A Bay Area biotech pioneering early cancer detection
Founded in the Bay Area, Everest Detection is a pioneering biotechnology company focused on revolutionising early cancer detection. Their research operations rely on processing complex clinical datasets from multiple sources — lab instruments, LIMS platforms, and external partners — requiring a balance between scientific precision and operational efficiency.
For a team of scientists, data preparation should not be the bottleneck. Every hour spent formatting spreadsheets is an hour not spent on the research that matters.
Clinical data from multiple sources, prepared manually, consuming research time
Everest Detection's research team worked with diverse clinical data sources — each with its own format, schema, and update cadence. Before any analysis could begin, scientists had to manually clean, reformat, and validate the data. This was time-consuming, error-prone, and required constant involvement from the data team.
With limited technical resources and no dedicated engineering team, the researchers had no way to modify data pipelines independently. Every change meant raising a request and waiting — slowing down experiments and delaying critical insights.
Code-free data automation — built and managed by the researchers themselves
Mammoth gave Everest Detection's scientists the tools to own their data workflows — without writing a single line of code:
- Standardise and cleanse clinical datasets from diverse lab and partner sources
- Automate recurring data collation tasks that previously consumed hours each week
- Enable researchers to edit and manage pipelines independently — no IT ticket required
- Ensure consistent, validated outputs ready for analysis and LIMS integration
50% faster analysis. 30+ hours freed. Researchers fully in control.
- 50% faster analysis — high-volume clinical datasets processed in half the time
- 30+ hours freed per month — previously spent on manual data handling tasks
- 75% more reliable outputs — validated views replacing error-prone manual formatting
- 68% of pipeline edits now made directly by researchers — no data team needed
- Improved data quality and consistency across all research and lab systems