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Jerry Ting is the CEO and Co-Founder at Evisort Inc. He is a former Board Member at Harvard Law Entrepreneurship Project and Harvard Association for Law & Business and was an Account Executive at Yelp.

Episode Links:  

Jerry Ting’s LinkedIn: https://www.linkedin.com/in/jerryting/ 

Jerry Ting’s Twitter:  @JerryHTing

Jerry Ting’s Website: https://www.evisort.com/ 

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Outline: 

Here’s the timestamps for the episode: 

(00:00) – Introduction

(01:36) – Law is a super fascinating industry in the sense that it's one of the last ones to typically adopt technology. Nothing with automation, artificial intelligence, business intelligence. But then you go into the law firm environment or the legal environment, and then we step back 10 to 15 years in technology. Legal tech is one of those morphous terms that emerged recently, but it's a new wave of technology that addresses the question of how to make lawyers more efficient.

(04:23) – There's a really big market opportunity to both modernize and also look forward, bringing in automation and artificial intelligence to help an industry that provides a lot of value, but hasn't adopted technology in the way that financial counterparts have.

(06:14) – Law firms bill on an hourly basis. If you bring in tools that save 80% of time, that might not necessarily be all good for a law firm for an in-house counsel, for a lawyer at Microsoft, for a lawyer at, and name any big firm, they're driven by traditional business KPIs. Being more efficient, being able to help close deals quicker, removing roadblocks for sales and procurement. These are good things for in-house counsel. So we focus on in-house corporate counsel. 

(09:21) – It's actually easier to change technology than it is to change people's minds. We think we can provide legal services, whether it's tech-enabled or with alternative billing models. There is a large opportunity for disruption in the law firm space. 

(10:54) – Microsoft is an investor. And the Evisort part of why that's exciting is that almost 80% of our customers use SharePoint or Microsoft teams to store contracts in one way or another. One of the main use cases is taking data that already exists in the cloud and activating it using machine learning and AI. 

(11:45) – One is for helping accelerate deals, helping accelerate how quickly a sales team can close contracts. We can provide a layer of automation to review contracts for proof. The other one is vendor management. Being able to see across a billion dollar supply chain, software license agreements to be paid, to be cancelled, to automatically renew, all in a calendar format and visualizing it. And the third one is one that encompasses both of the previous, which is bringing data to lights. 

(12:21) – A centralized enterprise repository where, regardless of where your contracts are stored, sales contracts could be in Salesforce. Employment contracts could be in Workday. Vendor contracts could be in SAP Ariba, but one centralized place where management can go and find and run a report and gather insights about their contracts across the entire enterprise.

(13:18) – Our AI technology does a couple of things. We can take a scan of the contract that we've never seen before, convert it to a Word file and pull out over 50 different data points, including who the contract is with, when does it expire and what are the key legal terms. We can do that all today. From a content analysis perspective based on benchmark data, how to optimize this contract is the next level of intelligence. 

(15:44) – We understand what the customers need and then, we go to our research team and we already have models that we built that we'll test with. And most of them are deep learning models, a lot of research being done on natural language processing on computer vision. We test it on the existing models that we have. And then, if the accuracy is not where we need it to be, we start to tune that model and then add additional features.

(18:57) – We've invested a significant portion of our R&D budget in building out a proprietary dataset that now spans hundreds of thousands of labeled data points. And the modeling then follows that. But without a large enough data, you might be building a model for the wrong subset of data. It might be under a fitted model. We're creating training data that customers may not have ordered yet, but we know that as a phase two and a transformation project they may need.

(21:40) – Historically, contract management and AI vendors have focused on the things to do after you sign a contract. We recently announced a full collaboration platform from generating a contract, to negotiating it, to getting it approved, all assisted by AI. That's now available to all of our clients. We are the first company to go end-to-end from the creation of a contract all the way through renewal, all AI assistants all in one platform. 

(25:55) – There's a big difference between SAS companies and AI companies. Our idea is to combine the two. Combine deep AI analytics that were traditionally meant for large enterprises working with consultants. Democratize the AI that's easily digestible and verticalized for business function and then wrap it in a SAS platform so that anybody can use it. AI companies mature, they're going to build more end-to-end SAS platforms. And, it is going to be hard for the SAS platforms to build the AI capabilities. And that over time to merge into end-to-end SAS and AI platforms. 

(25:12) – The Bay area is world-class for scaling companies. The leaders and go-to-market and marketing and sales and customer success, product management, the go-to-market team in the environment that we have in the Bay area is hard to compete with, including New York. But New York is actually one of the main bases for customers. I try to get the best of all three regions, deep research out of universities in Boston, meeting with clients in New York, and then also running my office here in California.

(28:02) – To be a Forbes’ 30 under 30 has given us some credibility and some recognition for the work that we're doing. We were never doing this as a hobby, we always believed in the vision and our ability to execute and then being named to the Forbes list was a validation for the efforts that we had so far. And then shortly after Microsoft and Vertex and other VCs invested $15 million. The 30 under 30 was a way for us to go out to our colleagues, peers and say, take a chance at Evisort and join us. We're here working on something cool, something meaningful and something impactful.

(31:09) – What's happening a lot with verticalized AI applications right now is it's removing some of the tedious parts of a person's job, but it's actually making that person more effective in doing what they were supposed to do in the first place. I don't think AI is going to replace people's jobs. It's actually going to replace the points that people didn't want to do in the first place, so they can spend more of their time doing the strategic work.