Solution
Market research coding that keeps pace with your studies
qualcode.ai codes each response in isolation with two independent LLMs, calculates agreement automatically, and learns from every reconciliation — giving agencies and insights teams the audit trail, reliability metrics, and repeatability clients expect.
Built for throughput
Run thousands of responses through per-response isolated classification instead of stitching together ad hoc prompts. Each response gets its own API call — no batch contamination, no order effects — so results are auditable at the individual level.
Made for client delivery
Show clients Cohen's kappa and Krippendorff's alpha alongside the coded data. Two independent raters plus agreement metrics give clients evidence of rigor, not just your word.
Easy to reuse
Keep the same coding guide, measurement language, and reporting structure across tracker studies and one-off projects, or bootstrap a codebook with the three-AI suggestion workflow: two LLMs independently propose categories, a third merges them into a deduplicated draft.
Typical market research workflows
| Workflow | What you need | How qualcode.ai helps |
|---|---|---|
| Concept and ad testing | Fast categorization of large verbatim sets. | Two independent raters code each response in isolation. When clients ask how themes were derived, you show agreement metrics. |
| Tracker waves | Consistent category definitions across repeats. | Reusable guides plus self-learning from reconciliation make each wave sharper than the last. |
| CX and NPS follow-ups | Clear themes plus enough detail for reporting. | Exports are structured for slide decks, tables, and downstream analysis. |
| Agency QA | A process that can be checked, not just trusted. | Agreement metrics, reconciliation rationale, and full coding history per response — QA is built in, not bolted on. |
Why agencies keep coming back
- They can move from a short project kickoff to a first coded sample without training a whole team on new software.
- They can tell clients which model produced the results and how much agreement there was between raters.
- They can reuse a proven guide for similar studies instead of recreating the coding logic every time.
- Each reconciliation outcome feeds back as a training example, so repeat studies on similar topics produce sharper results with less manual review.