Turning Questions Into Quantifiable Edge
Turning Questions Into Quantifiable Edge
Turning Questions Into Quantifiable Edge
Centralized data discovery for every stage of the buy-side workflow
Let Investors Find Your Datasets Through Proven Results
Make Research Time Count
Make Research Time Count
We simplify discovery and validation so you can focus on performance
We simplify discovery and validation so you can focus on performance
Signal Discovery
Simultaneously scan millions of features and identify potential signals in minutes
Signal Discovery
Simultaneously scan millions of features and identify potential signals in minutes
Pre-Trial Access
Validate datasets in your unique investment context before committing resources
Pre-Trial Access
Validate datasets in your unique investment context before committing resources
Scalable Testing
Keep pace with growing data volume by running more tests without added operational strain
Scalable Testing
Keep pace with growing data volume by running more tests without added operational strain
Signal Discovery
Simultaneously scan millions of features and identify potential signals in minutes
Pre-Trial Access
Validate datasets in your unique investment context before committing resources
Scalable Testing
Keep pace with growing data volume by running more tests without added operational strain
Thesis-Driven
Data-Driven
Quanted Query
Discover Data That Validates Your Ideas
Natural-language search that connects your investment ideas to
relevant datasets
STEP 1
STEP 2
STEP 3
01
Search for Features
Query the data lake using a natural language prompt or upload a research paper or investment thesis as a CSV to find relevant features.
Thesis-Driven
Data-Driven
Quanted Query
Discover Data That Validates Your Ideas
Natural-language search that connects your investment ideas to
relevant datasets
STEP 1
STEP 2
STEP 3
01
Search for Features
Query the data lake using a natural language prompt or upload a research paper or investment thesis as a CSV to find relevant features.
Thesis-Driven
Data-Driven
Quanted Query
Discover Data That Validates Your Ideas
Natural-language search that connects your investment ideas to relevant datasets
STEP 1
STEP 2
STEP 3
01
Search for Features
Query the data lake using a natural language prompt or upload a research paper or investment thesis as a CSV to find relevant features.
See How Quanted Is Helping Funds Discover Validated Data
See How Quanted Is Helping Funds Discover Validated Data
See How Quanted Is Helping Funds Discover Validated Data
See Results On Your Use Case
See Results On Your Use Case
See Results On Your Use Case
The fastest path to relevant data starts with
a discovery call
The fastest path to relevant data starts with
a discovery call
The fastest path to relevant data starts with a discovery call
B2B Data
+13%
B2B Data
+13%
B2B Data
+13%
Interest
Interest
Interest
ESG
ESG
ESG
Sentiment
Sentiment
Sentiment
Population
Population
Population
Transactions
+5%
Transactions
+5%
Transactions
+5%
IoT Devices
IoT Devices
IoT Devices
IoT Devices
IoT Devices
IoT Devices
Macro
Macro
Macro
Commodity
+3%
Commodity
+3%
Commodity
+3%
Trends
Trends
Trends
Equities
+8%
Equities
+8%
Equities
+8%
Investment Model
What Funds Ask Us
What Funds Ask Us
Topics that frequently come up in conversations with funds
Topics that frequently come up in conversations with funds
What’s the difference between your thesis-driven and data-driven products, and what’s required to get started?
How can I trial your products and evaluate their fit for my use case?
How do you ensure statistical robustness and mitigate common modeling biases?
Can the system support complex or cross-sectional model architectures?
How do you qualify and audit data vendors?
Do you use large language models (LLMs) in your products, and are they trained on client data?
What deployment options are available, and what security measures do you take to protect client IP and data integrity?
What’s the difference between your thesis-driven and data-driven products, and what’s required to get started?
How can I trial your products and evaluate their fit for my use case?
How do you ensure statistical robustness and mitigate common modeling biases?
Can the system support complex or cross-sectional model architectures?
How do you qualify and audit data vendors?
Do you use large language models (LLMs) in your products, and are they trained on client data?
What deployment options are available, and what security measures do you take to protect client IP and data integrity?
What’s the difference between your thesis-driven and data-driven products, and what’s required to get started?
How can I trial your products and evaluate their fit for my use case?
How do you ensure statistical robustness and mitigate common modeling biases?
Can the system support complex or cross-sectional model architectures?
How do you qualify and audit data vendors?
Do you use large language models (LLMs) in your products, and are they trained on client data?
What deployment options are available, and what security measures do you take to protect client IP and data integrity?