Manage strain engineering, sequence design, and DBTL cycles with native ELN, LIMS, and AI-driven design tools.
Synthetic biology lives or dies on the speed of the Design-Build-Test-Learn loop, but the loop is bottlenecked everywhere — DNA design lives in one tool, strain tracking in a notebook, growth-curve analysis in a one-off Python script, and sequencing results in a folder somewhere. Cypher AI brings the entire DBTL cycle onto one platform, with AI agents that help at every step.
One connected platform for the work you do today and the data you generate tomorrow.
Store DNA, RNA, and protein sequences with annotations, circular plasmid support, and version history. Track parts, vectors, and full constructs with proper lineage from design to verified clone.
Built-in LIMS for strains, transformations, and culture handoffs. Every clone traces back to its parent strain, transformation event, and the exact construct used.
Plate-reader output processed through reusable widgets you build by description. "Plot OD600 vs time, normalize by blank, and compute doubling time" produces a tool the whole team can rerun.
Track ML-designed variants — model name, version, predictions, confidence scores, and which variants advanced through each round of testing. Close the loop on what your design models actually predict.
Connect Nextflow pipelines for variant calling, quality control, and assembly verification. Pipeline outputs link back to the originating constructs and samples automatically.
A scientist describes a library of 50 variants of an enzyme of interest. Cypher's agent helps generate the sequences, annotates them, and registers each construct in the LIMS with provenance to the parent gene.
Constructs are sent to a synthesis CRO via the marketplace. As clones come back, they're registered as samples and linked to their constructs. Sequencing verification runs through a Nextflow pipeline.
Variants are screened in 96-well plates; OD and fluorescence readings are uploaded. A reusable Cypher widget computes growth rates, fluorescence per cell, and ranks variants.
Results feed back into the design model. The next round of variants is tracked with the same lineage, so what worked — and what didn't — is auditable across the campaign.