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FullCorpus AI began with our own need to conduct systematic reviews, and our inability to find any software to automate LLM-based data analysis at its full potential. A major problem we faced was that the more files we added, the more the quality and consistency of the analysis deteriorated.

— The founders

From idea to tool.

2023

Intensive use of generative AI

Heavy use of generative artificial intelligence, including for data analysis.

2024

First customizations via GPT

First generative AI customizations via GPT for various analysis purposes.

2024

Customizations for academic writing

Generative AI customizations focused on academic writing and data analysis facilitation.

2025

Transformation into software

Transformation of previous customizations into software: custom AI design for large-scale document analysis.

2025

First uses and tests

First uses, tests, and positive results with real users and concrete analysis cases.

2026

Expanded use

Confirmation, through experiments, of superior performance in exhaustiveness and fidelity in data extraction — and expansion of use to new projects and researchers.

Principles that are
non-negotiable.

01

Rigor before speed

Our priority was never to be the fastest. It was to be the most reliable for those who need to publish with evidence.

02

Full traceability

Every analysis must be auditable. Model, version, prompt, temperature — everything documented for reproducibility.

03

Built by those who use it

No feature was added without being needed in a real research project. This ensures every detail matters.

04

Model neutrality

We do not advocate for any LLM. We provide the layer that allows choosing the best model for each task.

Do you have a
corpus to analyze?

Talk to the team. It could be the start of a collaboration or simply early access to the tool.

Get in touch → Meet the team