Long Text or ID Examples

Real examples behind every metric and insight.

18%of cancellations mention onboarding confusion3 of 412 examples

resp_10482cancel_reason

Signed up in January, but the onboarding flow lost me at step three. I never finished setup and just stopped logging in.

resp_11907cancel_reason

Support was fine and the pricing was fine. I could not work out how to invite my team during setup, so we went elsewhere.

resp_12344cancel_reason

The setup guide was confusing and I gave up after twenty minutes. Might come back if it gets simpler.

every metric, one click from the records behind it

HOW IT WORKS

Why a Number Should Never Stand Alone

Steeped AI connects every metric in your report back to the real, raw examples behind it, whether that's a unique ID or dense, long-form text, so numbers never have to stand alone.

Pair it with topic mapping to quantify qualitative data like open-ended feedback or interview transcripts, then trace a claim like "18% of cancellations mention onboarding confusion" down to the exact bolded sentences that back it up. You never have to just trust that a form, a machine classification, or an AI-assigned topic was filled out correctly, the proof is one click away, for every metric, every time.

Examples

See Examples For

13% of "topic_appearance" records were "Bipedal / Upright"

"Motorist has a possible nighttime sighting west of Albion. I was coming home from work, heading from Albion towards Fairfield, which would have been about 11:30 that night. I started smelling something first, I had my window down. And all of a sudden I saw this huge figure off to the side of the road, between the ditch and the field. It is walking upright, it wasn't no coyote or wolf, we don't have bears up there that I know of, I grew up there."

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Column: sightingfortopic_appearance: Bipedal / Upright

QUANTIFIED TEXT

Quantify Any Qualitative Data

Pair this with topic mapping and any long-form text, product feedback, transcripts, open-ended survey responses, becomes fully quantified and connected to the rest of your quant data through relational metrics. Trace a claim like "22% of churn mentions pricing confusion" down to the exact bolded sentences that support it, across every response in your dataset. Messy text stops being a distraction and becomes one of your most powerful data sources.

EVERY METRIC

Real Examples, Every Metric

Any column, a unique ID or a field packed with long-form text, can be included alongside its metrics, so you instantly see the real examples behind every number. Numbers alone can feel abstract; real examples make them come alive for you and for anyone you share a report with. That context is what turns a metric into something people actually believe and act on.

SEE IT YOURSELF

Validate Every Insight Yourself

You don't have to take it on faith that a user's form, a machine learning classification, or an AI-assigned topic was labeled correctly, you can see the raw examples behind every quantified metric with your own eyes. Most examples are a single click away from the thousands of metrics and relational calculations running in any active project. Trust isn't asked for here, it's earned, one example at a time.

The insights are already in your data.

Ask your data anything. Get real findings ranked by impact, with AI reports your team can present and share on the spot.