Turn text into
structured
evidence.

Analyse large volumes of text-as-data with advanced AI. No code required. Qualitative judgements at large-N scale.

0
social media comments
$0.00
total cost
0 hour
runtime
parliamentary_questions.csv · row 1847
text_content
Question to the Minister: The government has categorically failed to address climate concerns in rural agricultural communities, leaving farmers without recourse or support structures. What do you intend to do about that?
id 1847source Hansarddate 2024-03-14
coded variables
Political Tone
Issue Domain
Geo. Focus
ai rationale
ai rationale

Used by researchers atAarhus UniversityDepartment of Political Science · Public Policy course · full-year cohort
workflow

From raw text to coded dataset in four steps.

1

Ingest your data

Upload a CSV. Interpret AI detects the delimiter and lets you point at the text and ID columns. A project holds your dataset, your variables, and the AI configuration.

2

Define your schema

Create categorical or scaled variables. Write natural-language coding instructions. Every variable returns a coded value, an uncertainty estimate, and a written explanation.

3

Run a pilot

Test runs (50 observations, pennies of compute) let students iterate on their codebook before committing to the full corpus.

4

Export with confidence

Download your coded dataset with per-cell certainty scores, AI rationale, and a full audit trail attached.

capabilities

Built for rigorous social science.

Certainty scores

Uncertainty estimates attached to every coded result. Filter, sort, and review low-certainty cells before export.

AI rationale per cell

A written model-logic explanation attached to every coded result. Click any cell to read the argumentation behind the decision.

Test-run-first workflow

Run 50 observations for pennies before the full corpus. Iterate on the codebook until it holds; only then commit.

Reliability round-trip

Export the AI codes to CSV, hand it to a human coder to re-code, upload the human sheet back. Interpret returns the disagreement set for inter-coder reliability.

Compare runs

Run the same dataset under two model configurations, see per-cell diffs, download only the mismatching cells for adjudication.

Flex service tier

For OpenAI’s GPT-5 family, Flex runs cost ~50% less than Standard while still returning synchronously. Pick per-run.

trust & transparency

Every decision is explainable.

LLM-assisted analysis is only credible when researchers can audit how every coding decision was reached. Transparency is built in from the start.

Full audit trail
Every run is logged: model version, prompt configuration, temperature, and timestamp. Reproduce any result.
Low-certainty review queue
Cells below your confidence threshold are automatically flagged for manual review before export.
Intercoder reliability export
Export pilot results alongside AI certainty for direct comparison with human coders.
Variable versioning
Coding instructions are versioned. Re-run analyses on updated schemas without losing prior results.
Certainty distribution · Pilot run247 rows
90–100%High confidence
162
75–89%Moderate
58
60–74%Review flagged
19
<60%Manual required
8
Recommend: review 27 flagged cells before full runReview →
what it costs

3,000 cells coded three ways.

1,000 political social-media comments, 3 variables per row. Same dataset, three price paths.

research assistantManual

Hand-coded by a Danish RA

1.500 kr

~10 hours · 150 kr/h

Ten hours of trained annotator time — before double-coding, review passes, or a coffee break.

interpret · instant

Results in minutes

$5.00

minutes

Real-time coding with an auditable explanation on every cell. Live the moment your dataset lands.

interpret · batchAvailable soon

Overnight processing

$2.50

next morning

Half-price batch runs return by the following morning — best for annotation work you can queue.

Free to start

Want to see how Interpret AI
handles your own dataset?

A 20-minute demo on the corpus you bring. We will help you spin up a starter codebook on the call.

Book a demo Try it now