I Built ScribeFlash to Turn Customer Interviews Into Evidence-Backed Insights

AI makes it easy to transcribe a customer interview. But transcription is only the beginning.

The real challenge starts when you have 10, 20, or even 50 interviews.

You need to figure out what people have in common, where their opinions differ, and which findings are actually supported by what customers said.

I wanted to make that process easier, so I built ScribeFlash, an AI-powered research workspace for turning customer interviews into evidence-backed insights and reports.

🔗 Try it here: https://scribeflash.com

The Problem I Wanted to Solve

Imagine you're building a SaaS product.

You've interviewed several potential customers about their workflows, frustrations, and willingness to pay.

Now you have hours of recordings and pages of transcripts.

You could paste everything into ChatGPT and ask for a summary. That's useful for a quick overview, but it doesn't solve the entire research workflow.

You still need to answer questions like:

Which problems were mentioned most often?

What did different participants say about the same feature?

Which findings have strong supporting evidence?

Did anyone disagree with the majority?

Can I trace a conclusion back to the original interview?

A polished AI summary isn't necessarily a reliable research finding.

That's the gap I wanted ScribeFlash to address.

What I Built

ScribeFlash combines transcription, interview organization, AI-assisted analysis, and report creation in one workspace.

Here's how it works.

1. Upload and transcribe interviews

Upload audio or video interviews.

ScribeFlash generates transcripts with speaker labels and timestamps, so you can review the conversation without manually typing everything.

1. Organize interviews by research questions

Instead of reading transcripts individually, you can organize participant responses around specific research questions.

For example:

What's your biggest problem with the current workflow?

How are you solving it today?

Which features would make your work easier?

Would you pay for a better solution?

This makes it much easier to compare answers across participants.

1. Discover patterns and contradictions

AI helps identify recurring themes, common pain points, and disagreements across interviews.

For example, one group of users might prioritize automation, while another cares more about accuracy or control.

Those differences can be just as useful as the similarities.

1. Connect findings to original evidence

This is one of the features I care about most.

Instead of presenting an AI-generated conclusion without context, ScribeFlash connects findings to the original interview evidence.

You can review supporting quotes and verify the reasoning before using a finding in a report.

1. Turn research into reports

Once you've reviewed the findings, you can organize them into a structured research report.

The goal is to spend less time manually assembling documents and more time making product decisions.

One Important Design Decision: Keep Humans in Control

While building this product, I kept coming back to one question:

How do we make AI useful for research without treating every generated insight as a fact?

LLMs can summarize conversations and suggest patterns, but they can also overlook context or make a weak finding sound convincing.

That's why ScribeFlash follows an evidence-first approach.

AI can help with analysis, but the researcher still reviews and confirms findings before they become part of the final report.

I think this human-in-the-loop approach is especially important when research influences product strategy or business decisions.

Who Is It For?

I built ScribeFlash primarily for:

Indie hackers validating SaaS ideas through customer interviews.

Product managers collecting customer feedback.

UX researchers comparing user interviews.

Startup founders trying to understand early customers.

Small teams that need research insights without a complicated workflow.

There's a free plan available, so you can try the workflow with your own interviews.

What's Next?

I'm interested in making the transition from customer conversations to actionable product decisions even smoother.

For now, I'm especially interested in feedback from developers and founders who regularly talk to users.

How do you currently analyze customer interviews?

Do you use spreadsheets, Notion, ChatGPT, specialized research tools, or something you've built yourself?

I'd love to hear what works for you and what's still frustrating.

If you'd like to explore the project:

ScribeFlash: https://scribeflash.com

Thanks for reading!