If you know Danish LPs, you know week 7 means one thing: everyone's gone. Schools are closed, slopes are calling, and inboxes go quiet.
I'm taking a few days off to recharge with my family, which means this week feels like the right time to revisit something foundational - the GP evaluation framework I used for every single one of the 2,000+ manager meetings I took as an LP.
I originally shared this back in September when BeyondTVPI was just getting started and it went out to 162 readers. Since then, we've grown to nearly 900 readers, which means most of you haven't seen it. And honestly, this framework is the backbone of how I thought about manager selection for over a decade, so it deserves the spotlight.
We'll be back next week with fresh content. For now, enjoy the break - and if you're still working through GP decks this week, here's the framework that kept me sane.
My “boring” framework for evaluating managers
First off: I don’t think there’s anything groundbreaking about the framework I used to evaluate GPs. Most LPs will recognize the broad categories.
What counts is how you apply it - the discipline, consistency, and detail you put into each analysis.
For me, that meant putting every GP through the same lens, and scoring not only each area but also each sub-category on a simple 1–5 scale. It wasn’t scientific, but it made us continuously hone our judgement - and it revealed “how we really felt” about a manager, not just what the numbers or narratives suggested.
The development: This became especially valuable as traditional metrics grew easier to manipulate, making gut-check evaluation more important than ever.
The “perfect GP” (in theory)
A proven strategy with a history of top-quartile returns
A team that’s stable and motivated for the next decade
Terms that keep incentives aligned
👉 In practice? I’ve never found one that ticks every single box.
Two examples to ponder
Axcel (Denmark):
Their 2007 vintage fund is the stuff of legends. Pandora’s 40x return helped deliver ~5.5x net for LPs.
The brand name opened doors and guaranteed invites to every relevant process.
But: no one believes Pandora is repeatable. Strip it back and performance was heavily skewed toward one investment. Add partner retirements, and the story became as much about succession as returns.
Audax (U.S.):
Famous for its buy-and-build strategy. Fund VII raised $5.2bn and plans 33–36 investments - far above a typical mid-market fund.
The model spreads risk and creates a more even distribution of returns - too many deals for performance to hinge on a single outlier.
But: can the model scale consistently as fund sizes grow?
The tradeoffs
These are the kinds of tradeoffs that show up again and again.
That’s why I leaned on a simple framework as an LP. It guided everything - from the first pitch meeting to onsite diligence and ongoing monitoring.
The framework at a high level
📊 Performance: The enabler to go into any diligence for me. It is both a matter of absolute and relative returns, as well as the distribution of returns and a lack of dependency on outliers.
🎯 Investment Strategy: Focus, consistency and discipline in fund size. As one GP told me: “If we raise $2 billion , we have $5 billion of demand, if we try to raise $3 (billion), we have no demand.”
📈 Value Creation: Evidence of a repeatable playbook. Can the GP actually tell me how they create value, and do they have evidence of this?
👥 People: Ownership, deal attribution, and succession planning: It’s a 10-year commitment, so I need to know the people we back are the same ones who’ve done it before - and will do it again.
⚖️ Terms: Typically a fairly standard analysis. While we of course like lower fees, we know a good GP costs 2/20. But, some may have premium carry or a key person clause, that doesn’t reflect the team. Finally, the higher the GP commitment, the better.
In due diligence, we scored every fund we reviewed across these parameters
What we didn’t spend much time on
Not everything made it into the core framework. A few areas, we’ve often heard other LPs focus on, that we deliberately kept lighter touch:
Macro plays: If you can predict macro cycles, you can probably do it more cheaply in public markets than by paying 2 and 20 for a blind-pool macro bet.
Team dynamics: We’re not trained psychologists. Unless something was egregious, we didn’t go deep here. On the contrary, this is exactly where confirmation bias sneaks in - seeing ‘good chemistry’ in the teams we already liked, and the opposite in the ones we didn’t.
Okay I may have one example: Once, an IR person introduced himself as “Hi, I’m Tom’s IR guy”… whereafter Tom proceeded to talk uninterrupted for the next hour.
Personal chemistry: When I was investing money on behalf of the Danish nurses, we used to say: the nurses probably don’t care whether we personally like the GPs.
First time funds: First time funds wasn’t part of my investment mandate, and much of this framework won’t apply to first time funds.
Go deeper
This is the overview. Next week we’ll dig into the single most important part of the analysis: performance.
Not just whether a fund made money, but how it made money - and tools to quantify why one home run isn’t the same as a repeatable track record.
The bottom line
There’s no perfect GP. Every fund is a mix of tradeoffs. The key is applying a consistent framework that forces you to confront each item in your diligence with the same lens - every time and without exception.
Written by
Steffen Risager
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 totaling approx. $6bn across fund- and co-investments.