What Faraday is
Faraday gives AI agents and marketing systems context about customers. Its own description is a customer context platform for AI agents, delivered through APIs, MCP servers, a UI and bulk deployment.
The work it supports is data completion, segmentation, predictive attributes and personalized messaging for growth and data teams.
What you can do with it
- Enrich customer records before campaigns
- Segment audiences by consumer attributes
- Score leads with predictive attributes
- Feed customer context into AI agents
- Run bulk enrichment through files or API
Who it is for
- Growth and data teams
- Martech and lifecycle marketing teams
- Product teams building customer-facing agents
- E-commerce and subscription businesses
What to watch out for
- Consumer and identity data carries privacy, consent and regional compliance duties
- Predictive labels are probabilities, not facts
- Field minimization and retention policy should be agreed before integration
- Output must be interpretable by your own systems to be useful
Pros & cons
✓ What we like
- Built for agent context rather than static reports
- Multiple delivery paths including MCP
- Covers enrichment, segmentation and scoring
! What to watch out for
- Compliance burden is significant
- Requires clear data purpose definition
- Predictive attributes need validation
FAQ
What does it solve?
It adds customer context to AI agents and marketing systems beyond the few fields a business owns.
Can it go straight into production?
It can, after privacy compliance, permission controls and output review are in place.
What should be prepared?
Customer data to enrich, the fields you need and internal approval for the use case.
Last reviewed: 2026-09-15
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