Workload-aware environments
Analyses are directed toward compute appropriate for their tools, data, and expected resource shape.
Agentic bioinformatics infrastructure
The operating layer between scientific intent and execution
Pipette plans analyses, runs open-source tools on managed compute, recovers from execution failures, and preserves every method, parameter, and output in durable project context.
Analysis control plane
Managed
01
PlanScientific context, methods, assumptions
Reviewable
02
ExecuteTools, environments, workload routing
Managed
03
RecoverRetries, continuity, durable state
Resilient
04
RecordCode, versions, provenance, outputs
Auditable
What Pipette manages
Conversation is the control surface. Infrastructure does the work.
A biological analysis is more than a script. It includes the scientific question, method selection, tool environments, compute requirements, intermediate decisions, recoverable execution, and a record another scientist can inspect.
Pipette keeps those layers connected. Researchers work through a natural-language interface while the platform manages the operational path from data to durable results.
Execution lifecycle
A traceable path from question to follow-up
Each stage produces context for the next, so an analysis remains understandable after the original session ends.
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01
Describe the scientific objective
Connect data, sample context, comparisons, and the result the team needs.
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02
Review the analysis plan
Inspect proposed methods, assumptions, tools, and expected outputs before expensive work begins.
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03
Execute on managed compute
Pipette runs the workflow in an appropriate environment and keeps project state outside ephemeral workers.
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04
Recover without losing the project
Recoverable failures can be retried or escalated while files, history, and completed work remain durable.
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05
Inspect reproducible results
Receive figures, tables, code, methods, software versions, reports, and provenance.
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06
Continue in context
Return to the project for follow-up analysis without reconstructing the original decisions and artifacts.
Managed execution
Bioinformatics workloads are not one-size-fits-all
A quick table inspection and a large alignment job should not run on the same compute profile. Pipette manages execution across workload families while keeping the project—not the worker—as the durable unit.
See how agents complement workflow managers →Durable sessions
Files, completed steps, conversation history, and output state remain available across long-running work.
Failures become actionable state
The platform preserves what succeeded and can reason about recoverable failures without discarding the entire project.
Platform failures are refunded
When an analysis fails because of a platform error, consumed credits are automatically returned.
Reproducibility model
The result is not separated from how it was produced
Pipette retains the analytical lineage needed to inspect, reuse, and communicate a result.
InputsFiles, metadata, references
DecisionsMethods, assumptions, thresholds
ExecutionCode, parameters, software versions
ArtifactsIntermediate and final outputs
CommunicationFigures, tables, methods, reports
Project knowledge retained
Method decisionWhy this approach was selected
AssumptionsConditions under which it remains valid
Workflow versionWhat ran and with which parameters
Review historyWhat the team inspected and changed
Project outputsFigures, tables, code, and reports
Institutional knowledge
Your methods should outlast the person who ran them
Shared workspaces keep analytical decisions connected to the projects that produced them. New team members inherit context instead of reverse-engineering folders, notebooks, and undocumented scripts.
This is not generic AI memory. It is the retained record of why an analysis was designed a certain way, what ran, what changed, and what the team learned.
Organization-owned workflows
Bring the methods your team already trusts
Pipette includes a managed library of 150+ bioinformatics tools. Teams can also add and reuse their own workflows so established methods become part of the same planning, execution, and provenance layer.
Workflow onboarding and governance are scoped to the organization’s requirements. Pipette does not require every team to abandon its existing analytical standards.
Managed library
150+ tools
Maintained execution environments
Your organization
Owned workflows
Reusable team methods
Pipette project
One execution record
Context, compute, and provenance
Operational capacity
Capacity that grows with the team
Input limits apply per analysis session. Workspace allocations describe active team workspace, not permanent archival storage.
| Plan | Input per session | Active workspace | Team infrastructure |
|---|---|---|---|
| Premium | 50 GB | 200 GB | Shared workspace, billing, project history, provenance |
| Scale | 100 GB | 500 GB | Institutional knowledge, advanced collaboration, owned workflows |
| Enterprise | Custom | Up to 1 TB standard | Governed workflow reuse and contracted identity, deployment, support, and capacity options |
Security and control
Research infrastructure should be inspectable
Pipette documents its data handling, encryption, isolation, retention, and current compliance posture. Research data, prompts, and results are not used to train AI models.
Review Security- EncryptedData protected in transit and at rest
- IsolatedAnalysis workloads run in private execution environments
- ControlledExport and deletion remain available to the user
- TraceableInputs, execution, and outputs remain connected
Platform questions
Pipette is an operating layer above individual tools and workflows. It helps plan an analysis, execute the required tools on managed compute, preserve project context, and return a reproducible record. Teams can also add and reuse organization-owned workflows.
Pipette retains the relationship between inputs, methods, parameters, software versions, code, intermediate artifacts, figures, tables, reports, and final outputs.
Yes. Teams can add and reuse organization-owned workflows alongside Pipette’s managed tool library. Workflow onboarding and governance details are scoped to the team’s requirements.
Pipette Lab plans support 50–100 GB of input data per analysis session and 200 GB–1 TB of active workspace capacity, depending on plan.
Build on a durable analysis layer
Start an analysis yourself, or talk to us about shared infrastructure for your lab or R&D team.