About LabACT
LabACT is a small team of experienced engineers providing forward-deployed scientific software engineering for biopharma organizations. We work directly with the people who understand the science, operations, and systems.
LabACT started as a consulting practice. As the work grew, so did the team and the range of problems we could take on. That experience still shapes how we work: start close to the problem, prove value with a useful improvement, and add engineering support as the opportunity becomes clearer.
We work with both small and large biopharma teams. Some engagements start with a research question or a workflow that needs improvement. Others improve an existing system that has become difficult to change. We map the work, test a focused intervention, and leave behind code with tests, documentation, and clear ownership.
In Laboratory Automation, this means applying Automation, Collaboration, and Transformation to laboratory workflows: start with a useful task, work with the people closest to it, and grow the system as the results prove its value.
We use current technology, including AI, where it improves engineering and scientific workflows. Depending on the problem, that can include Python and TypeScript services, React applications, Postgres and other data stores, APIs, cloud infrastructure, containers, queues, workflow automation, scheduled orchestration, CloudFormation, Rain, EventBridge, Lambda, ECR, model APIs, CI/CD tooling, and observability tools. We choose technology for the job and keep the design understandable to the team that will operate it.
What ships is maintainable software: application code, data systems, integrations, permissions, logs, and review interfaces. A company can begin with focused support for one workflow, then grow into a larger engineering partnership as more opportunities are proven. Technology supports the work, while your team retains control.
How engagements work
Engagements follow Automation, Collaboration, and Transformation. We start with a useful improvement, build it alongside your team, and expand the system as the value becomes clear. Your engineers and operators stay involved throughout.
Typical projects: approval workflows, batch record review tooling, automating manual handoffs with owned applications, document operations, knowledge retrieval, reproducible research pipelines, scientist portals, CDD API integrations, structured exports to Postgres, materialized reports, clinical trial competitive intelligence, custom search and ingestion, data quality and statistical views, external-source monitoring, service triage, and converting n8n or agent prototypes into tested code.