Faraday

Faraday is a customer context platform. It was founded in 2012 and is headquartered in Burlington, Vermont. We enable consumer brands, marketing agencies, and AI platforms access to quickly connect to a rich database of consumer data: the Faraday Identity Graph (FIG), which contains over 1,500 data points on approximately 240 million U.S. adults and their households. Faraday also has a unique ability to create custom data points by rapidly building predictive models that combine data from FIG with customers' unique first party data. All our capabilities are available via API, MCP, or batch deployment directly into a client's existing tech stack, enabling more precise acquisition, personalization, and AI agents that are grounded in the real context of their consumers' lives.

Customers can start using the Faraday platform in a variety of different ways: Enriching a lead or customer list with identity data (phone, email, address) 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 Identifying their top segments and best customer using cluster analysis Identifying which leads are most likely to buy using our Propensity model builder to create a custom likelihood to buy model 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 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.

A customer context platform combines, cleans, and synthesizes 1,500+ third-party consumer data points—including demographic, property, financial, and lifestyle data—into clear, actionable signals. These signals are then combined with your first-party data to power bespoke machine learning models, all of which are delivered seamlessly into your existing tech stack. Most companies have first-party data but lack real-world visibility into their customers' wealth, life stage, intent, and other key factors. Platforms like Faraday close this gap by enriching customer records with the missing context, enabling both AI systems and human teams to reach and convert customers with greater precision.

The Faraday Identity Graph (FIG) is a deterministic consumer data foundation covering approximately 240 million U.S. adults and their households. It acts as the core intelligence layer for the Faraday platform, containing over 1,500 curated attributes per individual. These attributes encompass deep, longitudinal data including demographics, financial signals, property details, and lifestyle metrics. Faraday uses this vast dataset to provide the real-world context necessary to build custom predictive models and ground agentic AI workflows.

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 1,500+ 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 1,500 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.

Faraday primarily serves consumer-facing industries where understanding individual customer behavior at scale drives measurable revenue outcomes. Core verticals include: Retail and e-commerce (e.g., jewelry, furniture, apparel, and subscription boxes) Home goods and home services (e.g., roofing, home renovation, HVAC, flooring, windows, and solar energy) Financial services and insurance (e.g., credit unions, banks, debt consolidation, and specialty insurance) Health and wellness (e.g., boutique fitness, nutritional supplements) Faraday also provides the underlying data and ML infrastructure for partners to offer consumer data enrichment and predictive intelligence directly to their own users. Partner types include: Marketing agencies (e.g., performance marketing and direct mail providers). SaaS and Agentic AI platforms (e.g., MarTech and vertical SaaS).

Faraday has maintained SOC 2 Type II certification since 2020 and is fully compliant with HIPAA (BAA available), GDPR, CCPA, and 14+ additional U.S. state privacy laws. Faraday ensures ethical data usage and security through: Strict isolation & encryption: Client data is logically isolated—never used to train models for other accounts—and encrypted at rest and in transit. Ethical sourcing: Faraday does not use third-party cookies or social scraping, and never positions its data for FCRA-regulated decisions like credit or employment. Continuous auditing: Security is maintained via a NIST 800-53 risk management program, HackerOne penetration testing, and Checkr employee background checks.

Most lead scoring and modeling tools work from the same limited foundation: 1st party signals collected by the brand itself like clicks, form fills, and CRM activity. Faraday adds a layer those tools can't replicate—combining that 1st party data with real-world consumer signals like life stage, financial capacity, and household context, then delivering configurable predictions directly into the tools and workflows where decisions get made. DimensionFaradayTypical prediction toolsApproachFull customer context layer (identity + real-world data + predictions)Individual scores (e.g., lead score, churn score)Data foundationCombines first-party data with real-world signals like life stage, financial capacity, and household contextPrimarily first-party behavioral data (clicks, purchases, engagement)Explainability & usabilityTransparent, explainable datapoints that show why a prediction existsLimited visibility into how scores are generatedActivation & integrationDelivered directly into CRMs, warehouses, and APIs for use across the full funnel, including support for real-time and agentic workflows via MCPOften confined to a specific tool or workflow

As a registered data broker, Faraday has the same data as TransUnion and Epsilon. However, with a traditional data vendor, getting consumer data is months of sales negotiations, opaque contracts, headerless flat files on SFTP, APIs capped at 100 records/second, and batch systems triggered by email. Faraday has spent a decade as a data buyer and learned exactly where those pain points are—and built a product that eliminates them. If you want a one-time file, we'll send it. If you want it loaded into your BigQuery, we'll keep it updated. If you want a real-time API, you get your API key immediately. The technology is already built and off the shelf. You get the best result on day one.

A Customer Data Platform (CDP) manages data pipelines, while Faraday provides the intelligence that populates them. They are complementary tools with distinct roles: CDPs: Organize your internal first-party data to track how customers have already interacted with your brand. Faraday: Acts as the intelligence engine, enriching your CDP with real-world customer context—like wealth, life stages, and predictive scores. Faraday does not replace your CDP; it provides the grounded context that makes your CDP data actionable.

Faraday is not an LLM like ChatGPT. LLMs generate responses based on patterns in text, but they don't know who they're talking to or retain persistent context. Faraday's role is providing that missing layer of context. Using machine learning, we generate predictive data — who is likely to convert, churn, or respond — and deliver it directly into your tech stack (along with customer datapoints like behavioral, financial, and demographic indicators) so your systems understand who a person is, what motivates them, and how likely they are to act. LLMs generate outputs; Faraday ensures those outputs are grounded and connected to the actual reality of the people they're talking to.

Faraday的核心功能是通过API、MCP和UI提供按需客户上下文。它能够实时获取并整合客户的历史数据、偏好和行为信息，为AI代理提供必要的背景，从而支持个性化交互。本质上是连接客户数据与AI模型之间的上下文中间层。

适用于所有需要个性化客户体验的AI代理场景，如智能客服、产品推荐、销售助手和营销自动化。当AI代理需要根据客户的实时行为或历史记录做出动态响应时，Faraday可提供即时客户上下文，提升响应准确性和用户满意度。

Faraday提供三种集成方式：API，便于开发者自定义集成；MCP（Model Context Protocol），使支持该协议的主流AI框架可原生接入；UI，供人工查看和操作客户上下文。MCP方式尤其适合当前基于LLM的智能代理生态，大幅降低集成成本。