Happy Thursday and welcome to another edition of Beyond TVPI.

For ten years as an LP, I sat on a dataset I never used.

Every manager we met had a track record in their deck. It got read for the meeting, then archived in a shared drive.

I knew the data was valuable, but I didn't have the time, energy or process to extract it.

Last week, I said year two would go deeper into diligence workflows and how LPs can use AI. This week is a first step: the three passes I would run on that data if I were still in the seat.

Enjoy the read,

Steffen

Note: Today’s edition is screenshot heavy so please click “download images” for this email to make the most of it.

An investment team meets far more managers than it invests in, and every one of them shows up with a pitch deck and a track record.

The deck gets read and then lands in a folder.

Unless you love data entry, that's where it stays.

I was an LP for ten years. In a perfect life, I would have built an internal benchmark from all this juicy data by hand.

I didn't.

Instead, I used Cambridge. But it always irritated me that I never knew who the underlying funds were.

The real benchmark I wanted was every fund we had sat across the table from.

What changed

Screening is our new AI-enabled workflow in FundFrame. You upload a deck, and about two minutes later a 40-page presentation is a GP's track record, with every number linked back to its source page. Every track record sits in one table that you can sort, filter, highlight and chart against your own benchmark.

Here is how I use it.

Pass one: flag the high performers

I sort all the funds by vintage and then by net IRR. That gives a natural break, with each vintage ranked on its own.

Then I add a highlight to spot the funds that also have a high TVPI and DPI.

I want the names where a strong IRR comes with a sensible TVPI and capital that has actually come home (ideally).

Pass two: plot the shortlist against the benchmark

I now have a shortlist. But a name on it may have one good fund, with the rest closer to mediocre.

So for every manager that stands out in pass one, I plot performance across as many vintages as the manager has raised. The benchmark is now our own data: the median, the quartiles, and the top and bottom 5% of funds. I use the built-in quartile chart in FundFrame and inspect it across IRR, TVPI and DPI.

A manager sitting in the first quartile across most vintages is a different proposition from one with a single standout fund surrounded by median results.

The first points to a repeatable process.

The second often points to one or two outlier investments in the same fund.

Pass three: check what fund-size growth changed

Finally, I look at fund size growth across the shortlisted managers.

I've written about fund size growth before. What I want to know is whether the managers are still playing the game that produced the numbers I just looked at.

I often made commitments with a step up in fund size of up to 100%, so this rarely rules a manager out for me. But a larger fund means larger investments, more of them, or both, and I would rather have that in front of me early.

What this gets you, and what it does not

There is a lot more you can do once you have the data. If you receive 100 GP presentations a year, you likely have the track records of 500+ underlying funds.

These three passes are where I start.

None of this replaces diligence. The screen only tells you who is worth the closer look.

The data entry no longer has to be yours.

The filter and the judgement still are.

The decks you need to get started already sit in your Outlook or a shared drive.

If you want to see what your own benchmark looks like, we run a free, guided trial of FundFrame.

Hit reply and let me know, or click here.

This newsletter is written by Steffen Risager, the founder of FundFrame, a platform for LPs to manage their private markets investments.

Before that, Steffen was CIO at Advantage Investment Partners, a Danish Fund-of-Funds.

Steffen has a decade of experience as an LP, and has made commitments totalling approx. $6bn across fund- and co-investments.