Faraday API

Name: Faraday API API
Brand: Faraday API
Availability: InStock

✓ Official Vendor SpecAnalyticsData Pipelinesbearer80 EndpointsREST

For Agents

Manage datasets, cohorts, outcomes, and recommenders to produce customer predictions on the Faraday platform. Useful for churn, lift, and conversion modelling flows.

Connect the Faraday API to your agent

Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the Faraday API, or any other public or private API you need. You set the rules, the agent never sees your credentials, and every call is logged.

Install Jentic One Beta (Self-Hosted)

Jentic One Host machine

# On the machine that will host your Jentic One instance:
curl -fsSL https://raw.githubusercontent.com/jentic/jentic-one/main/tools/install.sh | sh

Agent machine

# On the machine where your agent runs (keep this separate from the instance):
curl -fsSL https://raw.githubusercontent.com/jentic/jentic-one/main/tools/install.sh | sh
jentic register       # connects your agent to your Jentic One instance

Jentic One is in public beta. The setup above keeps your agent separate from the instance, which is what you want before using real credentials: an agent running as the same OS user as Jentic One can read its stored keys directly. Just evaluating? A single local install is fine to start. See the secure deployment guide for the tiers.

Capabilities

What an agent can do with Faraday API API.

Create datasets that hold customer records for prediction

Define cohorts that segment customers for modelling

Set up outcomes that describe what the model should predict

Build target audiences scored by an outcome

GET STARTED

Start building with Faraday API API

Explore with Jentic

View OpenAPI Document

Faraday is a customer prediction platform whose API exposes 80 endpoints to manage accounts, datasets, cohorts, outcomes, persona sets, scopes, targets, traits, connections, streams, recommenders, places, and webhooks. The surface lets a team load customer data, define what to predict, and consume scored cohorts back into downstream systems. It is aimed at marketing and growth teams that want lift, churn, or conversion predictions without standing up a data science stack.

Use Cases

Patterns agents use Faraday API API for, with concrete tasks.

★ Churn Prediction Pipeline

A subscription business loads customer data into a Faraday dataset, defines a churn outcome, and scores a target audience to surface at-risk customers. The flow uses /accounts and /attributes for setup, then datasets, outcomes, and targets to materialise the prediction. Reduces what would otherwise be a multi-week data science build to a configuration exercise on the Faraday API.

POST a dataset describing customer events, POST an outcome predicting churn, then POST a target referencing the outcome and read the scored audience back through the targets endpoint.

Lift Modelling for Campaigns

A growth team builds a lift model over a control and treatment cohort to estimate the incremental effect of a marketing campaign. The Cohorts and Outcomes endpoints define the experimental groups and the model output, and the Targets endpoint produces the scored list. Works for channel teams that want lift estimates without a custom modelling stack.

Create control and treatment cohorts via /cohorts, define a lift outcome via /outcomes, then create a target that scores customers and read the ranked list.

Next-Best Recommender

An e-commerce site uses Faraday's recommenders endpoints to publish next-best-product scores for each customer. The flow combines a dataset of past purchases, a recommender configuration, and an output stream that pushes scores to the merchandising system. Suited to mid-market retailers that want personalised recommendations without rolling their own ranking model.

POST a recommender configuration referencing the products dataset and a customer cohort, then read scored recommendations through the targets and streams endpoints.

Data Connection Setup

A data engineer wires Faraday to the warehouse by creating a connection and one or more streams that ingest customer events. The /connections and /streams endpoints handle the load side of the platform, and downstream datasets reference the streams as their source. Designed for one-time integration work that then feeds many downstream models.

POST a connection record describing the warehouse, POST stream definitions for the customer and event tables, then create datasets that reference the streams.

AI Agent Growth Assistant

An AI agent assisting a growth team uses Jentic to drive Faraday — answering questions like 'who is most likely to churn next month' or 'create a target for the holiday campaign'. Jentic exposes the 80 Faraday operations with their input schemas, so the agent can navigate datasets, outcomes, and targets without reading the developer reference. Useful for embedded marketing copilots.

Call jentic.search with 'create a Faraday target for churn', load the /targets operation, and execute it with the churn outcome id and the customer cohort, returning the resulting target id.

Key Endpoints

80 endpoints — faraday is a customer prediction platform whose api exposes 80 endpoints to manage accounts, datasets, cohorts, outcomes, persona sets, scopes, targets, traits, connections, streams, recommenders, places, and webhooks.

METHOD

PATH

DESCRIPTION

GET

/accounts/current

Read the current account

GET

/accounts/current/usage

Read current account usage

GET

/attributes

List attributes

POST

/attributes

Create an attribute

GET

/accounts

List accounts

POST

/accounts

Create an account

GET

/accounts/current

Read the current account

GET

/accounts/current/usage

Read current account usage

GET

/attributes

List attributes

POST

/attributes

Create an attribute

GET

/accounts

List accounts

POST

Why though Jentic?

Three things that make agents converge on Jentic-routed access.

Credential isolation

Faraday bearer tokens are stored encrypted in the Jentic vault. Agents receive a scoped execution permission and the token is attached to the Authorization header at execution time so it never enters the agent context.

Intent-based discovery

Agents search Jentic by intent (e.g. 'create a Faraday target' or 'list Faraday outcomes') and Jentic returns matching operations from the 80-endpoint surface with their input schemas.

Time to first call

Direct integration: 3-5 days to wire connections, datasets, outcomes, and targets end-to-end. Through Jentic: a few hours — search, load schema, execute the configured chain.

Related APIs

Alternatives and complements available in the Jentic catalogue.

Alternative

Amplitude

Product analytics with built-in cohorts and predictive segments

Choose Amplitude when the team already runs product analytics there and wants predictive segments inside the same tool.

Alternative

Mixpanel

Product analytics platform with cohort and behavioural segmentation

Choose Mixpanel when the team needs behavioural segmentation tied to event analytics rather than purpose-built prediction.

Complementary

Segment

Customer data pipeline that often loads events into Faraday

Use Segment to collect and route customer events into a Faraday connection so the prediction platform always has fresh data.

Complementary

Snowflake

Warehouse that often serves as the source for Faraday connections

Use Snowflake as the warehouse Faraday reads from, with streams configured against the relevant Snowflake tables.

FAQs

Specific to using Faraday API API through Jentic.

What authentication does the Faraday API use?

Faraday uses HTTP bearer auth — a token is passed in the Authorization header. Through Jentic the token is stored encrypted in the vault and attached to outgoing requests so the raw value never enters the agent's context.

Can I create a churn prediction with the Faraday API?

Yes. Create a dataset of customer events, define an outcome describing churn, then create a target that scores the customer cohort against the outcome. The resulting target exposes scored customers through the targets endpoint.

What are the rate limits for the Faraday API?

Per-second rate limits are not declared in the spec. Faraday governs throughput by plan — read /accounts/current/usage for the workspace's current usage and remaining capacity rather than relying on per-call quotas.

How do I list available attributes through Jentic?

Run pip install jentic, call jentic.search with 'list Faraday attributes', and execute the returned /attributes operation. The bearer token is replayed from the Jentic vault on each call.

Does the Faraday API support recommenders?

Yes. The recommenders resource publishes next-best-item or content scores for each customer in a cohort. Configure the recommender, attach a dataset of historical interactions, and read scores back through the targets and streams endpoints.

How do I load customer data into Faraday?

Create a connection that points at the source system, define streams for the relevant tables, and reference the streams from a dataset. The /connections and /streams endpoints handle the load side and the dataset endpoint binds the loaded data into modelling.