Data & Storage · Integration
Feathery
Add Feathery to your product for your customers, and give your AI agents governed access to it.
Feathery's API at api.feathery.io/api takes the credential as a Token scheme in the Authorization header rather than Bearer, and errors come back as a JSON array of message and code objects rather than a single object, which breaks naive error parsers on the first failed call. Its data model separates the form definition from the fields: fields are account-level entities reused across forms, so writing a submission means addressing the field by its identifier rather than by a form-local name, and two forms sharing a field share its stored value for the same user. Submissions can be created and updated server-side before a user ever opens the form, which is how prefill works, and a partially completed submission is a real record rather than a draft. Feathery also generates filled documents from submission data, so a document is an output artefact tied to a submission rather than an uploaded file. Hidden fields carry values that never appear in the rendered form but do appear in the submission payload. fastn holds each customer's own token, keeps it rotated, and keeps submission reads and writes attributed per tenant.
In your product
Embedded for your customers. Per-tenant auth, no per-customer code, maintained by fastn.
Let a customer connect their own Feathery account so forms embedded in your product write into their submission store
Prefill a submission with data your product already holds so the customer's user only fills the gaps
Read completed submissions and map field values into your own records as they arrive
Fetch a generated document for a submission and attach it to the customer's case file
For your AI agents
Governed, audited access for the agents you build, through the MCP server.
Let an agent read a submission and summarise which required fields are still empty, with each read audited per tenant against that tenant's own token
Have an agent create a prefilled submission before sending a form link to an end user
Allow an agent to pull the generated document for a completed submission and report what it contains
Example prompt
Which Feathery submissions from this week are incomplete, and which fields are people abandoning?
Set up Feathery in 4 steps
- 01Enable the Feathery connector from your fastn dashboard.
- 02Have each customer authorise their own Feathery account, so calls run under their credentials rather than a shared key.
- 03Decide which records, datasets and fields your product needs, map those fields, then enable the actions and triggers you want.
- 04Call it from your product and expose it to your agents through the same governed connection.
Why teams use the Feathery integration
What you get by embedding it with fastn instead of building it yourself.
- Ship a Feathery integration without building it. Your customers connect their own Feathery account inside your product and work their records, datasets and fields there, with no per-customer code on your side.
- Handle the part that actually costs time: schemas differ per customer and change without notice, and volumes can be large. fastn owns the auth, token refresh, rate limits, pagination and breaking-change fixes, so a Feathery update is not your on-call problem.
- One integration serves your product and your agents. The same governed Feathery connection powers in-product features and gives AI agents scoped, audited access, so you read and write your customers' data where it already lives without wiring it twice.
Used by these teams
Compare with
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Hugging FaceFeathery integration FAQ
How do I add a Feathery integration to my product?
Enable the Feathery connector in your fastn dashboard, then let each customer authenticate their own Feathery account. fastn handles the OAuth flow, token storage and refresh per tenant, so there is no Feathery client code in your app and no per-customer branch in your codebase. Setup is 4 steps.
Do my customers each connect their own Feathery account?
Yes. Every connection is scoped to the individual customer, so each authorises their own Feathery account and only ever sees their own records, datasets and fields. That per-tenant isolation is the point of an embedded integration: you support the long tail of customer setups without maintaining an integration per customer.
Can AI agents use this Feathery integration?
Yes. The same connection is exposed to your agents through the fastn MCP gateway, with permissions scoped per tenant and every call audited. Let an agent read a submission and summarise which required fields are still empty, with each read audited per tenant against that tenant's own token
Who maintains the Feathery integration?
fastn does. When Feathery changes an endpoint, deprecates a field or alters its auth, the fix lands in the connector rather than in your backlog, and your customers' connections keep working.
Does the Feathery integration adapt when a customer's schema changes?
Schema and field mapping is configuration per customer, so a change on their side is a mapping update rather than a code change and a release on yours.
How are large Feathery reads handled?
Pagination and throttling are handled for you, and initial backfills are rate-limited so a large import does not exhaust a customer's API allowance.
What can I build with the Feathery integration?
A common starting point: let a customer connect their own Feathery account so forms embedded in your product write into their submission store. Teams also use it for the other use cases listed above, and expose it to agents for governed reads and writes.
How much does the Feathery integration cost?
It is included. Pricing is based on connected accounts, not on how many connectors you enable, so adding Feathery does not change your per-connector cost. You can start free with 3 connected accounts.
Add Feathery to your product
Start free with 3 connected accounts. No sales call required, and no per-customer integration code.