Insights on the Build vs. Buy Debate from Angelini Ventures at Venture Intelligence Day.
For venture capital firms committed to the data-driven operating model, the debate over building bespoke technology versus buying off-the-shelf solutions remains a defining strategic choice. Angelini Ventures, a healthcare-focused VC with a €300M commitment, provides a practical case study of how to navigate this tension through its dedicated Digital Pillar initiative.
Founded in 2023, the Digital Pillar was designed not just to build analytics solutions, but to reshape the fund’s overall culture toward being data-first, addressing how traditional VC networking is often biased, inefficient and time-consuming.
The Build vs. Buy Equation in Practice
Angelini Ventures’ experience highlights the strategic trade-offs that VC operations and finance teams constantly face:
The ‘Buy’ Case: Efficiency and Independence. The firm found that outsourcing a software-as-a-service platform for deal flow management significantly increased the investment team’s efficiency. This approach saved internal data science resources by allowing investors to operate independently for common workflows and reporting tasks.
The ‘Build’ Case: The team recognized that some strategic objectives required ownership over the data and logic. To improve their deal sourcing and relationship strategy, they developed an internal investor ranking system. This proprietary tool helps the team prepare for conferences and travel by prioritizing which investors and startups they should connect with. Other internal tools include an automated exit report and a classification system that uses GPT to classify companies against the firm’s investment thesis.
The firm is now grappling with the next frontier: deciding how far to extend its internal build strategy versus when to adopt emerging, specialized AI solutions rapidly coming to market.
The Foundational Challenge: Structured Data
Regardless of where a fund lands on the build vs. buy spectrum, every strategic solution—from proprietary ranking systems to RAG-based chatbots—relies on the same prerequisite: a clean, reliable, and consistently structured data foundation.
Angelini Ventures utilizes an infrastructure that combines external data, internal documentation (unstructured data), and a centralized knowledge base (structured data). They feed all this into custom solutions.
However, as the team pointed out, making these custom tools truly effective often hits a snag at the foundation layer: consistently integrating the fund’s structured data (like portfolio KPIs and cap table information) into the broader knowledge base.
This highlights a critical operational necessity for every fund: data standardization. Before any powerful algorithm can deliver its value, the core data must be accurate, standardized, and easily accessible. Data standardization isn’t a nice-to-have; it’s the prerequisite for every strategic build or buy decision.
Vestberry actively supports these community discussions at events like Venture Intelligence Day. By automating and standardizing the structured data foundation – from investment documents and cap tables to KPIs tracking and reporting -Vestberry ensures that operations and finance teams in VC firms have the clean, trustworthy data required to power any bespoke tool or off-the-shelf solution effectively.
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