Making Static Research Answer Questions It Was Never Asked
A global brand had 1,770 research posts across 10 markets — and no way to use them beyond the original report. I built the system that turned that archive into an interactive behavioral simulator product teams could query in minutes.
Research that couldn't answer a follow-up question.
A global consumer goods brand had done the work — 1,770 posts from 170 participants across 10 international markets, exploring morning health and wellness behavior. And then it sat in documents. Product teams couldn't run "what-if" scenarios, simulate how a persona would respond to a disruption, or use the insights for anything beyond the original report. A product manager wanting to test 10 ideas would have to re-read hundreds of transcript pages. Most just guessed.
The hard part wasn't building a chatbot over the data — it was making the output trustworthy. A system that hallucinated plausible-sounding answers would be worse than useless; it would actively mislead product decisions. Every response had to be grounded in what real participants actually said, with the retrieval and generation engineered so credibility never gave way to fluency.
A three-stage pipeline that grounds every answer in real data.
- Built a context classifier that detects nine behavioral signals from each query — physical context, emotional state, and external influencers — so the system understands the situation before it answers
- Engineered semantic retrieval over a 598-record Q&A corpus (80% similarity / 20% keyword weighting) extracted from research spanning 38 personas across 8 markets
- Designed persona-grounded generation: conditional templating that activates context-specific behavioral frames and responds in-character from retrieved data, not invention
- Modeled habit-disruption probability with condition-combination rules and five scenario toggles, focused on the Overextended Caregiver archetype for the MVP
- Documented the enterprise translation path from the proof-of-concept stack to Azure production equivalents, so the build had a credible road to scale
The hard part wasn't generating answers — it was making sure every answer traced back to something a real person actually said.
Weeks of synthesis became an afternoon of queries.
The platform turned a static research archive into a decision-support tool. Product teams stopped guessing and started simulating — running scenarios against grounded personas in minutes instead of re-reading transcripts for weeks.
If your organization is sitting on expensive research — or any rich dataset — that gets used once and shelved, I build the systems that turn it into something teams query every day. I've taken raw qualitative data through classification, retrieval, and grounded generation into a working product, and mapped the path to production. That's the kind of hands-on technical build I'd bring to your team.