Lab in the Loop
Practical guides on outsourced assays, choosing CROs, and connecting AI agents to real labs — from the team building Litmus.
Meet the Fellows Running End-to-End Agentic Science
Hypothesis-to-Evidence Grant fellows Joy-Yan Lam, Alexander Reading, and Daniel Pascal will pilot closed-loop experimentation with K-Dense and Litmus Science.
Litmus Science × Accellurix Labs: Bridging AI and Academic Cores
Connecting MCP-native lab execution to university core facilities — routing AI-designed candidates into academic cores for fast, cost-effective validation.
How to Give Your AI Agent a Wet Lab
Your agent can design a rigorous assay in seconds. Here's exactly what happens in the four MCP tool calls between that design and a real, running experiment.
Lab-in-the-Loop: The Execution Layer for AI-Driven Research
AI can generate hypotheses faster than any team can test them. Here's what it actually takes to close the loop between a prediction and real experimental evidence.
CRO GuideqPCR Outsourcing 101: What to Vet Before You Send Samples
RNA quality, reference gene validation, primer efficiency, and raw Cq data — what actually determines whether outsourced qPCR results are trustworthy.
CRO GuideHow to Choose a CRO for ELISA Testing: A Buyer's Guide
What to vet before you send samples out — turnaround claims, curve-fitting methodology, data formats, and the questions that separate a good ELISA CRO from a slow one.
Have an experiment to run?
Describe the question — the copilot drafts the plan and routes it through the vetted lab network.
Submit an Experiment