A research assistant that's already read everything.
Search, summarize, and draft across your entire Labitron workspace using natural language. Grounded in your actual pages, samples, and datasets — not hallucinated from the open internet.
What it does today
Practical AI, honest limits.
Natural-language search
"Find the qPCR results from the Q1 cell-line screen with Cq above 25." Returns ranked rows from your data grid, linked to the originating samples and pages.
Summarize long protocols
Paste or link a 12-page methods document. Get a structured summary: reagents, conditions, expected outcomes, validation criteria.
Draft methods sections
Generate a publication-ready methods section from your actual experimental records. Citations link back to the protocol pages — no fabricated parameters.
Flag QC anomalies roadmap
When a dataset lands, surface samples whose QC metrics fall outside expected ranges — based on the lab's historical thresholds, not generic defaults.
Where the AI draws its data
Grounded in your workspace, not the open web.
The Labitron assistant is retrieval-grounded: every answer is anchored to specific pages, samples, datasets, or inventory items in your workspace, with citations the scientist can click back to. The model never invents a sample ID or a Cq value.
Workspace data does not train any third-party model. Every prompt and response is scoped to your tenant, encrypted in transit and at rest, and excluded from any model training pipeline. Enterprise customers can route inference through a dedicated deployment.
Get started
See the assistant on your data.
We'll connect it to a sample workspace during the walkthrough so you can ask real questions.