Built for academic and market research
AI survey coding for open-ended responses
Code open-ended responses with two independent AI raters, publishable agreement metrics, and a reconciliation workflow built for academic and market research teams.
What we deliver
Coding you can cite
You have 5,000 open-ended responses.
Your deadline is in two weeks.
Manual coding would take weeks, a second coder adds cost, and the cleanup work only starts after the first pass is done.
Pasting everything into a raw chatbot is faster, but it leaves you without reliability metrics, workflow discipline, or a credible methods story.
qualcode.ai is designed for the middle ground: faster than manual coding, more defensible than ad hoc prompting.
How it works
A dual-rater workflow for survey coding, reliability, and reconciliation.
Upload your data
CSV or Excel. Choose the response column you want to code.
Define the coding guide
Start from your own categories or let AI suggest a first draft you can refine.
Two AIs code independently
OpenAI and Anthropic process the same responses separately for real inter-rater comparison.
Review agreement metrics
Cohen's kappa, Krippendorff's alpha, and agreement rates are calculated automatically.
Reconcile and export
Resolve disagreements, improve the guide, and export clean outputs for analysis and write-up.
Research-grade, not research-adjacent
Built for publication, review, and client delivery.
Dual-rater architecture
Two independent LLMs code every response so you can measure agreement instead of trusting a single output stream.
Reliability metrics
Cohen's kappa, Krippendorff's alpha, and per-category agreement are calculated without extra spreadsheet work.
Reconciliation workflow
Review disagreements, improve definitions, and rerun with a clearer audit trail than ad hoc prompting can offer.
Optional training data
Start with zero training examples and add guidance later as your codebook matures.
Export-ready outputs
Move from classification to SPSS, R, Excel, or reporting workflows without rebuilding the dataset by hand.
Trust and compliance
Public trust, privacy, DPA, and transfer documentation support research teams that need procurement-ready answers.
Explore the pages built for different search intents
Jump into the audience, comparison, and methods pages that explain qualcode.ai from different angles.
Solutions overview
Browse the full solutions cluster for academic, market research, and public health teams.
Academic research
Methods sections, reviewer credibility, and citation-ready workflows.
Market research
Faster coding cycles, auditability, and client-ready outputs.
Public health
Sensitive data, governance, and compliance-conscious workflows.
ChatGPT vs qualcode.ai
See the difference between raw prompting and a dual-rater workflow.
NVivo vs qualcode.ai
Compare manual-heavy coding software with AI-assisted agreement workflows.
MAXQDA vs qualcode.ai
Compare document-oriented CAQDAS workflows with row-level survey coding and reliability reporting.
Methods section template
Copy a reporting template and pair it with the citation and agreement docs.
Honest comparison
Different tools solve different problems. This workflow is built for defensible coding, not generic text automation.
| Approach | Where it falls short |
|---|---|
| Manual coding | Slow, expensive, and hard to scale when you need a second coder and reconciliation time. |
| NVivo / MAXQDA | Strong for broader document-based qualitative analysis, but high-volume survey coding still means more manual setup, export cleanup, and separate reliability work. |
| Raw ChatGPT / Claude | Fast to start, but no built-in agreement metrics, no systematic reconciliation, and little audit structure. |
| qualcode.ai | Designed around row-level dual-rater coding, a three-AI codebook suggestion workflow, built-in agreement reporting, and structured exports for real research workflows. |
Start with pricing, docs, or trust
Explore cost estimates, methodology guidance, or compliance details before you create an account.
Your responses are waiting
Join the waitlist to get early access to dual-rater coding, agreement metrics, and the docs cluster built to support your methods story.
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