# Verbata

> A tool for sorting thousands of interview responses quickly

- **Client**: Deka Insight
- **Date**: 2024
- **Metrics**:
  - Processing Time Reduction: ↓ 864x (From 1-3 days to just 5 minutes)
  - Researcher Efficiency: 300% (More time for strategic analysis and insights)

## Details

Deka Insight had a very unglamorous problem. Researchers were spending one to three days reading and categorizing thousands of interview responses, a process called verbatim coding. It was necessary work, but it kept them away from the analysis their clients were paying for.

## The first version

I built the first version quickly. It had one job: read the text and categorize it. No fancy features. I wanted to find out whether the basic idea was useful before spending time on everything around it.

It worked. The job went from days to about five minutes. It also showed me what the first version was missing: researchers needed to see and control how the AI made its decisions.

## The second version

I rebuilt it with a blue theme and the controls the researchers had asked for:

- **Customizable prompts**: Researchers can adjust how the AI interprets responses
- **Better coding process**: Clearer about decision-making with confidence scores
- **Batch processing**: Handle hundreds of responses at once
- **Quality checks**: A person can review the output before it is used

## The hard parts

The output had to be accurate enough for ISO-certified research. It also had to handle Indonesian phrasing and research terminology, while keeping sensitive client data secure.

Qualitative research is full of context and emotional detail. You cannot throw a pile of text at an AI and expect it to understand the nuance without giving it the right framing.

## What I learned

Ship the small version, watch somebody use it, then fix what you got wrong. The first version proved the idea. The second made it fit their daily workflow.