👋 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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5 best practices of high-performing PE firms
With mid-market deals drawing 8 to 15 buyers and deal volume down 34% in H1 2026, a big network no longer gives PE firms an edge. The winners are converting the relationships they already have.
Join Niklas Krusche, Head of Origination at Armira, and Veronica Mora, VP of Customer Success at Affinity, on October 21 at 1 PM ET to learn how to spot a dormant network, make relationships belong to the firm instead of individuals, use introductions as a leading indicator of sourcing health, and stress-test your CRM for durability.
Last week, we ran the first DDVC Builders Hackathon in London, co-hosted with our partner HSBC.
15 builders. Three tracks: finding deals, assessing deals, managing deals. Three hours to build whatever the teams wanted. Six presentations with working prototypes.
Here's what stood out👇
What people came in with
The intro round alone was a snapshot of where funds are today:
Some had already automated parts of the investment process, from AI-assembled deal notes and data-room summaries to investment papers that IC members can question directly.
Others had automated admin work, such as keeping the CRM up to date from meeting notes.
Almost everyone's next goal was sourcing 2.0: seeing every relevant round in their space, and catching signals before a round happens.
Several wanted to put years of historic deal data to work as a benchmark for better decisions.
Investors inside larger funds pointed to a different bottleneck: approval processes that touch many internal functions and can take weeks or even months.
Finding deals
A "second brain" with a fence around it. One team built a dashboard that only reads from sources they trust: reports from other VCs, public bodies and consultancies, plus targeted scraping. They'd seen too many open-ended LLM searches return unreliable or invented results. The prototype scanned 300+ news items, kept what scored above a threshold, tracked how often focus areas and companies were mentioned, wrote briefs that link back to their sources, and pushed anything interesting to Slack. The trade-off: the thesis is built into the code, so a generalist fund would need more work to adapt it.
Finding companies before the data providers do. The third-place team started from lists of incubators, accelerators, university spin-outs and innovation grants. They scraped about 7,900 companies, then their LinkedIn pages, their people, and the posts those people wrote about other startups. That added roughly 7,000 to 8,000 more leads. From about 15,000 signals, the agent narrowed the pool to around 190 companies that fit their theses.
Three design choices stood out:
Every result is checked against the CRM. Each company is flagged as new or already known, with any existing relationship shown so the team knows who can make a warm intro.
Sourcing is kept separate from diligence, so early judgment doesn't filter the raw pipeline.
Claude wrote Python scrapers the team can rerun each week. Asking an LLM to scrape again next week returns a different format every time and costs more. A script gives the same structure every run, so you can see what actually changed.
The scoring discussion was just as interesting. For consumer deals, one member scores on hard data like web traffic growth, Instagram, Trustpilot and app store data. For deep tech, those numbers mostly don't exist, so the LLM has to reason from the thesis. The team also noted that switching models changes how the scoring behaves, even with the same prompt.
Never miss a round. Another team built a Monday digest. It collects the past week's rounds in fintech and AI infrastructure from Specter, adds rounds Specter missed, enriches each company, and routes each one to the person covering that vertical. Everyone gets a Slack message showing whether the company is already in the CRM and what to do next. A second step finds founder emails, matches them to the right thesis, and drafts outreach for a human to approve.
Mapping the LP landscape. One team mapped LPs, funds, commitments, co-investments and the people connecting them, filterable by stage, sector, geography and commitment size. It ran on mock data, and it prompted the most honest exchange of the afternoon. AI gets you 80 to 90% of a tool like this in a few hours. The last 10 to 20% might take another 30 hours. Before you start, ask what a finished, maintained version is actually worth to you, and whether you can buy it.
If this sounds interesting, check out LimitedPartnerList.com ;-)
Assessing deals
🥈 Second place: a benchmarking agent. This team built for two moments: preparing for a founder call, and benchmarking a company during diligence against the fund's own history and external data. The output is a dashboard with KPI comparisons, flags showing which data is verified, a score whose weights reflect confidence in each metric, and, most usefully, a list of questions to ask next. They ran it on real fund data, which impressed the judges.
Their two rules are worth stealing:
Compare like with like. Match benchmarks on business model, stage and time period. A SaaS company today shouldn't be benchmarked against one from five years ago.
Garbage in, garbage out. Add a gate that checks whether there's enough data to benchmark at all. At seed, where team and market dominate, there often isn't.
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Managing deals
🏆 First place: Deal Radar. This team built the closest thing to an operating system for deal flow. It started with a proper data model connecting every source, then added:
Routing based on expertise, geography, language, current capacity and existing relationships
A personal queue for every investor, with priorities, deadlines and to-dos, plus an admin view of who is overloaded
Inbox triage that classifies everything coming in. The demo correctly ignored a kindergarten invite.
Document parsing from intro emails straight into the CRM
A knowledge graph with an assistant you can query
Their sharpest observation was about ownership. Today, new deals land in a shared inbox, and as one member put it, nobody really looks at it. Once a deal has a named owner and a deadline, it gets handled.
They were deliberately cautious about scoring. Showing an AI score too early can bias people and make them stop doing the work. Their approach: humans assess first. Once a deal reaches the IC discussion stage, an LLM runs scenario models as a second opinion, and it does flag risks the team hadn't considered. Another participant scores entirely by hand, 1 to 5 after meeting the CEO, because an LLM can't see what happened in that conversation. Most agreed the right balance depends on stage. The earlier the stage, the more guesswork. The later the stage, the more a model can add.
What the judges debated
Deal Radar won because it pointed furthest towards where venture is heading: one connected system for running a fund. The judges also raised a question worth sitting with. Do funds really want everything in one place? Or do they want focused tools whose output is easy to check, with humans clearly in charge of judgment?
One judge put the market gap plainly. There are plenty of sourcing platforms and portfolio management tools. There seems to be no compliant, enterprise-grade agent layer built for private funds. Every fund in the room is assembling its own from separate pieces. Large groups add another layer of difficulty: tool approvals, security reviews, and mapping every workflow before anyone can start.
Five takeaways
Fence in your inputs. The strongest builds worked from sources the team chose.
Turn prompts into scripts for anything that runs every week to reduce costs.
Connect every output to the CRM and your network. A list of companies is only useful once it leads to a next step.
Give every deal an owner and a deadline. Shared inboxes are where deals go to die.
Clean your data before building agents on top of it. Several teams hit this wall within three hours.
Congratulations to the Deal Radar team, who won an annual “RUN” subscription to the DDVC Lab. Congratulations also to the benchmarking team and the pre-CRM sourcing team for second and third place.
A big thank you to HSBC for hosting us, keeping everyone fed, and sending every builder home with an awesome cap!
Want us to host the next VC Builders Workshop in your city? Reply to this email or reach out to [email protected], and let's make it happen ;)
Stay driven,
Andre & the DDVC Team
