Custom pipelines for evidence synthesis, systematic review and large-scale qualitative analysis. Designed for transparency, reproducibility and audit.
A discipline at the intersection of social research and software engineering. Opcit designs and builds custom automated workflows for academic and applied research — pipelines that ingest large bodies of literature or qualitative data, structure it, interrogate it against rigorous analytic protocols, and produce transparent, citation-anchored outputs with full audit trails. The work is research infrastructure: methods rendered as code, reproducible by design, engineered to a standard that survives peer review and commissioner scrutiny.
Rapid PRISMA-aligned evidence reviews: violence against women and girls (online harm) & child sexual abuse and exploitation — College of Policing. Two systematic evidence reviews delivered through a custom Python pipeline of our own design. The workflow ingests a screened corpus of academic literature, structures it against a fixed analytic protocol, and produces thematic syntheses with every claim traceable to its source.
Reflexive AI: tracing code lineage and downstream harms in open-source AI — research developed in partnership with the Oxford Internet Institute (Prof. Bernie Hogan). A custom scrape and analysis of 1,033 open-source AI repositories and around 200,000 files, using bespoke tooling engineered for the study. The work develops “TraceSeed”, a proposed voluntary telemetry framework for reflexive AI governance — accountability designed in at the point of authorship rather than retrofitted after harm.
Workflow engineering as a service — for universities, research institutes, evaluators and policy bodies commissioning evidence reviews, scoping studies or large-scale qualitative analysis. Opcit designs, builds and documents the pipeline; the client retains the methodology and the code. Available as a standalone capability or embedded within a wider research commission
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