FOR NGOs & M&E TEAMS

From raw text data to a defensible report for your team or donor.

NGOs and M&E (monitoring and evaluation) teams face a specific version of the qualitative analysis problem: findings that hold up to a funder or board, on evaluation timelines that don’t leave room for weeks of manual coding, on budgets that don’t stretch to enterprise CAQDAS licences built for university research departments.

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Every Code traces to its source quote
Verified nonprofits get 25% off any plan
WHY GENERIC ADVICE DOESN’T QUITE FIT

A single evaluation project looks like none of the standard advice.

Most qualitative-analysis advice is built for academic researchers with semester-long timelines, corporate teams with per-seat budgets, or foundations running continuous impact-tracking systems. A lean team synthesising 15–40 interviews, focus groups, or survey responses into one report on a project deadline is usually none of these.

“Where did this finding come from?”

→ Every Finding traces to its exact source quote.

Report due in two weeks. Manual coding takes months.

→ Qualibr drafts the first coding pass in minutes.

Enterprise CAQDAS pricing assumes a university budget.

→ Pay per project — plus 25% off for verified nonprofits.

You already have an indicator framework. Starting from zero wastes it.

→ Hybrid and deductive coding apply it directly.

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A PRACTICAL WORKFLOW FOR A TYPICAL EVALUATION

Five steps to a report your board can act on.

1
Collect field interviews or open survey responses

Transcripts, notes, or raw text, as collected.

Still designing the interview? See the interview guide →
2
Let Qualibr draft the first coding pass

Surface candidate codes fast instead of starting from a blank page — the same move as manual open coding, before the axial pass that groups codes into categories.

Open coding vs. axial coding →
3
Review against your own framework or evaluation questions

This is where your judgement, not the software’s, does the real work.

4
Pull supporting — and contradicting — quotes for each finding

Every quote traces back to the exact source it came from.

5
Structure the write-up around Findings your funder or board can act on

Export a formatted Dataset report or Codebook straight to docx or Excel — not a raw transcript dump.

What this isn’t for: a continuous impact-measurement system tracking dozens of grantees over years is a different, heavier category of tool, closer to a dedicated MEL platform. Qualibr is built for the more common case — one evaluation, one deadline, a batch of material that needs to become a defensible report once.

See the dashboard, exports, and the rest of the toolkit →

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PRICING FOR NGOs

Priced per project, not per seat — with a nonprofit discount on top.

No annual licence to justify against a single evaluation. Start free, then pay only for the credits the project actually needs.

Free
€020 credits

Enough to code your first real Source before you decide whether to pay for anything.

Project Pass Standard
€48400 credits

The size most single-Dataset evaluation projects land on.

Verified nonprofit affiliation gets 25% off any plan above, including subscriptions for organisations running evaluations continuously. See the full Plans breakdown →

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FAQ

Questions NGO and M&E teams actually ask.

Do I need a research background to use AI-native qualitative analysis for an evaluation report?

Not a formal one, but you do need to stay involved in reviewing what the AI proposes: the tool can accelerate the mechanical parts of coding, but deciding what a pattern actually means for your program is still a judgment call only someone close to the work can make.

Can this replace a full external evaluation?

For high-stakes, funder-mandated external evaluations, no — those typically require independent evaluators for credibility reasons regardless of tooling. For internal synthesis, monitoring, and most donor reporting, a well-structured, traceable internal analysis is often exactly what’s needed.

What if our organisation already uses a fixed coding framework or indicator set?

That’s a good starting point, not an obstacle: Qualibr’s deductive and hybrid coding let you code against your existing framework rather than generating a new one from scratch, which usually saves time rather than costing it.

How is sensitive program or participant data handled?

The same privacy and security handling applies to every Source regardless of plan — sensitive material isn’t gated behind a separate paid tier. You remain responsible for having a lawful basis to upload the data and for anonymising it where your protocol requires it; full detail is in the Privacy Policy.

Why I built Qualibr this way, from a sociologist’s perspective →

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