Custom research and analytics platforms — survey to insights in minutes.
We've shipped a market-research platform that runs cross-tabs, driver analysis, K-means segmentation, TURF, focus-group transcription, and auto-coding of open-ends — without anyone writing SPSS syntax. If you need yours, here's what's underneath.
A production-grade research module already built and running. We ship a custom one for clients who need their own.
ragenaizer.com / researchWhat goes inside a real research.
These are the moving parts we've shipped before. Your custom build picks a subset — and we tell you upfront which parts are worth re-implementing and which ones aren't.
Survey ingestion
CSV / Excel / Decipher / Qualtrics imports. Variable mapping, label preservation, weighting.
Cross-tabs with significance
Z-tests with letter notation, configurable confidence levels, weighted bases.
Driver analysis
Multiple linear regression with shapley-style importance. Outputs publication-ready charts.
Segmentation
K-means clustering with elbow / silhouette diagnostics, profiled segments.
TURF
Reach / frequency portfolio optimisation across SKUs / features / message variants.
Open-end auto-coding
LLM-assisted code-frame discovery, manual review, sentiment, theme tagging.
Focus-group transcription
Speaker diarisation, time-coded transcript, theme extraction.
Embeddable dashboards
Embed cross-tab + chart widgets in a client portal — under your domain, your branding.
The problems that don't show up in the demo.
These are the ones that take a custom build from "works in a screenshot" to "works in production for three years." We've already learned them once.
- 01 Statistical correctness — getting the Z-test to actually match what an SPSS user would expect, edge cases and all.
- 02 Performance — cross-tabbing a 50K-row dataset with 200 banner variables in the time it takes to make tea.
- 03 Open-end coding that's auditable — the LLM suggested this code, the analyst accepted / rejected, here's the diff.
- 04 Multi-language survey responses with mixed scripts in the same column.
- 05 Weighting + post-stratification that respects the survey design instead of pretending it's a simple random sample.
How we'd put it together.
ASP.NET Core for orchestration, Python workers (numpy / pandas / scikit-learn) for the heavy stats, Postgres for survey storage, ClickHouse for fast cross-tab queries on large datasets.
We'll tell you when not to build.
Custom isn't always the right call. We've shipped Ragenaizer so we can say that honestly.
- →The research is the moat — your competitor can't have the same one.
- →You have compliance / sovereignty / data-residency requirements no SaaS will satisfy.
- →You need to integrate at a level deeper than off-the-shelf vendors expose.
- →Per-seat pricing across thousands of users makes the build cheaper inside 24 months.
- →The workflow is generic enough that a configurable platform will do.
- →You want it in weeks, not quarters.
- →You'd rather buy than own — let someone else maintain the research forever.
- →Your engineering capacity should go to the parts of your product that no SaaS covers.
Custom research? Tell us what you need.
One conversation. We tell you whether it's a custom build, a Ragenaizer rollout, or something we shouldn't take on.