Every customer is unique. Context reveals how.

Customers can start using the Faraday platform in a variety of different ways:

1. Enriching a lead or customer list with identity data (phone, email, address)
2. Obtaining more information (context) about an individual or their household by enriching lead or customer lists with consumer information — including demographics, financial signals, lifestyle attributes, and behavioral indicators
3. Identifying their top segments and best customer using cluster analysis
4. Identifying which leads are most likely to buy using our Propensity model builder to create a custom likelihood to buy model
5. Identifying which products or services a customer (or lead) is most likely to buy using our Recommender model builder to create a custom next best offer model
6. Identifying which customers are most likely to churn using our Propensity model builder to create a likelihood to churn model

They're two ways into the same platform and the same data. What differs is how much you configure.

Faraday Enterprise is our original dashboard, built for complex use cases: custom predictive models trained on your own data, cohorts defined against your event streams, and deployments wired into your existing stack. It's also where you get an API key. Agent-ready with MCP.

Faraday Pro is a newer, streamlined interface for quickly selecting and deploying the context you need. You pick the attributes you want, consume them by real-time lookup or by uploading a file, and pay only for matches — nothing to configure, no contract to negotiate. Agent-ready with MCP.

Both are grounded in the same Faraday Identity Graph, so the underlying data is identical.

Start with Faraday Pro if you already have customers or leads and mainly want to know more about them — demographics, financial signals, life stage, and so on — and you'd rather pick attributes and hit an API than sit through a sales call. You can sign up yourself and pay per match.

Choose Faraday Enterprise if you need predictive models trained on your own first-party data, cohorts built from your event streams, or deployments into your existing tech stack. Enterprise is sales-led — talk to sales to get set up.

If what you're really after is a direct mail list for people you don't know yet, that's Row Pro, a separate product.

Teams switch to Faraday for a range of reasons like:

• Faster integration: Faraday replaces slow, flat-file transfers with real-time APIs and a Model Context Protocol (MCP) integration, deploying in days rather than months and reducing engineering burden.
• Optimized context budgets: Instead of raw data dumps that overwhelm AI, Faraday curates and synthesizes 1,400+ consumer signals into actionable intelligence.
• AI grounding: Faraday provides a persistent context layer of real-world data, preventing AI agents from hallucinating or generating generic responses.
• Predictive power: By combining a brand's first-party history with third-party context, Faraday models future behaviors (like churn risk or propensity to buy) rather than just looking at past interactions.

Faraday offers multiple flexible deployment methods to access customer context and predictive scores:

• No-code dashboard: Access via app.faraday.ai for uploading data, configuring predictions, and exploring results visually.
• REST API: A fully documented API for batch deployments and real-time lookups, returning enriched identity and predictive scores in under 200ms.
• Native MCP server: Allows AI agents to securely retrieve customer context directly within their context window.
• Native integrations: Direct connections to data warehouses (Snowflake, BigQuery, Redshift) and 50+ downstream integrations with platforms like Salesforce and Shopify.

Faraday draws on two distinct data sources to make predictions:

• Faraday Identity Graph (FIG): Over 1,400 consumer attributes on approximately 240 million U.S. adults and their households, covering demographics, financial signals, lifestyle attributes, and longitudinal history.
• Client customer records: Data collected directly through interactions with the brand itself, items like transactions, conversions, engagement.

Faraday matches these two sources using identity resolution, and then builds custom predictive models for that client's specific use case with the unified dataset. When building models, Faraday curates only the datapoints relevant to each client's outcomes, rather than delivering raw data dumps.