YC - Andrew

Question 1

What progress have you made since your application (then you had 2M cases, tens of millions of documents, and nearing MVP)?

Answer 1

We have completed our MVP, and identified a number of potential investments, and have reached out. Now iterating

Question 2

You said prototype/beta is coming up from November 1st - where are you at? On track?

Answer 2

We’ve already made a couple preliminary models, so this is actually already done. Now we’re just iterating and feature engineering to improve. Now we are pushing towards an investible model Dec 1.

Question 3

You said your advantage is driven by your combination legal and quant finance and your ability to invest in cases below $500K, lower than nearest competitor. Why do these give you an advantage?

Answer 3

These advantages allow us to successfully build the technology (and the portfolio). These enable us to access these lower cases and enjoy a better cost structure generally

Question 4

You talk a lot about cost-effectiveness in your app - where is this now?

Answer 4

With higher precision technological models, we don’t need as much human diligence. Also more efficient capital deployments

Question 5

How do you justify lower rates to LPs?

Answer 5

We have higher precision and more efficient capital deployments, leading to higher returns. LP stay in yo lane and enjoy your returns

Question 6

You talk about understanding NLP as being how you think about this differently. How would this work? How do you know it’s possible?

Answer 6

Abstractly, court documents contain a ton of information about a case. More concretely, I’ve read successful research papers on similar tasks, and we plan on being more sophisticated than what I’ve read.

Question 7

Is there no one else doing something similar?

Answer 7

Legalist and LexShares are the two other players investing in smaller cases. Both are using relatively simple models as a filtering system, and their average case sizes is well above what we’re targeting
Legalist average investment size is $500K, Lexshares is average ~$1.5M, both have been trending up.

Question 8

How is your product different?

Answer 8

Quant-driven approach. Lower cost. Smaller case sizes. Higher precision.

Question 9

What competition do you fear most?

Answer 9

Legalist if they decide to hire sufficient talent and revamp business model. Same for larger players.

Question 10

Who are your competitors?

Answer 10

Legalist most directly. Other large firms like Burford, Lake Whillans

Question 11

Who might become competitors?

Answer 11

Big players may all become more direct competitors with tech investments

Question 12

What is your growth like?

Answer 12

Had 244 customer conversations. We recently identified a segment of the market that seems very promising. Technical things that have improved.

Question 13

How large is the market that you are going after?

Answer 13

(I think this is the same as below…but) $640M/year in recoveries in our narrow NY cases. 10B if you extrapolate US (by population)

Question 14

How many people are in your target market?

Answer 14

33K cases per year for contracts in our population in NY. 500K est. in US., and this is the subsegment of contract cases which we are finding traction in

Question 15

How will you validate?

Answer 15

Will try to raise with backtest. If not, we’ll invest smaller amounts with our own money

Question 16

What have you learned so far from working on your product?

Answer 16

Dataset is highly particular, requiring significant domain knowledge to parse

Question 17

What’s new about what you make?

Answer 17

Quant approach, including NLP. Lower case sizes. Heavier reliance on tech.

Question 18

How does your product work in more detail?

Answer 18

3 data sources, advanced ML techniques, quant approach to the whole portfolio

Question 19

What are you going to do next?

Answer 19

Build robust model, get funding, invest in cases

Question 20

What part of your project are you going to build first?

Answer 20

Model and investing mechanism

Question 21

What is the next step with the product evolution?

Answer 21

Iteration on model. More feature engineering. Next are NLP iterations

Question 22

Where is the rocket science here?

Answer 22

Quant investing applied to law; litigation domain knowledge pairing

Question 23

Why is your product better than Legalist?

Answer 23

We believe Legalist’s models are simpler than ours, as they appear to be focused on building arrays of data largely based on metadata. Our additional tech (focused on language models/NLP) allows us to be more precise in our analysis of case outcomes, which allows us to invest in < 50K investments.

Question 24

Why do you think Legalist’s models are simple?

Answer 24

From their technology team, description of thier process in a June 2020 SEC filing, and all the other research we’ve done.

Question 25

How is your solution better?

Answer 25

Better models means lower costs, higher precision. Better portfolio management means higher returns on capital and less risk.

Question 26

Do you have proprietary data?

Answer 26

No, but as we fund cases we will build a proprietary database

Question 27

How are you analyzing settlement values?

Answer 27

Estimating settlement values based on similar cases, using full data picture at the time of settlement

Question 28

Demo?

Answer 28

We don’t have a demo because there is no user interface. We’re happy to show you anything you want to see about our model / backend.

Question 29

Algothmic Bias

Answer 29

We actually see this as an opportunity. First level, debias the corpus, but in the best version we can actually confront the counterfactual: what if disadvantaged plaintiffs had been given funding?

Question 30

What has been your progress over 6 months?

Answer 30

AH: Over the past four months on the technology side we’ve built scripts for blah blah blah. And during that time, we’ve diligenced hundreds of specific cases that have been identified by our algortihms, and reached out to the lawyers and plaintiffs in those cases.

Question 31

Why would you stick together?

Answer 31

We are each emotionally invested in solving this problem for personal reasons, we’ve already handled adversity, and through working together we have developed a deep level of respect for each other. Hell, we all moved to NYC during a pandemic to be together! (If the q is directed at Andrew or wrt ClassWise, mention the dedication levels of the founders.)

Question 32

Tell us about a tough problem you solved?

Answer 32

Technical stuff.

Question 33

Tell us something surprising you have done?

Answer 33

Living together. We used GPT3 generated phrases to generate our website.

Question 34

What’s an impressive thing you have done?

Answer 34

Current tech pipeline to get to multiple input model

Question 35

Why did your team get together?

Answer 35

We are also data guys, and this is one of the coolest problems out there. But more are really passionate about fairness as we have been personally impacted by the high cost of legal proceedings.

Question 36

Why did you quit ClassWise? Why will you stick this one out?

Answer 36

ClassWise failed fast. Weren’t even able to help schools for free. Team didn’t include domain experts and also wasn’t as dedicated as Patronus.

Question 37

Funders preferred explainability over predictability. What are the implications of building something like this for the legal system where results are unexplainable?

Answer 37

Explainability functionality. Counterfactual. See a lot of this as an opportunity. Partial dependence plot. LIME. I don’t think we’ll get this question

Question 38

How would you build a moat?

Answer 38

Building a pipeline of law firms. Over time we’ll also have proprietary data. (And also it’s hard)

Question 39

Bottom-up cases

Answer 39

Each case is avg 20K/year after paying our investors. So 50 cases = 1M. 5000 cases is 100M. And there are 200K cases every year.

Question 40

First hire

Answer 40

Engineer for expanding into new states and helping with model

Question 41

Tell me about your quant trading background

Answer 41

Built a new quant trading desk in a new asset. Involved data science, algo development, product management, portfolio optimization.

Question 42

If legalist decides to compete with you, why will you win?

Answer 42

The founding team. Sure, they have the resources to make hires, but it matters that the founding team has serious deficits in Data Science, Litigation, Ops, and Finance. That’s a lot to make up for.