Forms fail at the last mile.
Literacy, device access and survey fatigue decide whose experience makes it into the dataset.
Voice AI evaluation for social impact
Listen at scale. Keep the human texture.
Zolabs runs AI-led phone interviews in people’s own languages, for evaluations, programme feedback and qualitative evidence. Reach more people, hear the story behind the score and keep every finding connected to the original conversation.
Phone interviews in local languagesFindings traced to what was saidDesigned for social impact
Z
Livelihoods outcome studyVOICE EVALUATION · 3 LANGUAGES
LIVE01
Participant describes income stabilityTranscript retained in the original language
EVIDENCE02
Theme: confidence in decision-makingLinked back to the supporting excerpt
REVIEWIllustrative interface · every summary should lead back to the voice behind it
The gap you already feel
Between a form and a formal evaluation, too much experience goes unheard.
Forms and surveys are useful, but they lose context. Traditional qualitative research preserves nuance, but is hard to repeat at programme scale. Zolabs sits in that gap.
Checklists cannot capture agency.
A rating says what changed. A conversation helps explain how, why and for whom.
Analysis can become a black box.
A polished theme means little when a reviewer cannot trace it to the words that support it.
Insight arrives after the decision.
Transcription and coding backlogs turn living programme feedback into a late report.
Available today
Phone conversations for evaluation and programme learning.
Use AI-led phone conversations to collect and organise qualitative evidence without asking every participant to navigate another app.
Visit zolabs.ai ↗Design the conversation
Turn an evaluation objective or survey into a guided interview that can probe, clarify and adapt while staying inside the study’s boundaries.
Call people in their language
Run phone interviews at programme scale and reach participants in the most familiar way: a conversation.
Structure what was said
Bring transcripts, responses and emerging themes into one place for review and follow-up.
Review the evidence
Keep human researchers in control of interpretation, with access to the underlying conversation rather than only an AI summary.
The Zolabs model
From a research question to evidence your team can inspect.
The workflow keeps collection, interpretation and review connected instead of treating the call as the end of the work.
Start with what you need to learn.
Define the evaluation question, audience, language and boundaries for the conversation.
Let the AI run the phone interviews.
Reach participants at scale while allowing an answer to unfold in their own words.
Move from transcript to reviewed finding.
Organise themes and structured responses, then let a researcher verify the evidence behind them.
Evidence, not just output
Every conclusion should have a way back to the conversation.
The goal is not to replace a researcher’s judgement. It’s to take away the admin around listening, so teams can spend more time asking better questions and interpreting what they hear.
- Keep source material alongside structured findings
- Review AI-generated themes before they become claims
- Combine what people said with programme data
- Carry exceptions and contradictions into the final story
A visible evidence trail
Original voice conversationWhat the participant actually said
Transcript and translated viewSearchable, reviewable source material
Theme or structured responseAn AI-assisted interpretation
Researcher-reviewed findingA claim ready for analysis or reporting
Where it fits
Every moment where a real conversation matters.
Use Zolabs when the programme needs more than a checkbox but cannot send an interviewer everywhere.
Impact and outcome studies
Collect mixed-method evidence across a large participant group while preserving explanations and lived experience.
Journey-based follow-ups
Check in with learners, patients, mothers, alumni or entrepreneurs at the moments that matter.
Beneficiary feedback
Hear what happened when programme teams were not in the room, in the language people prefer.
Large-scale qualitative studies
Extend the reach of a research team without reducing every response to a closed question.
Alumni and cohort outcomes
Understand longer-term change in the words of the people who experienced it.
Onboarding and verification
Gather structured information through conversation and pass unclear cases to a person to follow up.
Where Zolabs is going
From voice evaluation to an end-to-end evidence workspace.
Today the product is centred on evaluation through AI phone interviews. Next is a broader platform that supports a study from start to finish without erasing researcher judgement.
Voice-led evaluation
AI interviews in local languages, managing participants and calls, transcripts, pulling out answers, and review.
Deeper analysis
Stronger coding, comparison across groups and clearer links between programme data and qualitative evidence.
Study-to-report workflow
Research design, evidence management, collaborative interpretation and reporting in one system where every finding can be traced.
“Most of what we call data in the social sector is a bit dead. Zolabs is an attempt to build a grammar for listening that sounds less like a spreadsheet and a little more like a person.”
Akshay · Founder, EdZolaSee Zolabs with your evaluation use case.
Bring the questions, languages and participant journey. Explore how a phone-based study could work for your programme.