The orchestration layer behind experimental R&D.
Scalarity provides the runtime, state layer, integrations, and provenance system that let models and agents safely operate across real research workflows.
Human review + governance
Approval gates, permissions, safety thresholds, and escalation rules sit above agent execution.
Agent orchestration
Scoped agents receive context, tools, memory, and task policies before acting.
Workflow state + provenance
Recipes, runs, outputs, anomalies, model calls, and decisions remain linked.
Models, tools, instruments
Connect to compute, databases, instruments, lab tools, and internal model infrastructure.
Built for the way R&D actually runs.
The architecture matters because research work is messy: models, instruments, notebooks, approvals, process constraints, and human judgment all need to stay connected.
Connect recipes, tools, and validation records.
- Keep process windows, equipment constraints, and safety limits in context.
- Link recipe versions to runs, measurements, anomalies, and approvals.
- Make process iteration traceable before handoff to scale-up or production.
Connect hypotheses, instruments, and group memory.
- Bring literature, prior experiments, notebooks, and instrument data into one workflow.
- Preserve reasoning and failed experiments across students, projects, and grants.
- Make methods, measurements, and decisions easier to reproduce and report.
System primitives for scientific workflows.
The platform separates model calls, agent execution, workflow state, integrations, and provenance so each layer can be swapped, governed, and audited independently.
Any model, governed per task
Route to LLMs, fine-tunes, physics models, surrogate models, or internal predictors with per-agent policies.
Controlled execution
Agents receive scoped context, tools, permissions, memory, and escalation rules for each step.
Structured research objects
Recipes, experiments, process windows, constraints, approvals, outputs, and anomalies stay linked.
Connects to your environment
HPC, cloud, VPC, on-prem systems, knowledge bases, databases, instruments, and lab tools.
How a research task moves from question to record.
Whether the task is a process recipe, formulation change, characterization request, or literature-backed hypothesis, Scalarity keeps context, approvals, data, and decisions connected.
Start from the research context
Pull in prior runs, process constraints, literature, notebook history, instrument data, and the scientist’s objective.
Evaluate the next best step
Use approved models, simulations, search, or analysis tools to compare recipes, candidates, process windows, or hypotheses.
Prepare an actionable recommendation
Generate a recipe change, experiment plan, characterization request, compute job, or report draft with supporting rationale.
Pause where judgment matters
Escalate safety limits, uncertain predictions, anomalous data, equipment changes, or final go/no-go decisions to the researcher.
Commit the outcome to the record
Link inputs, parameters, model/tool calls, approvals, measurements, failures, and conclusions so the next cycle starts smarter.
Designed for controlled R&D environments.
The same architecture can run against hosted services, private infrastructure, or air-gapped systems while preserving traceability and policy controls.