01 — Research problem

International comparison fragments when every market uses its own format.

Local studies produce rich information, but formats, languages and levels of synthesis vary. An insight from Poland may be phrased, categorized and archived differently from a signal observed in Portugal. The central team then spends substantial time harmonizing deliverables before comparing findings.

I framed the problem as a reproducible transformation chain. The goal was to reduce the mechanical work of transcription, translation, rewriting and classification while retaining the source, market, severity and context of each observation.

02 — Design choice

Build a working tool to test the contract between the team and artificial intelligence.

The prototype accepts three entry points: pasted verbatim, audio recording and a stream of public reviews. Audio is transcribed, language is detected, and translation plus a product glossary harmonize vocabulary. Extraction then works in free mode to discover themes, or guided mode to apply the same research framework across markets.

The output follows a strict JSON — JavaScript Object Notation — schema imposing the same fields on every response: theme, severity, insight, recommendation, provenance, market and potential cross-market signal.

03 — Research product

The repository turns extractions into usable team memory.

Each insight can be reviewed, edited, filtered by market, source or theme and linked to its original data. A question and group library supports reusable interview guides. A control dashboard aggregates studies and surfaces time-to-insight as well as recurring themes.

The system covers four markets, four source connectors, two analysis modes and four main taxonomies. Most importantly, the prototype shows that a team can share one method without forcing every country into the same collection channel.

04 — Scope

Automation prepares comparison; research validation remains essential.

The model proposes an initial structure and reduces preparation time. A researcher checks translation, insight wording, severity and recommendation before distribution. This step prevents transcription errors or cultural nuance from silently becoming product decisions.

For a ResearchOps team, the next evaluation should measure inter-researcher agreement, actual time saved, translation quality by market and the proportion of corrected extractions. These indicators evaluate the system as a research tool rather than a standalone artificial-intelligence demo.