their legal spend efficient, and there is also a use case for a law firms to make a data-driven pitch to win business from law departments and also manage their firm more strategically.”
Under the Hood: To get to these insights, Bodhala analyzes both private e-billing data it pulls from its legal department and law firm clients, and public legal data it gathers from a variety of sources.

Goyle noted that the platform is tracking over “a million matters and over 5,000 law firms,” which translates into over “a billion dollars of billing invoices.”

He added that such private data is combined with curated public information such as “news articleinformation about law firms, [information on] law firms’ business models, and their leverage ratios,” as well as other financial data.

Bodala then uses artificial intelligence to “read” and find patterns across all the private and public data, thereby highlighting legal spend and procurement trends.

Clean Data: Since Bodhala uses AI to perform high-level big data analysis, and not to primarily help clients better clean and classify their invoice data, to some extent, its analysis relies on clean data from law firms and legal departments.

“The cleaner the data the better,” Goyle said. But he added unorganized spend data hasn’t been a problem for the Bodhala given that the platform’s “algorithms have become so strong that we are able to largely get beyond the ‘dirty data’ issues since the algorithms can make sense of it.”

He added, “And in our experience, generally the data at law firms and companies is much cleaner than people think.”

Competition: Bodhala is one of several AI-powered legal spend analytics platforms on the market. Wolters Kluwer’s LegalVIEW BillAnalyzer, for example, uses AI to flag invoices which may deviate from specific billing amount limits and classification standards. The tool therefore, allows legaldepartments to easily highlight overcharges in invoices and track overcharge trends.

In addition, Brightflag offers a legal spend analytics platform that uses AI to “read” invoices’ narrative descriptions, which are written by attorneys to describe the charges in the invoice, and properly classify them into predetermined categories. From there, the platform extracts invoice data into analytics to help legal departments understand where their spend goes and how it has changed over a specific time period.

When asked how Bodhala compares with these platforms, Goyle said that the competitors’ “approach is narrow compared to what we are doing. So the basic approach in other companies, as we understand it, is to really sort of say, we are going to look at some narratives and see if there is a mistake in there. That’s very different than looking at big pattern analysis.”

2017-09-22T20:34:31+00:00

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