Customer context platform for AI agents - Faraday
We've overhauled the Faraday Identity Graph — learn what FIG v2 means to you
Every customer is unique. Context reveals how.
AI agents and workflows need rich data to create modern customer experiences. Faraday provides context on 240 million U.S. adults via MCP, real-time API, and batch deployment.
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On-demand context
Get the context you need, when you need it
Just choose the elements you want in your payload and Faraday will make them available via API, MCP, and easy file append. Start with 1,500+ consumer and identity data points from the world’s best compilers.
“Faraday allows us to personalize at scale for all of our customers”
Jonas Malpass Caligan, VP Ecommerce
Jewelry brand John Hardy uses Faraday’s on-demand data to instantly enhance their customer profiles and personalize.
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Build predictive model
Custom predictions
Predictive context based on your unique data
Gain expert-level context with predictions like propensity to convert, next best offer, or persona clustering. Faraday can use your data to build and deploy bespoke machine learning models automatically.
“Faraday has become the beating heart of our entire organization.”
Eric Kozak, head of performance marketing
By prioritizing leads with high Faraday scores, American Standard closed customers at a 3x higher margin, transforming its silent call center into a high-energy growth engine.
Recurring deployment
Infuse your stack with customer context
For ongoing workflows, Faraday can continuously deploy throughout your stack so that every engagement has the power of context. Integrated with every major data warehouse, cloud provider, and marketing tool.
Subscription brand Bespoke Post continuously scores customer traits against product attributes for their monthly boxes. The result is a 5x ROI, a 5% lift in revenue per customer, and a 3% reduction in churn.
Customer data to your warehouseContext delivered to your stack
Customer data to FaradayContext back to your warehouse
Every customer has a story
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Imagine what you could do with the right context
What would it take to engineer the perfect experience? Combine identity, consumer data, and predictions to power your next brilliant engagement workflow.
PopularFinancial services & insuranceE-commerce & retailHome services
Adaptive discountingSubject line personalizationLead biddingNext best offer insertionSales rep assignmentDirect mail list pruningLoyalty targetingHigh spender early detection
Adaptive discountingSubject line personalizationLead biddingNext best offer insertionSales rep assignmentDirect mail list pruningLoyalty targetingHigh spender early detection
Lookalike audiencesChannel selectionPersonalized bundlingMarket expansionLead prioritizationOut-of-home location sitingLead qualificationCustomer personas
Lookalike audiencesChannel selectionPersonalized bundlingMarket expansionLead prioritizationOut-of-home location sitingLead qualificationCustomer personas
Persona 1| Persona 2| Persona 3
Size Individuals| 2,841| 1,956| 1,203 % of total persona set| 47%| 32%| 21% Clustering traits Age| 55–64| 25–34| 45–54 Household income| $100k+| <$40k| $60–80k Shopping style| Luxury offline| Bargain hunter| Amazon-centric
Insight discovery
Learn what makes your customers tick
Get the context behind the context so you can engineer delightful customer experiences. Compare segments, build personas, and get detailed reporting, all harnessing the power of Faraday’s built-in consumer data.
“You've come to the right place”
Brendan Taylor, CEO
Bee’s Wrap infused their sales data with context from Faraday, generating the personas and customer insights needed to prove their shopper profile aligned with national retailer Target. Their data-driven pitch landed placements in 550 stores.
Security
Securing consumer and customer data is our top priority
We have been in business since 2012 and handle PII from thousands of US brands.
SOC-2 Type II audited
hackerone bounty program
NIST 800-53 risk management
CCPA, GDPR, and 15 more
HIPAA compliant
“Faraday is in our DNA.”
—Katia Unlu, Chief Commercial Officer
Context drives conversion Boll and Branch improved email conversion rates 30% by using Faraday-generated personas to send the best creative variant to each contact.
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billions of predictions every day.
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FAQ
What is 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.
How can you get started with Faraday?
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
What is a customer context platform?
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.
What is the Faraday Identity Graph?
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.
Why do teams switch to Faraday?
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.
How does one access Faraday's data?
Faraday offers multiple flexible deployment methods to access customer context and predictive scores:
- No-codedashboard: Access via app.faraday.ai for uploading data, configuring predictions, and exploring results visually.
- RESTAPI: A fully documented API for batch deployments and real-time lookups, returning enriched identity and predictive scores in under 200ms.
- NativeMCP server: Allows AI agents to securely retrieve customer context directly within their context window.
- Nativeintegrations: Direct connections to data warehouses (Snowflake, BigQuery, Redshift) and 50+ downstream integrations with platforms like Salesforce and Shopify.
What data does Faraday use to make predictions?
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.
What industries does Faraday serve?
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).
How does Faraday ensure data is compliant and ethical?
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.
How does Faraday differ from lead scoring tools and analytics & modeling platforms?
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.
Dimension| Faraday| Typical prediction tools
Approach| Full customer context layer (identity + real-world data + predictions)| Individual scores (e.g., lead score, churn score) Data foundation| Combines first-party data with real-world signals like life stage, financial capacity, and household context| Primarily first-party behavioral data (clicks, purchases, engagement) Explainability & usability| Transparent, explainable datapoints that show why a prediction exists| Limited visibility into how scores are generated Activation & integration| Delivered directly into CRMs, warehouses, and APIs for use across the full funnel, including support for real-time and agentic workflows via MCP| Often confined to a specific tool or workflow
How is Faraday different from data vendors like TransUnion or Epsilon?
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.
How does Faraday differ from a CDP?
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.
Is Faraday an AI platform? How is it different from an LLM like ChatGPT?
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.