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With access to millions of public and off-market data points, Metal has built an investor memory layer that deeply understands the investing patterns, inclinations and behaviours of venture investors.

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Underlying Data Sources

Investor memory draws on data from a broad set of sources explained below. For data ingested via each source, the platform has strong QA processes built in to ensure a continuous self-improvement mechanism.
By partnering with leading industry APIs, Metal has access to structured data on investment rounds from the past 15 years. With each API partner, the platform pulls data that is verifiable and reliable, and avoids using data points that are not particular strengths for that partner.
By accessing data from a broad mix of sources, the platform is able to gather accurate data and use that to build a nuanced understanding of venture investors. With each passing quarter, as the proprietary intelligence grows, the platform experience improves automatically for all users.

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Structured Insight via the Memory Layer

Equipped with proprietary intelligence on venture investors, the platform draws on large data sets to build structured insight into the investing patterns, inclinations and behaviours of venture investors. For institutional investors, this is managed separately for investments firms and for partners. For most VC firms, the platform holds structured insight into what they seek:

Business Models

Business models or market theses is the firm interested in or excited about.

Founder Backgrounds

An understanding of the types of founder backgrounds that the firm backs.

Process Insights

Insights on the investment process for a given firm (time to decision).

Key Patterns

Specific patterns that emerge in their recent investments.
The above are a few examples of the the of structured insight that comprises the investor memory layer.

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Application of the Investor Memory

Across the platform, the investor memory layer brings workflow intelligence to the following capabilities:

Pre Meeting Briefs

Intelligence on likely questions and focus areas for an upcoming investor call.

Post Meeting Intelligence

Intelligence on what went well versus can be improved after an actual investor call.

AI Mock Calls

AI-led mock calls that mimic the investing inclinations of the selected investor.

Data Rooms

Insight into what needs to improve for pitch decks or round collateral.

Relevance Ranks

Relevance ranking of investors based on investor and user memory layers.

Relationship Intelligence

Insight into connections that are connected with a given investor.

Investor Updates

Usage of investor memory to recommend improvements to investor updates.

Investor Pipeline

Proactive recommendations for new tasks or investor additions to the pipeline.
With proprietary data driving workflow intelligence, the platform is able to enable founders in novel ways with insights on how the round is progressing, or with suggestions on areas that need to be re-evaluated.