Research-driven, industry-sourced list derived from investors who have put several billion into the sector.

The methodology for the IA40 is based on Madrona's experience investing in founders and engaging with researchers involved in AI and Machine Learning for over a decade, and inspired by a research and survey methodology developed by Wing.vc. This list was compiled by and voted on by experts in the space: investors who are funding the next generation of intelligent application companies, technology companies and a leading investment bank.

Phase 1

Building the voting list

In 2022, we received over 250 private company nominations from over 50 venture capital investors from over 40 top-tier venture capital and investment firms. This year, we also partnered with PitchBook to further expand our nomination list by identifying the top-performing companies in the intelligent applications sector to ensure we were not missing out on any companies. Each judge had the opportunity to nominate companies for consideration in each category. The Madrona team vetted the companies to ensure they met the definition of intelligent apps.

Phase 2

Voting

After we collected the nominations, we asked the same venture capital investors to vote on their top 5 companies in each sector (early, mid, late, and enabler). Each venture capital investor was limited to voting for only 2 of their portfolio companies.

Phase 3

Tabulation

To tabulate the 2022 list, we then leveraged PitchBook’s “Venture Exit Predictor Tool.” Their model considers and weighs many data points, including total venture dollars raised, number of employees, and latest post-money valuation. We weighted the venture community votes and PitchBook’s Venture Exit Predictor Score to produce a final composite score that resulted in the 40 winners.

Defining Intelligent Applications

We define application intelligence as the process of using machine learning models embedded in applications that use both historical and real-time data to build a continuous learning system. These learning systems solve a business problem in a contextually relevant way - better than before, and typically deliver rich information and insights that are either applied automatically or leveraged by end users to make superior decisions.

Intelligent applications are:

Extracting and cleaning data from more than one source, and aggregating it in some form of a data lake/warehouse
Preparing the data and then using it to train and test machine/deep learning models
Refining those models and deploying them in applications to generate contextually relevant predictions, recommendations, inferences or insights.
These applications combine the above "dataware" and cloud-native software, built on a micro-services architecture, to deliver results in real time.
Applications enabled to continually monitor and improve the underlying models and predictions.
From an end user (or machine) perspective, leverage modern APIs and user experiences.

The Judges

Goldman Sachs

Scale Venture Partners

Decibel Partners

CRV

Revolution Ventures

B Capital

NEA

Merus Capital

Coatue

Positive Sum

ICONIQ Capital

Altimeter

Accel

Amazon

General Catalyst

Redpoint Ventures

OpenView

Playground Global

a16z

GGV Capital

Mayfield Fund

Norwest Venture Partners

Allen Institute of AI

M12

Canaan

Essence VC

Bain Capital Ventures

Khosla Ventures

defy.vc

March Capital

Madrona Venture Group

Bessemer Venture Partners

Index Ventures

Meritech Capital Partners

Acrew Capital

Greylock

Addition

Insight Partners

AIX Ventures

Sapphire Ventures

Menlo Ventures