UC Berkeley · Stanford

Turn experiments into structured, trackable research workspaces.

AI help for planning, troubleshooting, and documentation — grounded in your experiment, not a blank chat.

Persistent workspace per experiment — sessions, logs, and context stay intact
AI that knows your protocol, controls, and prior runs before answering
Plan, troubleshoot, summarize, draft protocols, and prepare handoffs
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01

Context that survives every session.

When a run fails at 11pm, you log it. When you come back Tuesday, the AI already knows the experiment, what changed, and what the controls showed. It asks the right questions — not generic ones.

Sessions are separated inside a project. Initial setup, run logs, troubleshooting, PI prep — each gets its own focused thread. One experiment never bleeds into another.

setup
Initial setup
Defined donors, controls, and expected readouts.
run-log
Run 1 — noisy controls
Captured low signal in donor 2, proposed compensation checks.
analysis
Compare Ct values week 2
Investigating primer mismatch vs reagent degradation.
handoff
Prepare summary for PI
Need final donor comparison and next-step recommendation.
02

A workspace built for your assay, not a blank doc.

Describe what you're working on. Labframe generates a structured workspace — objective, conditions, controls, run log, troubleshooting, results, handoff notes — all tailored to your experiment type.

Edit any section. Delete what doesn't fit. Save as a reusable template for the whole lab.

ObjectiveDefine activation comparison and readouts
Conditions & ControlsCondition A · Condition B · Unstimulated
ProtocolPBMC thaw · staining panel · acquisition
Run Log3 sessions — 1 active, 2 complete
TroubleshootingDonor 2 low signal — investigating
Handoff / ReportPI summary in progress

Built by

Benjamin FrancUC Berkeley EECS. RLHF and agentic AI research.
Zack DonovanStanford Physics & CS. Research in Prof. Hollberg's atomic physics lab on optical frequency standards.