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Research and lab-adjacent software has to handle large, structured scientific data sets and connect to instruments and workflows that are highly specific to the discipline.
Lab software development fails most often when it's built from a generic SaaS template and then bent to fit a workflow it was never designed for. The actual requirement in this sector is closer to custom instrumentation software than to a conventional web application — heterogeneous data formats from different instruments, analysis pipelines specific to the research question, and a reproducibility requirement that most commercial software simply doesn't carry.
Our research data pipeline engineering treats provenance as a first-class requirement, because research integrity depends on being able to trace a result back to its raw inputs and the exact processing steps applied — the same discipline that underpins our compliance-platform work, applied to a scientific rather than a regulatory context. This scientific data provenance tooling extends to visualisation and analytics built for how researchers actually work with data, not adapted from a consumer dashboard pattern that assumes a different kind of user.
Relevant capabilities
Yes — this is closer to custom instrumentation software than a templated build, and we scope it around your actual instruments, data formats and research workflow rather than fitting the work to a generic platform.
Provenance is built into the data pipeline as a first-class requirement — every result traceable back to its raw inputs and processing steps — using the same lineage discipline we apply in regulated compliance engineering.
Both, depending on scope. We work with existing analysis pipelines where one exists, and build new ones where the research workflow calls for it — always scoped around the actual scientific question, not a generic data-science template.
Tell us the constraint you're actually up against — regulatory, technical, or both.