About Cypher AI
We are building the lab OS that scientists actually want to use.
Founded in 2025 in Cambridge, MA, Cypher AI exists because the infrastructure under every research lab — the patchwork of LIMS, ELNs, custom scripts, and instrument exports — makes reproducibility an accident rather than a default. We are fixing the layer below the bench, not adding another tool on top.
The problem we are solving
Fragmented lab tooling is a reproducibility problem, not just a workflow inconvenience.
In 2025, Yaoyu Yang was building research tooling for computational biology labs in the Cambridge area — watching scientists spend a third of their time not on science, but on maintaining the stack around it. A protocol drafted in a Word document. Raw assay data sitting in a proprietary instrument format. Analysis code written once by a postdoc who left. Every handoff between tools was a place where context dropped and errors accumulated quietly.
The cost is not just time. When the protocol lives in someone's head and the analysis in a local Python script, science cannot be verified — and cannot be built on after that person moves on. This is not a documentation failure. It is a structural gap in the software layer underneath the bench.
Cypher AI is the answer to that gap. Not another ELN with more fields, not another LIMS configuration project — but an AI operating system that writes and runs the tools a scientist actually needs, on demand, and keeps every result traceable from the start.
"The best lab software disappears into the work. You describe what the experiment needs, and the tools are there." — Yaoyu Yang, Co-Founder & CEO
How we work
Three principles we will not trade away.
Data integrity before features
We do not ship capabilities that compromise the traceability of research data. Every protocol run, every analysis output, and every deviation must be attributable and auditable. That is a constraint we apply before asking whether a feature is useful.
Reproducibility is infrastructure, not documentation
Asking scientists to document their work after the fact does not produce reproducible science. We build reproducibility into the default execution path — protocol versioning, parameter capture, and analysis lineage are automatic, not optional.
We are a platform, not a record system
Cypher AI is not another ELN or another LIMS. Those tools record what happened. We give labs the AI layer that determines what happens — writing protocols, generating analysis code, and connecting instruments — then keeps the record automatically.
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