Impact & results
- 30
- PDFs processed per review batch
At a glance
“An AI-assisted suite that takes researchers from raw documents to a publication-ready literature review or coded transcript analysis.”
The challenge
Researchers spend more time processing documents than analyzing them.
Manually appraising study quality, coding transcripts, and assembling a literature review from dozens of source documents is slow, repetitive work that eats into the time researchers should spend on actual analysis and interpretation.
- Manual CASP-style quality appraisal across many source documents
- Transcript coding (meaning units, open/axial coding, theming) done by hand
- Assembling a publication-ready literature review from scratch
Platform intelligence
“We built a platform that parses uploaded research documents and transcripts with the OpenAI API, automates quality appraisal and coding, and produces a structured evidence synthesis or literature review ready for export.”
What we built
Everything inside this engagement.
Qualitative Evidence Synthesis
- Upload research PDFs for automated CASP quality appraisal
- AI-assisted inductive coding and meta-ethnographic synthesis
Review Writer
- Upload up to 30 PDFs at once
- Automated evidence synthesis matrix
- Publication-ready literature review exported to Word/PDF
Transcript Analyzer
- Upload interview/focus group transcripts
- Automated meaning units, open/axial coding, and theme generation
AI Document Parsing
- OpenAI API-powered parsing of uploaded research documents
- Feeds structured content into the coding and synthesis pipeline
Gallery
Inside the product.
Build & infrastructure
The stack behind it.
Our approach
We built a document-parsing engine on the OpenAI API that ingests research PDFs and transcripts, then layered automated CASP-style quality appraisal, inductive coding, and meta-ethnographic synthesis on top, exporting straight to a publication-ready format.
The result
Researchers can now run a batch of up to 30 source PDFs through automated evidence synthesis, or a full transcript through automated coding and theming, instead of doing it by hand.
Next
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