Metadata normalization
Align field names, units, formats, identifiers, and scientific metadata.
conc_pg_ml
→
Concentration
Scispot Harmonize
Turn Fragment Lab Data Into Trusted Scientific Context.
Normalize identifiers, units, metadata, and schemas. Link every result to its sample, method, experiment, and lineage.
Trusted by 200+ life science labs
The Data Readiness Gap
Your Lab Has Data.
It Keeps Losing Context.
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Inconsistent identities
The same sample or material appears under different names.
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Missing context
Results lose the experiment, protocol, or method that explains them.
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Broken lineage
Raw, processed, and reviewed data become disconnected.
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Repeated wrangling
Teams clean and reconcile the same information again and again.
What Harmonization Means
Integration Moves Data. Harmonization Gives It Shared Meaning.
INTEGRATION
Move it
Get data between instruments, systems, and applications.
STANDARDIZATION
Make it consistent
Align identifiers, units, fields, metadata, and schemas.
HARMONIZATION
Make it meaningful
Connect data to the scientific context needed to interpret and reuse it.
Scispot Harmonize connects the meaning behind the result to the work that produced it.
What Harmonization Means
Integration Moves Data. Harmonization Gives It Shared Meaning.
What Harmonization Does
Consistent. Connected.
Ready To Use.
Identity resolution
Link samples, lots, materials, experiments, and records across systems.
sample_142
→
S-0142
Scientific context
Connect results to their samples, methods, protocols, instruments, and experiments.
Sample
Result
Method
Raw-to-result lineage
Preserve the path from original source data through transformation, analysis, and review.
Raw
→
Processed
→
Reviewed
Partner data harmonization
Align CRO, CDMO, and collaborator data with your internal scientific context.
CRO file
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Shared schema
Scientific memory
Build a connected foundation for experimental history, decisions, and exceptions.
History
Context
Decisions
Scientific Memory
Your Lab Should Not Have To Relearn What It Already Knows.
Every experiment creates knowledge beyond the final result. Harmonize helps keep that context connected to the work that produced it.
Which sample and method version were used
What happened, and which exception occurred
Why a decision was made
How the result relates to earlier work
Customer-specific context, connected to the underlying scientific record.
AI Can Read A Value.
Give the context behind results.
A measurement becomes more useful when analytics and approved AI tools can trace the sample, method, source, quality state, and review history behind it.
Flag Tomy’s QC deviations
Count Cynthia’s CD8 antibody lots
Find patient-042’s sample location
Flag out-of-spec results
Compare assay run results
Summarize Jeremy’s sequencing run
Flag Tomy’s QC deviations
Count Cynthia’s CD8 antibody lots
Find patient-042’s sample location
Flag out-of-spec results
Compare assay run results
Summarize Jeremy’s sequencing run
One Lab Operating System
Every Layer Has A Role. Harmonize Creates Shared Meaning.
GLUE connects the sources. Bring data in and out of instruments, files, systems, and applications.
Coordinate The Work
Orchestrate
Use trusted context to coordinate workflows, decisions, approvals, and next actions.
Workflow · state · rules
Create Shared Meaning
Harmonize
Normalize, link, and preserve scientific context for reuse across the lab.
Data · relationships · history
Perform The Action
Execute
Run approved digital work through configured workflows, agents, and connected tools.
Actions · review · outputs
GLUE moves the data. Harmonize gives it meaning. Orchestrate coordinates what happens next.
Under the Hood
One Governed Context.
Every Workflow Connected.
GLUE
Connect instruments, files, and lab systems.
Smart Actions
Automate analysis, QC, routing, and reporting.
Jazz
Build governed tracks on the same data and workflows.
Scibot
Deploy AI coworkers on trusted lab context.
Trust Vault
Preserve approvals, evidence, audit history, and validation artifacts.
MCP + APIs
Connect approved models, agents, developers, and internal tools.
What Changes
Less Reconstruction.
More Scientific Reuse.
Less data wrangling
Reduce repeated cleaning, mapping, renaming, and reconciliation.
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Faster data readiness
Make structured data easier for scientific and computational teams to use.
Stronger lineage
Trace a result back to the sample, method, experiment, and source.
Better reuse
Keep past experiments and their context useful for future work.
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Software platforms manage tools. We manage outcomes.
Chosen by labs that set the standard
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Scispot stayed close to the technical details and extended label printing across our device environment. That makes the clinical workflow easier for the team to use and showed that the delivery team would stay with the practical issues that matter in day-to-day operations.
Ally Hirsch
Scientist & Regulatory Technical Lead
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Our team was spending too much time on sample tracking and spreadsheet gymnastics instead of the science. Scispot is becoming the single place we can answer what happened to a sample, from intake through capture, libraries, and sequencing.
Brett Stevens
Director of Translational Laboratory Research
Scispot gave our Medical Affairs team a practical way to collect PHI-aware patient submissions and test requests while keeping the lab workflow connected to Sample Manager
Dr. Onome Braimah
Head Medical Affairs
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Our team needed a system that allowed for more flexibility while keeping data for our sample intake and processing current. Scispot worked closely with us through recurring sessions to shape a more manageable system using custom automations and also determine best next steps for workflow improvement.
Emily Reister, PhD
Staff Laboratory Science Lead