QualIntel OS
NVivo
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Code Flex
QualIntel OS is an AI-assisted qualitative research platform for PhD and postgraduate researchers โ built for the question every examiner now asks: how did you use AI in your analysis?
Other AI tools code your data and ask you to check it. QualIntel works the other way: the AI surfaces candidate evidence semantically matched to your codebook, and you confirm or reject every coding decision. Every suggestion and every decision is logged into a complete, timestamped audit trail โ and on export you get a non-editable AI-disclosure statement built from that trail, ready for your methods chapter.
Methodology-aware, not generic: seven qualitative methodologies supported โ Reflexive Thematic Analysis, IPA, Grounded Theory, Gioia, Codebook TA, Content Analysis, and Template Analysis โ with method-specific guidance and reporting standards (RTARG, COREQ, SRQR).
From data to draft: upload your research design, build the codebook, review evidence, write your synthesis, and scaffold a methodology-aware, rubric-aligned report โ then export an examiner-ready evidence pack.
The analysis โ and the credit โ stay yours.*
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QualIntel OS's answer
QualIntel OS is built around one non-negotiable rule: nothing gets coded without human confirmation. The AI retrieves candidate evidence from your transcripts, but the researcher accepts or rejects every suggestion โ and each decision is timestamped into a methodology audit trail as you work. At submission time, that becomes a one-click package: evidence pack, codebook, audit trail, and an auto-generated AI disclosure statement an examiner can actually inspect. Most AI analysis tools do the thinking for you. QualIntel OS deliberately refuses to โ it does the busywork and keeps the interpretation provably yours.
QualIntel OS's answer
It depends what you need. If you want maximum speed โ automated theme generation across large document sets โ AI-native tools do that well. If your analysis has to survive a supervisor, an examiner, an ethics board, or a funder, QualIntel OS is built for exactly that moment: researcher-confirmed evidence, an accept/reject decision log, methodology-aware workflows (reflexive TA, grounded theory, IPA, Gioia, and more), and a disclosure statement generated from what actually happened rather than what you remember. Legacy tools like NVivo organise your data but leave all the work and none of the defence; generic chatbots do the work but destroy the defence.
QualIntel OS's answer
Postgraduate researchers โ master's and PhD candidates whose thesis has to survive examination โ plus their supervisors, independent research consultants, and programme evaluators who need to defend findings to funding boards. Anyone doing qualitative analysis where "the AI found the themes" is a disqualifying answer.
QualIntel OS's answer
The founder built it for his own problem: doing postgraduate research at a university whose AI policy demands declared, accountable AI use, while facing hundreds of pages of transcripts. Generic AI tools would do the analysis but hollow out the rigour; legacy software preserved rigour but did none of the lifting. QualIntel OS is the missing middle โ AI that carries the structure and retrieval while the researcher keeps every interpretive decision, with the proof generated automatically as a by-product of working.
QualIntel OS's answer
Next.js on Vercel for the web app, a Python/FastAPI API with PostgreSQL, Qdrant for semantic search, Anthropic's Claude for evidence retrieval and Voyage AI for embeddings (both under no-training terms), Clerk for authentication, and Stripe for billing. Hosting is on SOC 2 Type II certified infrastructure (Railway, US West).
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