Build versioned protocols with custom data collection forms, then arrange them into scheduled workflows. Every completed step connects the operator, sample, material lot, observation, and result.
This is the execution backbone of FlaskTrack: the place where people, materials, instruments, data, approvals, and scientific context come together while the work is happening.
A technician opens the next scheduled step, records its required measurements and files, and completes it against a specific protocol version. The sample and batch retain that history for review.
The FlaskTrack operating model
A platform, not a collection of tabs
The same records move through design, preparation, execution, instrument capture, analysis, and review.
That continuity is the product: fewer handoffs, less duplicated context, and a usable scientific history from the bench to the data layer.
FlaskTrack can help reduce manual data entry by extracting useful workflow structure and components
from uploaded research papers, methods, SOPs, and technical documents.
Generated records remain reviewable drafts for scientific validation.
Digitize the procedure your team can actually execute
A workflow should be structured enough to guide work and preserve evidence without
turning every scientific exception into a software problem.
01
Procedure design
Select the steps, timing, materials, expected results, required fields, and approval points that must
be consistent.
02
Exception paths
Define how operators document deviations, failed steps, contamination, holds, skipped work, and
corrective actions.
03
Change control
Agree on who may draft, review, approve, supersede, and execute protocol and workflow versions.
Make execution the backbone of the laboratory platform
FlaskTrack turns procedures into execution systems. Teams can reuse methods,
coordinate bench work, preserve sample history, monitor progress, generate reports,
and maintain reviewable evidence without rebuilding records after the fact.
