AI Customer Prediction Platform

What is faraday.ai?

Faraday.ai is a consumer prediction platform designed to facilitate intelligent and efficient business growth through data science and machine learning. It empowers users to embed AI predictions seamlessly into various workflows and tools, including marketing, analytics, and platforms. Additionally, Faraday.ai offers built-in consumer data, responsible AI features, and flexible integration options, enhancing its usability and effectiveness for businesses.

How does Faraday.ai ensure data privacy?

Faraday.ai prioritizes data privacy and employs several measures to safeguard user data and ensure compliance with pertinent laws and regulations:

1. Data Usage: Faraday.ai utilizes your data exclusively for generating predictions tailored to your business needs and refrains from sharing it with other accounts or third parties without your explicit consent.

2. Security Standards:
     - SOC 2 Type II Compliance: Faraday.ai adheres to stringent standards encompassing security, availability, processing integrity, confidentiality, and privacy.
     - HackerOne Program: Through a penetration testing and bug bounty initiative, Faraday.ai invites ethical hackers to assess its systems for vulnerabilities.
     - NIST 800-53 Risk Management: Faraday.ai implements a comprehensive risk management program to identify and mitigate potential operational and data risks.

3. Regulatory Compliance: Faraday.ai complies with prominent regulations including the California Consumer Privacy Act (CCPA), the Health Insurance Portability and Accountability Act (HIPAA), and the European General Data Protection Regulation (GDPR), safeguarding user rights regarding data access, deletion, and opt-out of data sales.

4. Data Encryption: Faraday.ai employs encryption protocols to secure user data both at rest and in transit, thwarting unauthorized access or interception.

5. Google Cloud Platform: Operating on the Google Cloud Platform, Faraday.ai benefits from the robust security and reliability inherent in Google's infrastructure and services.

For further insights into Faraday.ai's security and privacy practices, users can explore their dedicated security and privacy pages.

How does Faraday.ai generate predictions?

Faraday.ai employs a comprehensive process to generate predictions, integrating your own data, third-party data, and machine learning techniques. Here's an overview of how it operates:

1. Recipe Selection: Users begin by selecting a recipe, which defines a predefined or custom prediction task such as propensity, persona, or forecast.

2. Data Source Integration: Next, users connect their data sources, which may include CRM, analytics, or platforms, to incorporate historical customer data into the prediction process.

3. Data Enrichment: Faraday's platform enriches user data with its built-in consumer data, augmenting it with hundreds of demographic, interest, and behavioral attributes.

4. Objective Definition: Users define prediction objectives, specifying the outcomes they aim to predict, such as buying, upgrading, or churning. An intuitive interface facilitates the mapping of data to prediction objectives.

5. Pipeline Deployment: Users deploy a pipeline, an automated process that handles tasks such as model training, testing, and updating. Deployment options include integration with various tools, workflows, or platforms.

Faraday.ai manages all technical aspects of prediction generation, including data cleaning, feature engineering, model selection, and evaluation. Users can conveniently monitor and manage predictions through a user-friendly dashboard.

How accurate are the predictions of Faraday.ai?

Faraday.ai employs several methodologies to ensure the accuracy and reliability of its predictions, validated through rigorous testing and evaluation processes:

1. Model Calibration: Faraday.ai adjusts predictions through model calibration to closely align with actual outcomes. Probability scores reflect the likelihood of individuals achieving specific outcomes, providing users with actionable insights.

2. Holdout Tests: To assess generalization capabilities, Faraday.ai conducts holdout tests, comparing predictions against a separate subset of data not used during model training. This evaluation method gauges how well predictions extend to new and unseen data.

3. Multiple Models: Faraday.ai utilizes an ensemble of models, leveraging diverse data types and algorithms to enhance prediction robustness. Models are weighted based on performance and relevance, resulting in more comprehensive and reliable predictions.

Additionally, Faraday.ai furnishes users with model reports containing various metrics and charts to evaluate prediction quality and accuracy. Users can conveniently monitor and manage predictions through an intuitive dashboard interface.

How much does faraday.ai cost?

Based on available information from web search results, Faraday.ai offers diverse pricing plans tailored to various needs and preferences:

1. Pro Plan: Starting at $36.00 per month, the Pro plan accommodates 5 users, 5 workspaces, and 512 assets.

2. Business Plan: Priced at $96.00 per month, the Business plan provides unlimited users and workspaces, along with an increased number of assets.

3. Free-Forever Plan (Community Edition): Faraday.ai extends a free-forever plan for its Community edition, an open-source option offering unlimited assets.

These pricing plans enable users to select options aligned with their specific requirements and budget considerations.

How does FaradayAI handle data ingestion and integration?

FaradayAI supports various data source connections such as data warehouses (Snowflake, BigQuery), databases (Postgres), and cloud buckets (S3). Users can even start by uploading a CSV file via POST /uploads. By utilizing the API endpoints like POST /connections, POST /datasets, and POST /cohorts, FaradayAI facilitates seamless data ingestion and integration to prepare predictive models efficiently.

What features does FaradayAI offer for predictive modeling?

FaradayAI offers a comprehensive suite of features tailored for predictive modeling, including data ingress and integration, identity resolution, and feature engineering. It supports algorithm tuning, validation, and reporting, along with probability calibration for accurate scoring. Additionally, FaradayAI provides explainability, bias detection, and mitigation tools, real-time and batch inference, and compliance with regulations such as SOC-2 and CCPA.

How customizable are the prediction objectives in FaradayAI?

FaradayAI allows users to declare custom prediction objectives through its built-in API endpoints. Users can align these objectives with key customer behaviors, such as likelihood to convert, lead scoring, and more. The system also provides adaptive discounting, lead prioritization, next best offer, repeat purchase readiness, and thematic personalization, making it highly adaptable to various business needs and strategies.