Don't vibe code your customer intelligence

In the first week of February, an estimated $285 billion evaporated from global software stocks in 48 hours. The thesis: AI tools like Claude Code and Cursor are enabling anyone to build custom internal software, making expensive SaaS subscriptions obsolete.

"Vibe coding" was Collins Dictionary's Word of the Year for 2025. Teams are building faster than ever. Things that would have taken months are being prototyped in hours.

For most things, that's great. Prototype in an afternoon. Build the internal tool your team has needed for years. Automate the workflow nobody had time to fix. The barrier to building useful software has collapsed, and that's genuinely one of the more exciting things happening in product right now.

Vibe code your prototypes. Vibe code your internal tooling. Just don't vibe code your customer intelligence.

Why customer intelligence is different

Vibe coding works because the feedback loop is tight. You build something, it breaks, you fix it. Bad code is visible. Tests fail. The app crashes. You know when you're wrong.

That's the whole methodology. Move fast, iterate, let errors surface and correct course. It works because mistakes are catchable.

Customer intelligence has no feedback loop

When you wire an LLM up to your research data and start querying it, the outputs look right. The summaries are coherent. The insights sound plausible. There's nothing that obviously breaks.

But AI is confidently wrong. Not occasionally. Regularly. It conflates sentiment from different participants. It fills gaps with plausible-sounding inference. It presents synthesized outputs as fact. And critically, it doesn't surface what it doesn't know.

Here's what makes this particularly hard to catch: the people using the tool have no way to verify the answer. No link back to the original source. No quote from the participant who actually said it. No way to check whether the insight reflects a pattern or a single outlier. Just an answer that sounds right.

With code, you discover the mistake before it ships. With customer intelligence, the mistake ships with your product. You build the wrong thing, for the wrong reason, for the wrong person. By the time you find out, you've wasted a quarter, or damaged the trust of the customers you were trying to serve.

That's not technical debt. It's something harder to pay back.

We've been thinking about this problem for a long time. Not because we predicted vibe coding, but because the failure mode is the same regardless of how the tool gets built. The moment a team routes customer research through a system that can't verify its own outputs, the answers stop being trustworthy. We built Askable specifically so that wouldn't happen.

What it actually takes to get this right

At Askable, we've spent years building AI-native infrastructure for processing research data. Not because it was the quick option. Because we understood the problem deeply enough to know that shortcuts here are invisible until they aren't.

The transcription layer, something easy to treat as commodity, turned out to matter enormously. Generic services lose the nuance that makes qualitative research valuable: the hesitation, the correction, the moment where someone says one thing and means another. The retrieval system had to be built so that every answer traces back to a specific moment: a direct quote, a timestamp, a video clip. Evidence is scored before it surfaces, so a vague prompted response doesn't carry the same weight as an unprompted, specific account of real behavior.

Every session your team runs in Askable is analyzed automatically when it ends. By the time anyone asks a question, the knowledge base is already built, scored, and ready. When you query your data, you're retrieving evidence. Not triggering a summarization and hoping it's accurate.

It took years. This part is genuinely hard to get right, and the consequences of getting it wrong are invisible until they aren't.

Introducing the Askable MCP

Build aggressively. Prototype fast. Use the incredible tools at your disposal.

But your customer intelligence is the foundation everything else is built on. If that's wrong, everything downstream is wrong too.

At the end of April, we're releasing an MCP server that connects your Askable research repository to the tools your team already works in: Claude, ChatGPT, Cursor, Lovable, and more. Query your research in natural language, inside the workflows where decisions actually get made. Get answers backed by real quotes, real participants, and links to the original source.

Research, wherever decisions get made. Evidence you can actually trust.

Askable is an AI research platform that turns every study into a compounding knowledge base. Follow us on LinkedIn to be the first to hear what's coming.