Solutions
Solutions for teams coding open-ended survey responses
Explore the qualcode.ai pages built for academic research, market research, and public health teams. Each one highlights the same core workflow from a different angle: dual-rater coding with per-response isolation, agreement reporting, self-learning reconciliation, and three-AI codebook suggestion.
Academic research
For methods sections, reviewer credibility, and projects where defensible reliability reporting matters.
Market research
For agencies and insights teams that need faster throughput, clearer audit trails, and client-ready outputs.
Public health
For sensitive datasets, governance-conscious workflows, and teams that need transparent reporting.
What stays consistent across every solution
- Two independent AI raters code each response in its own isolated API call — no cross-contamination, no order effects — so agreement metrics reflect genuine classification, not shared context.
- Two independent LLMs propose categories from your data, then a third merges and deduplicates them — so the first draft reflects two distinct perspectives, not one model's bias.
- Reconciliation outcomes feed back as training data, so the system gets sharper with every coding cycle — start with zero examples and build precision through use.
- Agreement metrics, reconciliation history, and structured exports are ready for reviewers, clients, and stakeholders.