AI Tool for Large-Scale Research
Compatible with leading LLMs
Use cases
Originally created for systematic reviews, FullCorpus AI adapts to any document analysis workflow — from auditing to qualitative research.
Pre-analysis, extraction, and synthesis of evidence from corpora of hundreds of articles, with full process traceability.
Scientific researchCoding and categorization of qualitative interviews using researcher-defined criteria, processed in batch.
Qualitative researchStructured data extraction from institutional documents, reports, forms, and archives at any scale.
Document analysisVerification of correspondence between produced content and the original corpus documents — ensuring fidelity and coverage.
VerificationIdentification of textual similarity risks and citation adequacy in relation to original sources.
AuditHow it works
FullCorpus AI orchestrates state-of-the-art LLMs to process, classify, and synthesize documents at scale — in a structured and reproducible way.
The process begins when we have the PDFs organized in a folder — even if there are hundreds.
After software analysis
LLMs analyze each document individually, applying the same criteria, and data is automatically tabulated for better presentation.
Results are consolidated into a structured report with groupings, syntheses, and meta-analyses of findings.
Verification of textual similarity and citation adequacy in relation to the original corpus sources.
Time
It depends on the number of data points to be analyzed. Our average estimate is approximately 1 hour per 100 documents.
Performance
According to our tests, data extraction achieves near 100% accuracy — surpassing manual reading in exhaustiveness and consistency. Errors are still possible, which is why every analysis is traceable and reviewable. Reliability is further reinforced by adequacy crosschecking analyses, which verify the correspondence between what was extracted and the original documents.
Reliability
FullCorpus AI is not an AI or an LLM. It is software that automates the use of LLMs (GPT, Gemini, and Claude). Its reliability is therefore on par with high-effort, high-reasoning models.
Our differentiator
Data is not mixed in bulk — each document is analyzed individually, with the same criteria applied one by one. It is like running the same prompt for every document, on a model with the highest reasoning effort.
Application
We recommend it as a pre-analysis stage — an initial round of analysis, often also simulating possible results and findings. Researcher review and analysis are still necessary.
We use developer APIs from GPT, Gemini, and Claude — with greater context capacity and performance than chat interfaces.
While chatbots answer one question at a time, FullCorpus AI applies the same criteria to hundreds of documents with fidelity and consistency.
Errors can occur. That is why every analysis is documented and reviewable — traceability is part of the process, not an add-on.
The software
Automates large-scale document analysis using GPT, Gemini, and Claude — data extraction, spreadsheets, and reports, without coding.
Hundreds of documents with the same criteria, in a standardized way.
Data automatically extracted and organized into a dynamic spreadsheet.
Exportable results, ready for academic or institutional use.
The organization
FullCorpus AI goes beyond its proprietary software. For many of our activities it is viable to use other tools, run analyses or audits with other LLMs. This includes advisory and research consulting services beyond the software, such as:
Verification of correspondence between what was produced and the original documents.
Identification of textual similarity risks and citation adequacy.
Methodological structuring, prompts, data, and support for disseminating results.
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