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Documentation Index

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Strand AI turns a single H&E-stained whole-slide image into per-pixel predictions for a panel of protein markers: biology you would otherwise need multiplex immunofluorescence, mass cytometry, or a dedicated antibody panel to capture. We trained POSTMAN on paired H&E ⇄ multiplex datasets so you can recover spatial protein signal directly from the H&E you already have. The platform accepts a slide, runs inference on our infrastructure, and returns model-validated marker channels you can drop into the rest of your pipeline.

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Why Strand AI

H&E in, markers out

No antibody panels, no IF capacity, no second slide. Submit the H&E and request the markers you need.

Spatial outputs

Predictions are returned as multi-channel OME-Zarr you can open as AnnData (Python) or SpatialExperiment (R) and treat like real multiplex data.

REST + SDKs

Python and R clients, a documented REST API, and a credits ledger you can estimate against before you submit.

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Who this is for

Researchers and biotech teams who have H&E at scale and want spatial proteomics signal, typically for:
  • biomarker hypothesis generation in retrospective cohorts,
  • enriching slides where IF or mIF was not collected,
  • batch-level QC of multiplex panels against an orthogonal predictor.

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What’s next

Quickstart

Mint an API key and run your first prediction in 5 minutes.

Supported markers

The 19 markers we predict today, with the model versions behind them.
The platform is currently invite-only. If you don’t have access yet, email support@strandai.com.