> ## Documentation Index
> Fetch the complete documentation index at: https://docs.rafflesia.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# AI Foundry

> One typed API for open biological models — structure prediction, protein design, embeddings, and variant effects.

**AI Foundry** puts open-source biological models behind one uniform REST
surface — `/v1/foundry/models/…`. Rafflesia owns the public resource,
content-addressed result storage, metering, and provenance; heavy inference runs
on external GPUs. Every result is addressed by object id and every call is
recorded against an explicit model release.

## Workloads

Browse the catalog with `GET /v1/foundry/models` (filter by `?workload=`), then
call the endpoint for that workload:

<Columns cols={2}>
  <Card title="Structure prediction">
    POST …/predictions — durable jobs. Boltz-2, Chai-1, Protenix, AlphaFold2, RoseTTAFold3, ESMFold2.
  </Card>

  <Card title="Embeddings">
    POST …/embeddings — sync. Protein (ESM C, ESM-2), DNA (Evo 2, HyenaDNA, Caduceus), RNA (RNA-FM), and 3Di (SaProt, ProstT5).
  </Card>

  <Card title="Protein design">
    POST …/designs — sync. Sample sequences on a fixed backbone with ProteinMPNN and LigandMPNN.
  </Card>

  <Card title="Variant effects">
    POST …/variant-effects — sync. Substitution log-likelihood ratios from the ESM-1v ensemble.
  </Card>
</Columns>

## How a call works

* **Structure prediction is a durable job.** `POST …/predictions` returns a
  `job_id`; you poll it, and the result surfaces as a content-addressed
  structure object. Everything else — embeddings, designs, variant effects —
  returns inline.
* **Results are objects.** Structures in and out are `ObjectRef`s
  (`object_id`, `uri`, `sha256`), so downstream work is reproducible.
* **Calls are safe to retry.** Every `POST` accepts an `Idempotency-Key`.
* **Releases are explicit.** Each call names a `model_release`, and the provider
  must echo the exact release that produced the result.