Quality text-data annotations with
advanced
LLMs and AI
Analyse large volumes of text-as-data with advanced AI. No code required. Qualitative judgements at large-N scale.
From raw text to a checked, coded dataset in six steps.
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.
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.
Run a pilot
Test runs (50 observations, pennies of compute) let students iterate on their codebook before committing to the full corpus.
Run the full corpus
Once the pilot looks right, code the whole dataset in one run. You see the price before you start, and the run keeps going while you do something else.
Check it holds up
Code a sample by hand, or compare two runs against each other, and read the agreement as Krippendorff’s alpha. Reviewers ask for this number; the app gives it to you.
Export with confidence
Download your coded dataset with per-cell certainty scores, AI rationale, and a full audit trail attached.
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.
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.
Pricing on 3,000 cells coded.
1,000 political social-media comments, 3 variables per row. The same dataset, priced against a trained annotator.
Hand-coded by a Danish RA
$214.29
~10 hours · $21.43/h
Ten hours of trained annotator time, before double-coding, review passes, or a coffee break.
Results in minutes
$1.25
minutes
Real-time coding with an auditable explanation on every cell. Live the moment your dataset lands.
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.