AI-enabled portfolio data collection for faster valuations

AI-enabled portfolio data collection for faster valuations

Authors

Q Chen, Jordan Agnew

|

Read time: 

2 minutes

Published date: 

January 6, 2026

Carta’s new Portfolio Valuations solution is now powered by features that make collecting and standardizing portco data in order to calculate equity value fast and seamless.

Portfolio valuations has traditionally been a time-consuming and error-prone process for firms. Collecting the portfolio company data, along with standardizing them in order to ingest into the valuation calculations, can be a major bottleneck and pain point. 

Carta’s new Portfolio Valuations solution is now powered by AI-enabled features that make collecting and standardizing portco data in order to calculate equity value fast and seamless.

Use AI to create a summary cap table in minutes

When your portfolio companies use Carta for equity management, ownership data from their Carta cap tables are automatically piped into the Portfolio Valuations engine. And now there’s a fast and easy way to bring non-Carta cap table information into Carta so you can value all your portcos in one place: Introducing Cap Table Builder, leveraging AI. 

Whether  your non-Carta portfolio company is a C-Corp or LLC, with Cap Table Builder you can now:

  • Import unstructured data such as Certificates of Incorporation and Stock Purchase Agreements in PDF

  • Import cap table ownership data stored in Excel

Cap Table Builder

The Cap Table Builder can digest all imported data and convert it into standard format for Carta’s Portfolio Valuations engine to ingest. No more spending hours on manual data entering just to collect a small portion of the necessary inputs for valuations.

Q Chen
Author: Q Chen
Q is Director of Product Marketing at Carta with more than 15 years of product marketing experience, including six years in fintech. Q specializes in aligning customer needs with the solutions Carta builds to deliver clear, meaningful value.
Jordan Agnew
Author: Jordan Agnew

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