Research Workspace
An autonomous research workspace from target to results.
Explore biological structures, compare candidates, inspect parallel experiments, follow samples through laboratory workflows, and trace every conclusion back to its source.
Product demonstration
See BioSphereer at work.
Follow one illustrative campaign from biological objective to evidence-backed result and human review.
Product demonstration · Illustrative campaign data · Narrated in English
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Begin with a biological outcome. BioSphereer turns that intent into a structured research objective, making priorities, constraints, evidence requirements, and unresolved questions explicit.
Behind the workspace is a governed fleet of specialized models and deterministic services. AURA coordinates the research loop. ORIGIN structures the target. ATLAS retrieves and ranks scientific evidence. HELIX develops evidence-backed strategies, while MATRIX generates an experiment portfolio sized to the scientific objective and available capacity. Each capability is versioned and governed through the Model Registry, with defined permissions, evaluation status, known failure modes, and rollback controls.
The research plan connects scientific reasoning to operational execution. SENTINEL applies safety, policy, and approval controls. Approved plans pass to FORGE, a deterministic workflow compiler, and ORBIT, a deterministic scheduler that reserves connected equipment, materials, and sample movements.
Researchers can explore generated candidates through tables, heatmaps, parallel coordinates, scatter plots, Pareto frontiers, and rankings. This makes it possible to compare confidence, information gain, cost, quality, uncertainty, and measured performance without limiting the research campaign to a fixed number of candidates.
Pressing Restart live run starts the connected laboratory workflow. ORBIT coordinates the scheduled equipment. Samples move through storage, robotic handling, incubation, sampling, and analysis. PULSE monitors equipment telemetry and raises anomalies, while the interface updates run progress, measurements, device state, and sample movements in real time.
PRISM analyzes measured results and compares candidates against the biological target. Researchers can inspect molecular structures, changed regions, confidence, measured and predicted provenance, and candidate rankings. NOVA uses the accumulated evidence and experimental results to propose the next experimental portfolio.
Consequential recommendations remain subject to human review. TRACE records every model, dataset, workflow, decision, sample movement, and equipment event. SCRIBE transforms the validated research record into evidence-linked documentation for scientific, operational, and governance audiences.
BioSphereer connects scientific reasoning, governed models, and laboratory execution in one auditable research system. Biological intent. Experimental reality.
01 / Goal Builder
Turn an objective into a measurable research target.
Define desired output, host system, quality requirements, optimization priorities, success criteria, and the evidence needed to validate the outcome.
Target Studio
Explore the biology behind the objective.
A unified visual environment for molecules, proteins, DNA, RNA, enzymes, biological pathways, host strains, and research candidates.
Molecular structure
Navigate domains, surfaces, active sites, variants, and confidence.
Sequence intelligence
Compare sequence features, regulatory regions, coding regions, and evidence-linked variants.
Biological context
Connect the host system, production pathway, related samples, and campaign history.
Parallel Experiment Matrix
Explore multiple research paths simultaneously.
Rank candidates, compare results, stop weak branches, expand promising strategies, and balance time, cost, uncertainty, and information gain.
- Candidate ranking and heatmaps
- Pareto frontier and result distributions
- Iteration, plate, batch, and timeline views
Workflow Studio
See the entire campaign before it begins.
Move between scientific, operational, data, and governance views without losing the relationship between them.
Scientific
Targets, hypotheses, evidence, strategies, and experiments.
Operational
Samples, devices, transfers, storage, incubation, and disposal.
Data
Model inputs, measurements, transformations, outputs, and lineage.
Governance
Approval gates, permissions, policy decisions, versions, and audit records.
Results and iteration
Every measured result determines what happens next.
Compare candidates against the target, reference data, earlier iterations, and quality requirements—then identify the experiments most likely to improve the result or reduce uncertainty.
Campaign outcome
- Target score / 83
- Improvement / +27%
- Best candidate / A-04
- Confidence / medium-high
Recommended action
- Expand candidate A-04
- Resolve one quality uncertainty
- Repeat for reproducibility
- Update evidence graph