Changelog

Ask Faraday lets you ask plain-language questions about U.S. consumers from Claude, ChatGPT, Gemini, or another AI assistant. Answers come from the Faraday Identity Graph, which covers 1,400+ attributes on 240 million U.S. adults and their households.

Ask about a market, then ask the same question about your own customers, and Ask Faraday shows where the two differ. Because it runs in your own assistant, it works alongside the other tools you’ve connected there. One conversation can pull records from your CRM, compare them in Faraday, and turn what you find into lists and campaigns.

Every answer has to be backed by at least 50 distinct people, and the database itself refuses requests for records about individual people.

Ask Faraday is in beta for current Enterprise customers. To get access, contact your account team. For now, read the launch announcement. More documentation is coming soon.

Explore is no longer available. We removed the Explore page and the Explore switches on the cohort and persona set lists from the Faraday dashboard. Faraday no longer builds Explore data when a cohort or a persona set updates.

The explore field on cohorts and persona sets is deprecated in the API. The field has no effect. Requests that set it still succeed, and responses still include the stored value. You can remove the field from your requests and Terraform configurations at any time.

Looking for a way to understand who your customers are? Try an Insights Report, or, for Enterprise customers, Ask Faraday.

Pipelines that carry a Nearest Location column can now report the distance in miles. Pick Reported distance unit under the payload's Advanced settings; meters remains the default, and every existing pipeline keeps reporting meters until you change it.

The bounds you set are unchanged: a maximum distance is still entered in miles in the dashboard and in meters through the API, whichever unit the pipeline reports.

Deployments with human friendly column headers now name the unit: the distance column is fdy_location_distance_m for meters or fdy_location_distance_mi for miles, where it was fdy_location_distance. A deployment that already delivers this column picks up the new header on its next build, so update anything downstream that reads it by name. Machine friendly headers are unaffected.

Via the API, set "distance_output_units": "miles" on the pipeline's payload.location. See Atlases for the full configuration.

We've added Amazon Athena as a new connection type. Faraday can read Glue-catalogued tables as datasets and write predictions back as tables in the same lake — no extracts, no separate warehouse.

Athena runs in your AWS account, so you keep the data and pay for the query compute. Faraday generates a unique IAM role per connection and assumes a role you create in your account to submit queries; there are no IAM user access keys involved. To set one up you'll need an AWS account ID, a region, the Glue database Faraday should read from and write to, and an S3 prefix for staging.

Targets always fully replace the destination table: Faraday writes CSV to a new prefix under your output location, then updates the Glue table in place, so the table name never disappears. Append and upsert aren't supported.

Via the API, use POST /connections with "type": "athena". To get started, see the new connection guide.

Faraday’s MCP server now also supports ChatGPT. You can connect it through OpenAI’s app directory, alongside Claude and other MCP-compatible assistants. See our MCP blog post to get connected.

Faraday now delivers new mover data, a weekly file of people who recently moved into your service area. New movers set up new services, find new providers, and furnish new homes, so reaching them early can open a new acquisition channel.

Files can go straight to your direct mail team or anywhere else you run campaigns. To see how many people are moving in your area, load your zip codes into the new mover tool, or contact your account team to get started.

You can now manage location data — stores, branches, dealers, service areas — as a first-class resource with Atlases, and output each person's nearest location directly in a Pipeline payload.

Previously, proximity required stitching together a Dataset and a Place. Atlases follow the Dataset pattern instead: create one from a hosted CSV or an existing connection, map your locator columns (address + postcode, lat/lon, or GeoJSON/WKT geometry), and define properties you can filter on later.

In the Pipeline configuration page, a new Nearest location control under Payload adds Location name, Location reference key, and Location distance (m) to your deployment. Advanced settings let you restrict to specific Atlases, filter by location properties, and choose between the nearest location or every location that qualifies. Radius cohorts and market opportunity analyses can now be built from Atlases as well.

Via the API, use POST /atlases with output_to_locations, and the new location property on a Pipeline's payload. If you use select: all, you must supply a max_distance — otherwise every location is returned for every person. See the Atlas API reference to get started.

Faraday Pro is a new self-serve way to buy Faraday context. Pick data from the catalog; enrich your contacts by CSV, API, MCP, or database integration; and pay per matched element at $0.05 a credit. No contract or call required.

The original Faraday dashboard is now Faraday Enterprise. Existing accounts and app.faraday.ai are unchanged; public signups now go to Pro.

If you'd like to read more, check out our blog post about the release, or take a look at the docs to learn more about what Pro is and who it's for and how projects work in the platform. And if you're still not sure self-serve is the right motion for you, read a little more about what Enterprise is and who that's for.

And if you're ready to get the data, just create an account. All you need to get started is a credit card and an ID.

Persona sets can now cluster on your own data — first-party traits defined on your account and properties from any event stream used to define the cohort — not just Faraday Identity Graph attributes.

Previously, clustering was restricted to a curated set of FIG attributes. That restriction keeps a persona set universally applicable: because we have those attributes on everyone, anyone can be assigned to a persona. But if you're personalizing after a first purchase, the restriction is overkill — and it can suppress model quality. Which product line someone bought first, or whether they used a discount code, is often far more useful clustering signal than their FIG demographics.

In the Dashboard, new controls under Advanced on the persona set form let you pick clustering attributes, clustering traits, and clustering stream properties. Note that persona sets built on custom traits or stream properties only apply to people present in the underlying first-party data — anyone outside it can't be assigned a persona.

Via the API, use the new clustering_dimensions property on create and update persona set. The modeling_* properties are now deprecated — if both are supplied, clustering_dimensions takes precedence. See the persona set documentation for full details.

Faraday now has a public status page at status.faraday.ai, showing real-time uptime and incident history for the platform and API.

Bookmark it to check current system status or subscribe to updates during an incident, instead of waiting on a support reply.