🔥Building Products That… | Data Driven VC Summary

Data Driven VC — 🔥Building Products That Investors Actually Use

Data Driven VC — 🔥Building Products That Investors Actually Use (2)

Data Driven VC — 🔥Building Products That Investors Actually Use (3)

1. Key Themes

VC firms are becoming software builders, not just software buyers

The traditional VC tech stack has evolved from basic tools into custom-built, in-house infrastructure because off-the-shelf solutions no longer meet firms' needs.

"What used to be a spreadsheet, a Salesforce instance, and a shared inbox has turned into stacks of dozens of specialized tools, in-house AI models, and increasingly, software that firms build themselves because nothing on the market fits the needs of the fund."

Internal adoption is the real test of product success

The session's core value proposition centers not on building tools, but on getting a firm's own partners to actually use them — a distinct challenge from external-facing product development.

"For this session, we wanted to go deeper with the people actually building these tools inside their own firms: what they built, why, and what it took to get their own partners to actually use it."

The market is bifurcating: commoditized tools vs. proprietary edge

There's a clear split emerging between categories where standard vendor tools suffice and categories where firms must build proprietary systems to maintain competitive advantage.

"including where the market's headed and which categories are getting commoditized versus where funds still build their own edge."

2. Contrarian Perspectives

Relationship density doesn't equal deal flow — most warm connections go unused

Despite VCs' emphasis on network and relationship-building, the vast majority of firm relationships never convert into actual introductions, suggesting the "spray and pray" networking model is inefficient.

"The 2026 Venture Capital Benchmark Report breaks down the activation gap (only 38% of firm relationships turn into intros), exposes where key-person concentration puts deal flow at risk..."

Key-person dependency is an underappreciated operational risk

Rather than diversified, systematized sourcing, many firms' deal flow is concentrated around specific individuals — a fragility that isn't typically discussed as a firm-level risk factor.

"exposes where key-person concentration puts deal flow at risk, and lays out quartile benchmarks across every major metric."

3. Companies Identified

645 Ventures — VC firm building internal engineering/product capabilities.

Why mentioned: Case study firm for the panel on building internally-used investor tools; notable for having a dedicated product leader who also invests.
Quote: "Lexi Quirk, MD & Head of Product at 645 Ventures, who oversees the firm's engineering team and splits her time between building and investing."

NFX — VC firm with dedicated internal tooling and data infrastructure function.

Why mentioned: Case study firm for the panel; represents fintech-to-VC product talent migration.
Quote: "Christopher Ruiz Chiu, Head of Product at NFX, a former fintech product manager who now leads the firm's internal tooling and data infrastructure."

Moonfire — VC firm building internal AI/ML products.

Why mentioned: Case study firm for the panel; represents ML-native talent building investor tooling.
Quote: "Jonas Vetterle, Head of AI & ML at Moonfire, who came from a machine learning background and now builds the firm's internal products alongside its investment work."

Affinity — CRM/relationship intelligence platform for VC firms.

Why mentioned: Sponsor and source of the 2026 Venture Capital Benchmark Report, providing data on nearly 3,000 VC firms' platform activity.
Quote: "Affinity dug into platform activity across nearly 3,000 VC firms to find out."

4. People Identified

Lexi Quirk — MD & Head of Product at 645 Ventures.

Why mentioned: Panelist representing a hybrid investing/product-building role at a VC firm.
Quote: "who oversees the firm's engineering team and splits her time between building and investing."

Christopher Ruiz Chiu — Head of Product & Data at NFX.

Why mentioned: Panelist bringing outside (fintech) product management experience into venture tooling.
Quote: "a former fintech product manager who now leads the firm's internal tooling and data infrastructure."

Jonas Vetterle — Head of AI & ML at Moonfire.

Why mentioned: Panelist representing the ML/AI-first approach to building investor products.
Quote: "who came from a machine learning background and now builds the firm's internal products alongside its investment work."

Andre Retterath — Author of Data Driven VC, moderator of the panel, affiliated with Earlybird VC.

Why mentioned: Host/curator of the newsletter and summit session.
Quote: "I'm excited to share one of the most watched sessions from the Virtual DDVC Summit 2026!"

5. Operating Insights

Measure ROI on internal tools before scaling them: The panel explicitly promises guidance on "what workflows to start building for and how to actually measure ROI" — implying firms should treat internal tooling with the same rigor as external product investments.
Adoption is governed by a specific process rule: There's "one process rule that separates a product from getting adopted or flopping on release" — signaling that internal change management/rollout process, not just tool quality, determines success.
Have an explicit build-vs-buy framework: Firms use "the build versus buy rule funds use to draw the line," suggesting entrepreneurs and operators serving VCs should understand where firms will never buy (the proprietary-edge zone) versus where they'll happily purchase commoditized tools.

6. Overlooked Insights

Security policy for AI connectors is being reduced to a single sentence: The mention of "the one-sentence security policy for data exposed through an MCP connector" hints at a lightweight, pragmatic approach to AI data governance emerging in VC firms — worth watching as MCP (Model Context Protocol) adoption spreads beyond engineering-heavy firms.
Quartile benchmarking is now available across "every major metric" of VC platform activity, per Affinity's report — implying a maturing infrastructure for firms to self-assess operational performance (sourcing, network activation, deal flow) against peers, a capability that didn't previously exist at this granularity.