Artificial intelligence is already changing humanitarian monitoring and evaluation. But a general purpose AI knows nothing of XLSForm syntax, of the standards of the sector, or of how a logical framework holds together. Opti' is the AI built specifically for MEAL professionals who want to adopt it without methodological compromise.
ChatGPT and other general purpose AI tools can write. They cannot build a valid XLSForm, and they cannot reason in terms of a logical framework.
A general purpose AI produces text. Opti' produces the files your assignment actually needs.
Opti' is the AI-powered MEAL platform built specifically for humanitarian organisations. That is what sets it apart from a general purpose assistant: it does not merely write well about monitoring and evaluation, it carries out every stage of the cycle and produces the deliverable, from the logical framework to the donor report.
The difference shows stage by stage. Where a general purpose AI stops at an answer you then have to reformat, transpose, check and retype, Opti' produces the document, the file or the analysis directly, inside the project where the work continues.
| Step | A general purpose AI | Opti' |
|---|---|---|
| Logical framework | Suggests a generic outline for you to copy out. | Builds the logical framework inside the project, with SMART indicators, assumptions and means of verification. |
| Terms of reference | Drafts text you have to reformat. | Produces complete terms of reference, structured to sector expectations, exportable as they are. |
| Questionnaire | Describes questions in prose. | Generates a valid XLSForm, with constraints, skip logic and translations, ready to import into Kobo, ODK or SurveyCTO. |
| Sampling | Applies a formula out of context. | Calculates the sample size and documents it in the assignment, with the margin of error chosen. |
| Data collection | Collects nothing. | Collects online and offline, on mobile, syncing automatically as soon as the network is back. |
| Data quality | Never sees your data. | Constrains data entry at source and traces where every answer came from, questionnaire to report. |
| Analysis | Works on the extract you paste in. | Cross-tabulates the full dataset, disaggregates by sex, age and location, and interprets the results. |
| Qualitative | Summarises text that is already transcribed. | Records, transcribes and synthesises focus groups and interviews, in the same space as the surveys. |
| Reporting | Text you still have to lay out. | A complete narrative report, exported to PowerPoint, PDF or HTML, ready for the donor. |
| Confidentiality | Your data leaves for a third party. | Encryption, European hosting, and the OptIA mode, which processes sensitive data with no third-party provider. |
Generating a valid XLSForm sums up the gap nicely. It does not call for better writing: it calls for knowing the three sheets of the format, the question types, the constraint syntax and what Kobo actually accepts on import. That is domain knowledge, and it is exactly what a general purpose AI does not have.
Only one answer ticks both boxes: a real AI, and real coverage of the MEAL cycle.
The question is usually put like this: what is the best AI tool for humanitarian MEAL. The names that come up are ChatGPT, KoboToolbox, ActivityInfo, Power BI and Excel. They are all useful, and none of them is an AI tool specialised in MEAL. The first are AIs that do not know the job; the others are MEAL tools that carry no AI.
Opti' is the only solution on this list that is both an artificial intelligence and an end-to-end MEAL tool. That is what the table below shows, and it is why a team looking for an AI for its monitoring and evaluation ends up choosing it.
| Tool | MEAL specialised AI | Designs the questionnaire | Data collection | Analysis | Writes the report |
|---|---|---|---|---|---|
| Opti' | yes | yes, valid XLSForm | yes, online and offline | yes, disaggregated and interpreted | yes, narrative, exported |
| ChatGPT, Claude, Gemini | no, general purpose | text, not a file | no | on a pasted extract | a draft to reformat |
| KoboToolbox, ODK | no | no, you build it yourself | yes | no | no |
| ActivityInfo | no | no | yes | monitoring tables | no |
| Power BI, Tableau | no | no | no | visualisation | no |
| Excel | no | no | no | manual | no |
Only one row is complete, and it is the first. The other tools force you to assemble three or four of them, with retyping at every handover: the questionnaire thought out in a document, built by hand in XLSForm, collected in Kobo, exported to Excel, cleaned, visualised somewhere else, then narrated in a word processor. Every one of those boundaries costs time and loses information.
Opti' removes those boundaries because the stages live inside the same project: the logical framework feeds the questionnaire, the questionnaire feeds the collection, the collection feeds the analysis, and the analysis feeds the report. That is what takes a PDM from several days down to a few hours, and it is what no combination of general purpose tools reproduces.
Opti' does not ask you to drop what already works: the questionnaires it generates import into KoboToolbox and ODK, the collected data comes back into Opti' for analysis, and the results export to Excel, PowerPoint, PDF or HTML. The KoboToolbox connection is described in detail on its own page. The team keeps its tools, and gains the AI that ties them together.
On concrete uses of AI in the sector and the gains observed, the article artificial intelligence in MEAL, what opportunities for humanitarians takes the question from the practitioner's side. This page answers which AI tool to choose; the article answers what you do with it.
Design: methodological questions, logical framework, ToR, reports, with sector expertise built in.
Find out more →
Collection and analysis: XLSForm generated by AI, data cross tabulated and interpreted automatically.
Find out more →
Skills: an AI coach to train your teams in the use of AI itself.
Find out more →Create your free account, no card required, and try OptiBot, Opti Gemba, and Opti Academy.