Four disciplines. One way of working.
We do not consider AI, quantitative methods, or data infrastructure as separate departments. Each Vanillapha product draws on the same four disciplines in different proportions — and the rigor compounds across the portfolio.
The four, in their own words.
Applied AI
Generative and predictive models embedded inside the product workflow — not a marketing surface.
- Retrieval-augmented analysis
- Domain-tuned reasoning
- Audit trails on every model call
Quantitative methods
Statistical rigor borrowed from systematic finance, applied to ranking, risk, and product decisions.
- Backtesting before deploying
- Calibration on real distributions
- Risk surfaces, not point estimates
Resilient data infrastructure
Storage and pipelines built to survive load, audit, and the next five years of schema drift.
- Event-sourced where it matters
- Backups designed to be restored
- Observability as a first-class concern
Compliance-first engineering
Privacy, retention, and jurisdiction modeled into the system from day one.
- Data residency, by design
- Minimum-necessary access
- Deletion on policy, not on memory
What we cannot measure, we do not ship.
Every product surfaces its own truth metric — the single thing that decides whether the system is working. We define it first, instrument it next, and only then write the feature.
Two rules, applied without exception.
We ship what we run.
If we wouldn't put our own data on it, we don't ship it.
We do not chase frontiers.
We use the smallest model that solves the problem — and the simplest architecture that survives it.