Confidence in every call.

Variant and sequence analysis where each call traces back to the evidence — for research and clinical genomics teams that need reproducibility.

A call is only as good as the evidence behind it.

Genomic analysis generates more data than any team can review by hand, and the interpretation is where the risk sits. A variant call that cannot be traced is a claim, not a finding.

Reproducibility is the currency of the field. If a result changes when the pipeline changes — with no record of why — it will not survive peer or clinical review.

Our view: provenance is part of the analysis, not a report bolted on afterwards. The pipeline, the model and the evidence are one record.

What we do

Capabilities

Variant & sequence analysis

Analysis and interpretation support over variant and sequence data.

  • Variant prioritisation and interpretation
  • Sequence QC and anomaly detection
  • Human review of material calls

Reproducible pipelines

Pipelines designed so the same input yields the same, traceable output.

  • Versioned pipelines
  • Deterministic runs
  • Recorded model and reference versions

Provenance by design

Every call tied to the reads, references and model decision behind it.

  • Read-to-call lineage
  • Reference and annotation versions
  • Exportable evidence

Interpretation support

Evidence assembled to support classification and review.

  • Evidence summaries
  • Classification support
  • Audit-ready records

How we deliver

01

Understand the data

We map the assay, reference genome and the clinical or research question.

02

Build & integrate

We build the analysis into your pipeline and validate it against known results.

03

Reproduce & assure

We prove reproducibility, document provenance and hand over the pipeline.

What confidence looks like

Outcomes
  • Reproducible, versioned results
  • Every call traceable to its evidence
  • Human review where it matters
  • Records ready for peer or clinical review

Bring us a cohort or a pipeline.

We will analyse a sample of your data and show how each call traces back to its evidence — before you commit to anything.