

Bench: Wet-lab Co-pilot
Bench predicts experimental conditions and generates editable protocols—from expression through stabilization—so you avoid wasted lab cycles. Powered by Astra AI models and agents, it reviews the literature and proposes evidence-backed methods.
Wet-lab work is full of trial and error.
Too much guesswork, too little context. Planning lives in PDFs and memory, so every parameter change restarts the clock and hard-won knowledge doesn’t compound.

Trial-and-Error Workflows
Choosing host, tag, construct, and buffers is mostly manual. Small nudges force fresh batches and re-optimization, and negative results rarely inform the next run.

Fragmented Know-how
Critical parameters are split across papers, supplementary tables, and lab wikis—making you reinvent screens and miss edge-case constraints buried in the literature.

Hidden Pitfalls
Incompatible reagents and unrealistic steps slip into drafts and only surface at the bench, burning days and compromising reproducibility and safety.
From Guesswork to Guided Work with Bench
Bench turns your protein sequence into evidence-backed starting points you can run, edit, and iterate—so experimental cycles shorten and learning compounds run-to-run.

Ready-to-Run
Bench converts sequence + context into editable protocols for expression, purification, crystallization, cryo-EM, and stabilization—with buffers, concentrations, and sensible ranges.

Work on Tough Targets
Predict experimental conditions and suggest stabilizing mutations to raise hit rates on unstable or unknown proteins, then iterate toward optimal results.

Guardrails for Success
Astra models characterize function, localization, PTMs, and quaternary assembly while agents scan the literature; Bench auto-flags conflicts so errors don’t reach the bench.