Wellness
FrostFit
How it works
A coaching-grade experience that stays calm under personalization, integrations, and store policies.
What we designed
Teams ship wellness products fast, then get stuck bridging sensors, sane recommendations, and analytics that reviewers understand.
FrostFit is framed as multitenant SaaS: crisp onboarding, opt-in personalization, guarded ML ranking, and dashboards that reconcile mobile + API traffic.
Trust-first personalization
Profiles merge explicit preferences with lightweight implicit signals—every inference path is reversible and explainable enough for QA.
Recommendation guardrails
Offline eval harnesses simulate cold starts, spikes, and store-review edge cases before a model promotion hits production cohorts.
Operator clarity
Tenant admins inspect cohort health, campaign lift, and abuse signals without drowning in noisy event streams.
- 01
Guided onboarding & consent
Users choose goals and share device permissions progressively; jurisdictional prompts live behind feature flags.
- 02
Signal ingestion pipeline
Normalized events hit a schema’d stream, hydrate feature stores idempotently, and fan out async workers for deduped aggregates.
- 03
Ranking + rules hybrid
Learned rankings sit beside deterministic safety rails—cold weather gear logic never overrides medical opt-outs coded in policy tables.
- 04
Iterate with observability
Tracing ties API latency to retrieval depth; playbooks downgrade traffic when GPU queues back up unexpectedly.
Under the hood
API gateway, typed services, Postgres + OLAP-lite exports, embeddings cache, and Canary-friendly deploy hooks.
Stack highlights
Illustrative composite based on recurring studio patterns—not a testimonial tied to any single deployed client.