Literature intelligence
Ingest papers and reports, classify topics, extract structured fields, build semantic search, compare studies, and organise evidence for review.
We build analytical and research software that can ingest documents and datasets, structure evidence, run repeatable analyses, visualize results, and support human interpretation.
Research teams benefit most when automation improves collection, organisation, analysis, and traceability without hiding methodological choices.
Ingest papers and reports, classify topics, extract structured fields, build semantic search, compare studies, and organise evidence for review.
Cleaning, transformation, validation, feature construction, reproducible pipelines, provenance, and quality checks.
Build repeatable analytical workflows, generate tables and visualizations, and preserve parameters and outputs for review.
Model development, evaluation, experiment tracking, feature analysis, prediction workflows, and deployment where appropriate.
Interactive tools for exploring experiments, cohorts, model outputs, trends, comparisons, and decision relevant evidence.
Specialist applications, simulation interfaces, data processing tools, experiment management, and custom research infrastructure.
AI can accelerate retrieval and extraction, but research outputs are stronger when teams can trace claims back to the source material and review the transformation steps.
The same core architecture can be configured for very different research domains without pretending that domain expertise is interchangeable.
Our role is to engineer the software, data, analytical, and AI layer around the research process. Scientific interpretation, methodological judgement, and domain conclusions remain with the qualified research team.
We can design a research platform around the sources, analytical methods, users, and review requirements of the project.