👋 Hi, I’m Andre and welcome to my newsletter Data Driven VC which is all about becoming a better investor with data and AI.
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Last week, during Bits & Pretzels, we hosted our DDVC Builders Breakfast in partnership with our friends at Vestberry in Munich.
From 100+ funds on waitlist, we picked 25 hand-curated professionals to join two hours of deep conversations about how AI and automation transform startup investing.
Here's a summary of my key takeaways👇
Sourcing and scoring
Fix the data before the scoring. A sophisticated score built on unreliable data can quickly cost you your investment team's trust. We learned this at Earlybird when we launched scoring too early. Once trust is gone, it is very hard to win back.
The good news: AI has compressed the plumbing. Entity matching and deduplication used to take years. Today, a one-week sprint quickly gets you beyond 90% accuracy, the same range that once took several years.
Measure coverage with a hit rate. Pick the peer funds you consider relevant and track their new investments through public registers. Your hit rate is the share of those companies you had already met. Show me the incentives and I will show you the outcome.
Weight what you can still judge. One fund built a thesis-fit score around five factors: team, momentum, market, business model durability, and pricing and terms. With moats so hard to predict right now, they significantly upweighted team and momentum. They were also candid that pedigree signals penalise underdog founders.
Make the score something people act on. A few mechanics shared by participants:
Two layers. Hard filters (geography, stage, company age, funding raised) come first. A second layer learns what each investor, and the team as a whole, likes, based on the reasons people give when they reject a recommendation.
Learn from the misses. When a great company slips through and you only hear about it from a peer, it goes into a structured review: what do these misses have in common, and why did the scoring drop them? Repeat regularly as success vs failure per company becomes more obvious.
One owner for the weights. Only engineering can change the scoring. Investors send feedback, which keeps the model from turning into a puzzle nobody can untangle.
Periodic reviews. Don't change the scoring every day but review batches of misses and survey the team on which characteristics matter most right now.
Treat scores as rough guides. A 76 vs a 81 of 100 deserve equal attention. A 40 vs a 76 of 100 do not.
Network graphs are the next frontier. Several funds see their collective network as one of their biggest untapped assets and want a warm-intro graph that surfaces the strongest path to any founder. The building blocks are there, but relationship data from email and calendars still needs to get more reliable before it can carry that weight.
After the deal
Portfolio data collection is a behavioural problem. Founders are busy, and sometimes they don't want to send numbers. Our partner Vestberry's answer is to automate collection as much as possible. Portfolio updates are CC'd to a dedicated email address, and AI extracts the data, attachments included, with accuracy they report at 97%+. With their MCP server, an analysis like follow-on vs. initial-ticket performance takes few minutes in Claude.
KPIs need a purpose. One investor asked what all this data does for DPI. His view: portfolio data matters when it helps you show up better for your portfolio companies and your LPs. He also doesn't think walled-garden tools that keep data inside their own UI are where the market is heading.
One question stayed open: how much should benchmarking data feed into decisions when benchmarks for recent companies change so quickly? Reply and tell me how you handle it.
Join 1,996+ investors in our free Slack group as we automate our VC job end-to-end with AI. Live experiment. Full transparency.
The operating model
Build permissions into the stack. Use a central MCP with role-based rules that decide who sees what. A partner can read colleagues' email content, while an analyst only sees that a partner was in contact with someone.
Put a review gate on skills. Anyone can submit one. Engineering checks it for data leakage and relevance before rolling it out firm-wide, and a weekly team call shows everyone what's new.
Protect your builders. LP data requests, DD support, or compliance questions quietly eat engineering capacity. Clear OKRs keep the team shipping and aligned.
Connectors are the unlock. One fund in a larger regulated group ran an enterprise chatbot without connectors for a long time. Since connecting their tools, people there describe being able to do about 5x more. Their next step is a dedicated CTO-level hire, because an investor building on the side eventually hits a ceiling.
Top ROI workflows
Memos and LP updates. Memos that took days now take hours, built from data sources, CRM notes and call transcripts, and updated after every founder call until the IC. One fund now often runs Series A and B deals with two people instead of three. LP updates follow the same pattern, drafted from board decks and portfolio data, and they only work when that data is accurate.
The catch: memos nobody reads. One attendee said that since their memos became AI-assembled, people are reluctant to read them in full. Another fund shared a fix. Their own analysis of the past 12 months found over 80% of the content in their IC papers repeated information from decks, data rooms and websites. Now deal-team commentary on key risks and "what we need to believe" sits in a different format in every section, so readers can jump straight to what the humans think.
Where the edge and real alpha moves
Most of the stack is or will be commoditized. The room pointed to what remains:
Proprietary first-party data: transcripts, memos, and above all portfolio data. Individual IC ratings let you look back at each partner's decision quality.
Judgment: it takes many repetitions, and some people simply have it. Two investors can leave the same meeting with opposite reads of the same founder.
Access: personal compatibility with founders, your personal track record and brand (which compound slowly), and the firm brand, which is really the sum of individual brands.
Network leverage: a platform one or two hops away from almost anyone in its market has an advantage that's hard to copy.
Founder value-add: as technology democratizes, venture becomes more about brand and looks more like sales. Using the fund's internal knowledge, like the right intros or structuring an M&A process at exit, is part of that pitch.
Every big brand I've spoken to is doing something here. Many just aren't allowed to talk about it.
Over coffee
Transcribe everything? One investor now transcribes most of his meetings by default, internal ones included, and switches it off for the few that shouldn't be recorded. His compliance team requires the other party to know they're being recorded. How are transcriptions handled at your firm?
ICs are predictable. Knowing each partner's preferences, you can often forecast the vote. Votes also shift with energy and mood.
Walled gardens are slowly opening. Pressure from customers is pushing more data providers to offer MCPs and integrations.
Sunk cost is real. Many funds are stuck with an average in-house tool because it's someone's baby. Buy when something better exists.
The through-line
Since our last breakfast in Berlin in June, the conversation has moved from adoption to operations. Who can change the score? Who sees which data? Who triages the top 100? Which parts of the memo are written by humans? These are organisational questions, and the funds at this table are actively working through them.
Thanks to Vestberry for partnering on the breakfast. They were one of DDVC's first partners, and we've been happy customers at Earlybird for years. Thanks also to everyone who joined in the middle of the Bits & Pretzels and Oktoberfest rush. You rock <3
Join our next DDVC Builders Breakfast at Slush in Helsinki on November 18th, together with Harmonic and Goodwin. Apply here.
Stay driven,
Andre
