Designed around DICOM, sequence, and genomic data
Generic MLOps tools treat data as rows in a bucket. Clinical and genomic data are neither: an imaging study is a structured hierarchy carrying identifiers, and a genome is too large to move casually. XAIOps was built for both from the start.
- Imaging studies organized the way clinicians expect, and reviewable in the browser
- Large genomic data made analysis-ready on ingest, so it can be explored where it sits
- Every access to protected data recorded as evidence you can verify
Clinical data breaks the usual assumptions
These are the three failures we watched teams hit before building the platform around them.
A study is not a folder of images
Imaging data carries patient, study, and series structure along with identifiers. Flatten it into files and you lose the organization your clinicians and your compliance office both depend on.
Genomes are too big to move
A researcher who has to transfer many gigabytes before looking at one region will not look. Exploring data where it already sits turns hours into seconds.
Protected data needs provenance
With patient data, who opened what and when is not a nice-to-have. If the record has to be reconstructed afterwards from tickets and memory, it is not a record.
Medical imaging
Studies in, inference out, provenance throughout
- Studies organized the way clinicians expect them to be
- Review imaging in the browser, without a separate workstation
- Findings captured against the study rather than in a side spreadsheet
- Run inference directly from the study you are looking at
- Train segmentation, detection, and classification on the same data
- A reviewer role for clinical staff who are not platform administrators
Sequence & genomics
Explore large data without moving it
- Sequence and genomic files made analysis-ready as they arrive
- Browse a genome inside the platform instead of downloading it
- Open a region of interest in seconds rather than waiting on a transfer
- Validation and summary statistics applied on ingest
- Genome-scale models available on your own GPUs
- Results stored beside the rest of your research data
The audit trail that comes with it
Every access to an imaging study or genomic file is recorded, held in a form that cannot be quietly altered, retained for the long term, and verifiable on demand. The result is evidence you can hand to a reviewer rather than an assurance you have to defend.
- Who accessed what, and when, across every dataset
- Records protected against later modification or deletion
- Retention aligned to clinical and research obligations
- Built to support HIPAA-oriented review, on your infrastructure
Bring a study or a cohort
We will register real data in a live environment, open it in the viewer, run inference against it, and then show you the audit record that produced.