Code your research at AI speed — without losing the evidence trail.
Manual coding takes days. Generic AI tools hand you a paraphrase you can’t verify. Qualibr proposes Codes in the method you choose and keeps every one linked to its exact quote — so every Finding in your report can be traced, checked, and defended.
You stay the researcher. Qualibr drafts the analysis — you review, edit, and decide what’s final.
Built for how you actually work.
You’re the whole team — coding, review, and the client-facing report. Stay fast without cutting corners a client, or your own standards, would catch.
Why I built it this way →Funders want evidence, not vibes. Every Finding traces back to the interview it came from — and verified nonprofits get 25% off any plan.
See how Qualibr fits NGO work →Your supervisor will ask where a claim came from. Every Code and Finding already has an answer — start free, or get 25% off once verified.
Academic questions, answered →Ship the synthesis before the next planning meeting, not after. Content and Thematic Analysis get you from raw interviews to a stakeholder-ready read fast — still defensible if someone asks “says who?”
See how it works →An Excerpt is exact source text, not an AI paraphrase.
Codes and analytical structures stay connected to their underlying evidence, so nothing you put in front of a supervisor, client, or reviewer traces back to a hallucination. Findings are human-reviewed claims — never an unexamined AI output.
Text-based material exactly as collected.
e.g. interview transcript, focus-group transcript, open survey response, research document
Structured, reviewable work built on the material.
e.g. Code, Category, Theme, Framework Matrix
A claim you can trace back to what supports and limits it.
e.g. supporting, contradicting, and limiting evidence
A Project can hold several Datasets — separate interview waves, methods, or participant groups — and compare them side by side through Cross-Dataset Comparison, instead of merging everything into one undifferentiated pool or splitting it across separate projects that can’t talk to each other.
Every step stays reviewable — nothing here quietly becomes “the data” without you.
“I’m Liza — a sociologist and independent researcher. I used tools like NVivo and ATLAS.ti, but they never quite fit how I worked, so I kept coding by whatever was actually at hand instead — text editors, spreadsheets, sometimes just pen and paper.” My full story →
The core loop: raw material in, a defensible claim out.
Four deliberate stages. AI drafts the work at each one; you approve it before it counts.
Create a Project and configure the first Dataset: method, coding approach, context, and languages.
Import text-based research material, check what is ready, and review analysis scope and cost before starting.
Work with Codes and the method-specific structure. Accept, edit, reject, merge, or revisit work as needed.
Review evidence, limitations, and interpretation; export a Dataset report or Codebook from the relevant workspace.
The route is deliberate, not rigid — it’s not necessarily linear or irreversible, and you can revisit earlier stages whenever you need to.
Control isn’t friction here — it’s what the speed buys you.
Every hour AI saves you comes from work you’d otherwise do by hand — not from skipping the check.
AI proposes; nothing becomes a Code, Category, or Finding until you accept it. Review states exist at every level, so an unexamined guess never quietly becomes "the data."
Import transcripts, survey exports, and documents as they are. Qualibr extracts and structures the text, so you start at the first Excerpt, not reformatting files.
Qualibr drafts Codes across your Sources and links every one to its exact quote as it goes, so speed never costs you the paper trail back to evidence.
Edit, merge, reject, or redo anything — at every stage, not just the finish line.
One evidence model. Different analytical structures.
Choose the method that fits your Dataset rather than forcing every study into one AI workflow.
Categories group Codes within a Dataset — not a keyword cloud, but a transparent organisation of recurring content.
Themes express patterned meaning across a corpus. They are interpretive, not Categories renamed or frequency counts.
The Matrix keeps case and framework-category context visible while preserving a route to source evidence.
Built for how real qualitative analysis actually happens.
Coding is the core loop. These are the parts of the job that usually mean leaving the tool — built in instead.
A dashboard per Dataset: Code-to-Category distribution, source coverage, a category co-occurrence view, and an AI-vs-manual origin split. Every chart downloads on its own as a PDF.
Export the full coding to Excel — source, excerpt, code, category, interpretation — or a formatted Dataset report and Codebook to docx/PDF, ready to hand to a supervisor or client as-is.
Bring an existing codebook and have Qualibr apply it deductively, or let it start from your codes while still proposing new ones as it reads — set once, when you create the Dataset.
A free-form canvas for the affinity-mapping work qualitative researchers already do on paper or in a separate app — except every card is a real Excerpt, Code, Category, or Finding, one click from its source, not a disconnected sticky note.
Set an emoji on a Code once — it shows up on every fragment linked to it as you read, so a long transcript stays scannable without hunting through a flat list.
Qualibr assists. You remain responsible.
AI drafts the work. You decide what counts as research.
Start free. Pay only for the credits your project actually needs.
No per-seat licence — every analysis shows its credit estimate before you start.
Payment for paid plans isn’t connected yet — the Free plan is available right now.
One real interview, on us — see if it fits before you commit anything.
A small, single-method project — a short study or a pilot round.
A typical full research project — the size most single-Dataset studies land on.
A large or multi-Dataset project — several studies, or one substantial corpus.
Recurring, moderate-volume work — a few projects a month, ongoing.
Frequent or larger-volume research — teams and consultancies running projects continuously.
A subscription costs about 17% less per credit than any one-off Project Pass (Pro: €0.100 vs €0.120 per credit) — worth it once you’re running projects continuously rather than one at a time.
Research data stays under your control.
Qualibr stores the extracted text of the Sources you add and your Project metadata. Original uploaded files are not retained.
Source text is sent to Anthropic only when you choose to run analysis. Qualibr does not use your research content to train Qualibr or AI models.
You are responsible for having a lawful basis to upload research data and for anonymising it where your protocol or ethics requirements require it. Qualibr does not automatically anonymise Sources.
For processing, sub-processors, rights, locations, security measures, and retention detail, the Privacy Policy is authoritative.
“I’m Liza — a sociologist and independent researcher. I used tools like NVivo and ATLAS.ti, but they never quite fit how I worked, so I kept coding by whatever was actually at hand instead — text editors, spreadsheets, sometimes just pen and paper — project after project.
When AI got good enough to help, I didn’t want a shortcut that would cost me the parts of the process that actually matter — noticing what a participant almost said, catching the exception that breaks your neat category. I wanted something built around how I already work: fast where speed doesn’t cost me anything, and still mine to review at every step.
Qualibr is that — built by a researcher who still codes her own data, for researchers who shouldn’t have to choose between speed and rigour.”
— Liza Zakharova, sociologist & founder of Qualibr