Munich Re Ventures (MRV) is the strategic venture capital arm of Munich Re, a global re-insurance and primary insurance provider. MRV invests across industries that affect the broad interests of the insurance industry including mobility, cyber security, and IoT. Notable investments include Next Insurance, Hippo, and Augury.

MRV has been a Standard Metrics customer for years. We first wrote about that relationship in our “Founder-Friendly Portfolio Management” case study, which covered how the firm moved off manual, spreadsheet-based portfolio tracking and standardized reporting across its funds on Standard Metrics. This time, we sat down with Michael Feeley, MRV’s Finance Manager, to talk about the next chapter: how the firm now uses Claude to build tailored portfolio reporting workflows and artifacts without technical know-how.

The Problem

Standard Metrics had already solved the data collection problem for MRV, moving the team from stale, hardcoded spreadsheets to a centralized system of record. What remained was turning that centralized data into insight without a lot of manual production work in between.

Building a dashboard was time-consuming and didn’t ensure adoption from the broader team after build. And ad-hoc answers required extensive back and forth and often served as just another repetitive task in a long list of others.

“Senior management could fire off an email saying ‘hey, what’s our ownership percentage of every company in this sector?’ And then I’d have to add that answer to my list of to-dos, get to it as soon as I could, and then send them an email back once it was done,” Feeley said.

And some information like maturity dates and terms on convertible notes still lived nowhere in particular despite attempts to centralize information on tools like Excel sheets or Monday boards. “It was either in certain people’s heads at the firm and not anywhere else, or it was maybe in your inbox and you had to go digging for it if somebody asked the question,” explained Feeley.

The Solution

MRV runs its portfolio data through Standard Metrics, supplemented by custom columns for anything that doesn’t fit a standard field — sector, board presence, exit year, even the latitude and longitude of each portfolio company’s headquarters. Feeley then uses Claude, connected to Standard Metrics via MCP, to turn that data into whatever the firm needs next, without manual labor, one-off back-and-forths, or complicated code.

“We use Claude at Munich Re Ventures, and I think as they’ve kept making progressive jumps in the model, it’s become easier and easier to just ask questions and pull the information out of Standard Metrics in Claude versus having either static dashboards or dashboards that have to be updated and maintained,” said Feeley. “Maybe there’s an ad hoc request of, ‘hey, what were the pre-money valuations for these companies for the last three or four rounds.’ In response, you can just ask Claude to ‘put together this table for me and format it this way in Excel,’ and it’s done in 30 to 60 seconds.”

That same connector let Feeley build a co-investor network graph, or a visualization mapping which firms MRV has led or co-invested with across its portfolio. “I had been working in Python trying to figure out how to do this for a while. It was taking a lot of work,” Feeley said. “But instead, once the connector with Standard Metrics came along, then I could use Claude to just basically have a conversation, iteratively talk through how we were going to build this thing. With that connector, I was able to jump over that Python learning hurdle, and it probably took less than 10 minutes to build all together.”

Every quarter, as portfolio data comes back into the firm, Feeley now also asks Claude to comb through the batch for anomalies or unusually large changes in metrics like burn or op-ex before reviewing himself. Claude sorts its findings into red, yellow and green categories (e.g. most alarming to least) so now Feeley can spend his time investigating the handful of real red flags instead of checking every company by hand. “It’s been helpful to have companies that have something going on bubble up to the top,” said Feeley.

The same approach has also improved MRV’s convertible note monitor, which tracks when maturities in convertible notes are coming up. MRV stores information about maturity dates, valuation caps, and interest rates in Standard Metrics and pulls out that information via Claude. The team has a dynamic dashboard that flags when a note is due in advance so they can make decisions like extensions or calling the debt with “all the data they need to make decisions quickly.”

The Results

For Feeley, one real win is in how his time gets spent. “Too often you’re crunched for time, you’re putting something together, and your review time is 4 to 5% of your work, and the 95% is the production part, and you really want that review and analytics part to be the 90%,” he said. “That’s where Claude comes in. Claude will take that production piece and then let you do the analytics and thinking.”

Feeley trusts these workflows because the private company data running them is held to the same accuracy standard as data from public companies.”When I go to Bloomberg and I look up Apple’s revenue, I don’t wonder if that’s the right revenue number,” he said. “It’s the same with Standard Metrics. The data is clean, correct, and I don’t think about it. It’s just feeding everything else.”

The ability to do this sort of analysis is also more readily available to non-technical users at the firm who simply have to prompt an LLM to get the portfolio answers they need. “Claude and the Standard Metrics connector are just tearing down the barriers to entry to doing these kind of things,” said Feeley.