Computational / TechBio

Return experimental evidence to your models with less manual work.

Scispot builds the Digital Brain between your models and lab. Automate requests, assay data handling and review handoffs, preserving the candidate, conditions and evidence behind each learning input.
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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

Built for AI-driven discovery, protein design and TechBio teams.

Models need reliable material preparation, assay and dataset-review handoffs. Our scientists and engineers connect those steps around your existing scientific stack, starting with one recurring loop and its manual bottleneck.

Map your model-to-lab workflow
Features

Published Customer Outcomes

From computational request to usable experimental evidence.

40%

Less request-creation time

Helped one synthetic peptide AI company cut request-creation time.

20%

Less process-run planning time

Helped a synthetic peptide company cut planning time between runs.

10h → <1h

Shorter image-data processing

Cut image-batch processing below one hour for one Netherlands-based AI biotech.

Up to 50%

Less manual reporting and admin

Reduced manual reporting and administration in connected biotech workflows.

One system, three layers

One Digital Brain connects proposals, experiments and the next design.

Model Proposals & Experiment Requests

Bring nominated candidates into agreed requests through supported APIs or intake records. Preserve model or design versions, predicted properties and assay requirements; check identifiers before experimental work.

A Jazz request queue lets scientists review feasibility, assign preparation and record accepted changes on shared Scispot data, without rebuilding the modeling team’s request.

Assay Execution & Returned Results

Capture the material, method and conditions actually tested. GLUE imports supported instrument outputs into candidate and assay records. Smart Actions applies agreed calculations for scientific review.

Return controls, repeats and missing measurements with the evidence. Keep untested candidates, failed preparations and reviewed results distinct as data returns to computational teams.

Reviewed Datasets & Next Designs

Prepare handoffs using agreed assay, quality and selection criteria. Record source selections and dataset versions, flagging missing evidence before acceptance.

Where configured, return proposals through the same candidate and request relationships. Your team approves datasets, model updates and experiment priorities, with evidence preserved for each iteration.

One connected operating layer

A learning loop moves only as fast as its handoffs.

Automate digital handoffs between modeling and the bench while preserving what was requested, what was tested and what reviewers accepted, so each design starts from usable evidence.

Find the missing evidence.
Draft the next handoff.

Ask about candidates, experiments and data awaiting review.

Meet Scibot
Scibot, the Scispot AI coworker
Which candidates are awaiting reviewed assay data?
Compare predictions with these measured results
Find requests missing experimental conditions
Draft a handoff for this dataset review
Show failed preparations for this design batch
Which model version nominated this candidate?
Which candidates are awaiting reviewed assay data?
Compare predictions with these measured results
Find requests missing experimental conditions
Draft a handoff for this dataset review
Show failed preparations for this design batch
Which model version nominated this candidate?
AI-native lab transformation

An operating loop for both modeling and lab teams.

One Identity Across the Loop

Link model proposals to physical material, assays and reviewed results. Preserve tested designs and conditions so data preparation retains differences that matter to the next model iteration.

Controlled Access for Your Stack

Supported APIs and Scispot MCP give approved tools permitted records and actions. Keep proprietary models while defining request intake, returned results and action review.

Assay Data Ready for Review

GLUE maps supported assay outputs to agreed identifiers, units and metadata. Smart Actions runs configured calculations, preserving source files and review states with each dataset.

Adaptable Apps With Expert Delivery

Our scientists and engineers configure, test and train teams on the first loop. Jazz provides focused request or review screens; measure preparation and use before expanding.

Remove the digital work delaying your next model iteration.

01

Protein Design to Assay Evidence

Turn an accepted design request into traceable experimental work.

Capture the sequence or design reference, version and requested assay. A Jazz queue supports request review and preparation, linking tested material to the method, conditions and instrument outputs.

Route calculated results through scientific review before returning them. Preserve failed preparations and incomplete measurements so missing values retain their experimental explanation.

Explore design-to-assay handoffs

02

Imaging Outputs to Experimental Evidence

Prepare image-derived measurements with the source experiment attached.

Link supported images or cloud references to samples, conditions and acquisition records. Return measurements from approved image-analysis tools with analysis versions and source references.

Check metadata before dataset review. Scientists inspect source images and recorded exclusions to decide inclusion. Model training stays in your team’s approved tools and process.

Explore contextual image data

03

Reviewed Evidence to the Next Design

Return a defined dataset and retain the basis for the next request.

Define assay, quality and inclusion criteria for each handoff. Record selection versions and source records supplied downstream, retaining exclusions and reasons where scoped.

Return model-nominated candidates through the agreed interface. Dataset acceptance and follow-up proposal approval remain separate decisions, each with an assigned scientific owner.

Explore the evidence-to-design loop

Bring the learning loop your teams still run by hand.

Bring a model proposal, lab request and returned dataset. Our scientists and engineers will map gaps and review owners, then scope one workflow to reduce manual preparation.
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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

What does Scispot add to a TechBio company’s existing stack?

Scispot connects requests, physical material, assays, results and review, then automates scoped digital handoffs. Your company keeps proprietary models and specialist tools. Our scientists and engineers start with the loop where manual requests or data preparation delay the next useful experiment.

Can our models and AI tools connect through APIs or MCP?

Scispot supports APIs for available record operations and Scispot MCP for compatible tools and approved actions. Scispot’s computational toolkit supports connected analysis workflows. Agree record access, returned results and approval requirements, checking compatibility and permissions for the intended workflow. You retain access to your Scispot data and exports.

How do you implement a loop with different modeling and lab needs?

Start with one candidate family and assay. Our scientists and engineers agree identifiers, configure request and result paths, test repeats and failures, and train users. Jazz provides team-specific screens on shared data. Subscriptions combine software, integrations and expert delivery, with unlimited users within agreed scope. Measure preparation and adoption before expanding.

Does Scispot train our models or choose the next experiment?

Your team owns training, evaluation and scientific priorities. Smart Actions runs agreed calculations; users preview and approve proposed changes before saving. Scibot retrieves evidence, compares recorded values and drafts requests or summaries. Connected tools follow scoped permissions. Scientists accept datasets, approve model changes and choose experiments.

How are proprietary data and scientific decisions controlled?

Configure project and record access by team role. Scibot and connected tools use permitted context and scoped actions, retaining source, review and action records. For regulated use, your organization sets procedures, validation and approval requirements. Research workflows can use proportionate controls without treating every experiment as a clinical process.

Make every automated handoff accountable to the right team.

Use permissions, source history and review gates across your scientific stack. Your teams retain proprietary-model decisions, dataset acceptance and any regulated validation or sign-off.