Bioanalysis Labs

Move analytical batches into study reports with less manual reconciliation.

Scispot’s Digital Brain automates supported result handling, configured calculations, and reporting preparation. Bring study samples, calibration, QC, reanalysis, and review into one workflow, with source records and analytical decisions intact for authorized reviewers.
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Explore LabOS

Trusted by 200+ life science labs

Supporting 250+ instrument types, more than 1,000 experiments each month, and millions of samples across diagnostics, genomics, biotech, and regulated testing.

Your best fit

For bioanalytical teams spending analyst capacity on review and reporting.

Support drug quantification, ligand-binding assays, and study biomarker work alongside your analytical systems. Start with the batch-to-deliverable workflow that needs repeated reconciliation.

Map your bioanalysis workflow
Features

Published Customer Outcomes

From analytical results to reviewable study records.

50%

Less time spent on reporting

Halved weekly reporting time for one preclinical CRO.

10h → <1h

Shorter data-processing time

In one scientific data workflow at a Biotech Lab.

15% → 5%

Lower data-entry error rate

In one workflow connecting scientific data and reporting.

2 days → <6 hrs

Shorter data-retrieval time

Cut data-access time for one computational biology lab.

One system, three layers

One Digital Brain from study samples to reviewed bioanalytical results.

Receive Samples & Prepare Batches

Capture study identifiers, timepoints, matrix, aliquots, receipt condition, and handling history. Check required information and route discrepancies before batch assignment.

Carry method versions, preparation instructions, and sample identities into the worklist. Link development or validation evidence where needed so analysts receive the batch records without rebuilding a sample manifest.

Process Results & Route Exceptions

Import supported analytical outputs against batches, calibration standards, QC samples, and study samples. Retain original files and connected calculation records.

Smart Actions prepare agreed calculations, formatting, and checks. Review outputs before accepting changes and route flags with source evidence. Authorized reviewers assess calibration, QC, and sample issues before recording batch decisions.

Reconcile Studies & Prepare Reports

Record repeat-analysis reasons and approvals while preserving original and subsequent results. Keep planned incurred sample reanalysis distinct from repeats caused by analytical issues.

Reconcile requested samples against reviewed results, exclusions, and outstanding work. Populate agreed datasets or report templates from selected records and route approval with method versions, source data, and analysis decisions available.

One connected operating layer

Give reviewers the batch evidence without the manual assembly.

Match agreed sample and run records, prepare configured checks, and route unresolved work. Analysts review evidence and prepare study outputs with fewer transfers between files, spreadsheets, and reports.

Ask about a study result
and retrieve its analytical history.

Follow the sample, batch, and decision behind the answer.

Meet Scibot
Scibot, the Scispot AI coworker
Which samples are still unreported for this study?
Trace this concentration to its calculation record.
Show batches awaiting calibration and QC review.
Compare original and reanalysis results for these samples.
Find samples with missing storage history.
Summarize open method validation evidence gaps.
Which samples are still unreported for this study?
Trace this concentration to its calculation record.
Show batches awaiting calibration and QC review.
Compare original and reanalysis results for these samples.
Find samples with missing storage history.
Summarize open method validation evidence gaps.
AI-native lab transformation

Tools that remove manual work from batch and study review.

Batch Screens Built for Analysts

Jazz gives receiving staff, analysts, and reviewers personalized screens on shared Scispot data, bringing assigned samples, calibration, QC, exceptions, and required actions into view.

Analytical Data without Re-entry

GLUE imports supported instrument outputs and calculations into the agreed batch structure. Match samples, calibration standards, and QC records through defined identifiers, retaining inspectable sources.

Method-Specific Result Processing

Smart Actions prepare agreed calculations, formatting, and assay checks. Preview outputs before approval while retaining source inputs, method context, and exceptions throughout review.

Scientists and Engineers for Delivery

Scispot scientists and engineers map the batch-to-report process, configure data flows, test results and failures, and train users. Measure reconciliation time with scientific and Quality teams.

Reduce repeated data handling across bioanalytical work.

01

Method Validation Evidence

Prepare method evidence without repeatedly assembling source records.

Capture validation experiments against method versions. Import supported results with matrix, preparation, calibration, and QC context. Configure fields and checks for the assay’s intended use.

Assemble experiments, criteria, and calculations for the method reviewer. Route evidence gaps to owners and retain decisions. Preserve earlier records when methods change so study teams can identify the evidence for each version.

Explore method evidence workflows

02

Analytical Batch Review

Bring calibration, QC, and sample records into one review queue.

Match preparation and acquisition sequences to supported outputs. Smart Actions prepare configured calculations and checks with source inputs and method versions available for preview and review.

Route exceptions to scientists or Quality reviewers. Record batch decisions and sample-level actions. Only accepted results from accepted batches move into the study dataset; unresolved records stay visible for the next review.

Explore analytical batch workflows

03

Study Reanalysis and Reconciliation

Resolve reanalysis records before preparing the controlled study deliverable.

Distinguish planned incurred sample reanalysis from repeats for documented analytical causes. Link each test to its original sample, aliquot, run, method, and result, preserving both records.

Prepare comparisons under agreed criteria for review. Reconcile requested samples, selected results, exclusions, and unresolved work. Populate the deliverable from reviewed records with the rationale for each reporting choice retained for final approval.

Explore reanalysis and study reconciliation

Remove manual work between an analytical batch and its report.

Bring a sample manifest, representative batch, and required study output. Our scientists and engineers will scope result handling, checks, and approvals, then measure reconciliation effort.
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FAIR data • pipelines • automation

Instruments • ELN/LIMS/SDMS • 200+ connectors

FAIR data • pipelines • automation

Instruments • ELN/LIMS/SDMS • 200+ connectors

Frequently asked questions

Which bioanalytical workflows can Scispot support?

Scispot can configure LC-MS bioanalysis, ligand-binding assays, and study biomarker workflows. Its Digital Brain captures records, imports supported outputs, runs configured processing, and prepares review and reporting tasks. Define assay fields, calculations, rules, and reviewers. Specialized acquisition, curve-fitting, and pharmacokinetic tools retain their roles; Smart Actions analysis is agreed and tested for intended use.

Can we retain our instrument and bioanalytical data systems?

Yes. Supported files, APIs, and exports connect analytical tools, LIMS, and related systems. Define sample and batch identifiers, calibration and QC outputs, calculation history, original records, and review handoffs. Your laboratory agrees decision ownership, data location, and access or export needs before selected results feed the configured study deliverable.

How does Scispot track original results, repeats, and ISR?

Link each analysis to its sample, aliquot, method, run, and source result. Distinguish planned incurred sample reanalysis from repeats for documented analytical reasons. Retain original and subsequent results, comparison criteria, and reviewer conclusions. Reconciliation preserves earlier records and the reason for every additional analysis.

How would we implement an analytical batch workflow?

Start with a representative batch and study output. Scispot scientists and engineers map samples, methods, calibration, QC, calculations, exceptions, and approvals; configure connections; test accepted and rejected cases; and train users. Measure reconciliation or report-preparation time. Your laboratory approves method-specific rules and required validation before expanding.

How does Scispot support regulated bioanalysis and ICH M10 evidence?

Scispot organizes validation, sample, calibration, QC, reanalysis, and review records for your workflow; Trust Vault supports evidence and validation scope. ICH M10 covers bioanalytical method validation and study sample analysis within its stated scope; other assay purposes differ. Scientific and Quality teams set criteria, validate intended use, and approve reported data.

Bioanalytical evidence with clear ownership of every review decision.

Source-linked records, role controls, audit trails, signatures, and validation evidence support your workflow. Your laboratory retains method validation, scientific judgment, regulated compliance, and final study-data approval.