Latent Lab

Give science an agent
that can do the work.

Latent Lab plans, runs, and documents scientific analysis inside secure sandboxes. Bring your team into the same live project, connected to your data and ready to resume exactly where the work stopped.

Start a projectSign in to your organization
01
Tenant isolated
02
Team synced live
03
Workspaces resume
Fleetline Labs/
Research objective

Protein folding study

Predict the structure and binding affinity of a benchmark protein–ligand pair.

Run a Boltz-2 folding task and keep the structure, confidence, and affinity outputs together.

Latent Lab

I attached the configured scientific workflow for review.

Boltz-2 structure and affinityGPU workflow · estimated $12
Ready

Launching this workflow requires explicit cost approval.

Ask Latent Lab about your research…
Ask, plan, analyze, and run approved work
scientific-jobs/boltz2/outputs/input_model_0.cif
Interactive 3D structureinput_model_0.cif

Opening structure…

Drag to rotate · scroll to zoom · sample data PDB 6LU7
Scientific job 7b46a893sha256:91e63b400c3…Completed

Example 01 · Real-time collaboration

Work together in the same live project.

See who is present, edit analysis together, and follow your team's activity as the agent, code, and results move forward in real time.

Fleetline Labs/
PMA
3 here
Research objective

EGFR binding review

Review the predicted binding pose and confidence with the structural biology team.

Check the confidence output and annotate the analysis while I review the structure.

Latent Lab

The shared analysis and project activity are live for everyone in this Project.

Priya and Miguel are editing

Shared drafts, workflow inputs, pointers, and the protein camera stay synchronized.

Ask Latent Lab about your research…
Ask, plan, analyze, and run approved work
scientific-jobs/boltz2/outputs/input_model_0.cif
from pathlib import Path
import json

confidence = json.loads(
    Path("confidence_input_model_0.json").read_text()
)

print(confidence["complex_plddt"])
Priya editingMiguel
Scientific job 7b46a893sha256:91e63b400c3…Completed

Example 02 · Differential expression

Turn connected datasets into reviewable results.

Ask a research question, follow the agent's analysis plan, and keep figures and source data attached to the run that produced them.

Fleetline Labs/
Research objective

KRAS G12C response genes

Identify the genes most strongly upregulated in KRAS G12C cells relative to wild type.

Rank the ten strongest response genes and retain a reviewable figure with the run.

Latent Lab

I validated the count matrix, completed the contrast, and attached the ranked result.

Run differential_expression.pyCompletepython differential_expression.py --contrast G12C WT
Ask Latent Lab about your research…
Ask, plan, analyze, and run approved work
run-84b2/outputs/differential_expression.png
Stored artifact

differential_expression.png

image/png · 44.8 KB
Top differential-expression signals in KRAS G12C cells
Run 84b2sha256:91e63b400c3…Completed

One continuous workspace

From question to inspectable result.

The agent's work stays close to the conversation: tool activity expands inline, outputs become real artifacts, and paused environments can be restored without starting over.

01

Sandboxed execution

Every project runs in an isolated workspace with its own tools and data.

02

Scientific connectors

Bring in versioned records, datasets, and new sources through one connector seam.

03

Durable artifacts

Reports, figures, tables, and source files stay attached to the work that made them.

04

Organization security

Tenant boundaries, role-aware access, and resumable state are built into the platform.