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Welcome to another edition of our Sunday “Resources” stream where we share our most valuable data & resources across four rotating formats:
For 1. and 3., we collaborate with best-in-class partners to ensure you get the highest quality data. For 2. and 4., we leverage our ever-growing product portfolio and share selective snapshots of the most sought-after resources from The Lab.
At the end of this post, you'll find a unique deep dive on "The Compounding Data Layer: Building an Edge When Everyone Uses the Same AI Model" with Francesco de Liva (Founder & CEO, Kruncher), Meredith Parsons (Operating Partner, Offline Ventures), and Harman Bahd (Head of Data & AI, Eight Roads).
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Building an Edge When Everyone Uses the Same AI Model
I'm excited to share our latest virtual roundtable session, part of our monthly webinar series.
Every fund today can plug into the same AI models. The question the panel set out to answer is what still separates one investment firm from another once that's true.
Francesco de Liva is Founder & CEO of Kruncher, bringing 18+ years as a technical architect at Microsoft and Accenture to building the knowledge layer for private market investors.
Meredith Parsons is an Operating Partner at Offline Ventures, leading fund and portfolio operations from investment execution through LP reporting.
Harman Bahd is Head of Data and AI at Eight Roads, the Fidelity-backed global VC firm with $5B+ in AUM, where he leads data infrastructure and AI strategy across the firm.
Their shared conclusion: the company data most funds compete over is becoming a commodity. The real edge sits in the data only your own team can generate.
Watch if you want to learn:
The four layers of data most funds collapse into one, and why keeping them separate changes what you can trust downstream
Why a metric like ARR is meaningless without the value, source, and date attached to it
How Kruncher's "time machine" makes true apples-to-apples comparison possible across funding stages
The internal data asset most funds already have and never use: old investment memos and pass reasons
The three-tier trust hierarchy to decide which data sources are even worth integrating
A 500+ characteristic scoring framework built to stop AI from giving a different answer to the same question twice
The questions you should ask before writing a single line of schema or taxonomy
... and a lot more
Here’s the link to the full panel discussion👇
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