How Kromatid runs cytogenetic dGH scoring entirely on-prem

Kromatid is our first regulated-lab customer and the design partner we built the on-prem workflow with. This is a longer version of the case study summary on our landing page, written so you can decide whether the pattern fits your lab.

The work, briefly

Kromatid runs a directional Genomic Hybridization (dGH) cytogenetic assay. Without diving into the biology: it's a scoring workflow where a subject-matter expert (a cytogeneticist) reviews stained chromosome images, identifies events using a probe Lookup Table, and signs off on a result. The work is image-heavy, the expertise is rare, and the assay output feeds clinical decisions.

The throughput bottleneck is the cytogeneticist. The data is regulated. Both facts shaped what they needed.

What got deployed

DataTorch runs inside Kromatid's environment. The pieces in the loop:

• Lab data stays in their storage, on their hardware.
• Their in-house ML models run as Job Agents called from a Pipeline. The pipeline runs against new images and surfaces predictions to the SME review surface.
• Their cytogenetic LUT (the probe-to-interpretation mapping their team has been refining for years) drives the scoring workflow. The LUT lives where the SME works.
• Audit logs are local, exportable, and shaped for their QC team's review process.

What's not in the loop: us. We don't have credentials into their environment. We don't see their data, their models, or their corrections. The deployment is validated once by their IT and security team, and we stay out of the data path after that.

What changed for their team

Two things were the visible wins.

The first is throughput. With the SME reviewing predictions instead of starting from raw images, the per-case time dropped meaningfully. We're not going to publish a specific number until Kromatid is ready to put one in writing. But the direction is what matters: their cytogeneticists are doing more high-judgement work per shift.

The second is durability. The model in the loop is theirs, the LUT is theirs, the corrections are theirs. If they swap us out tomorrow, none of the assets they've built leave with us. That mattered to their leadership team during procurement.

What didn't get easier

A few honest notes.

On-prem deployments take longer than cloud signups. We work through your IT and security review on your timeline, not ours. If your security team needs three weeks, that's three weeks. We can do many things; we can't accelerate your validation process.

Bringing your own model is great if you have a model. If you're still building one, DataTorch is something you grow into, not something that hands you an ML team.

The lookalike pattern

If your lab looks like Kromatid's shape (SMEs as the throughput bottleneck, data and models that can't leave, an in-house ML effort that's either real or seriously planned), we should talk. The on-prem + Agents-and-Pipelines + workflow-LUT combination is what we built for that pattern.

Book a 30-min call.