WellMatch
A health aware product recommendation engine that bridges ecommerce and clinical context. The right wellness products, for the right person, safely.
Wellness shoppers purchase products that
conflict with their health, not from carelessness
Product pages do not surface health compatibility meaningfully. A customer managing a chronic condition, taking prescribed medications, or following clinical dietary guidance has no reliable way to filter a product catalogue against their health profile before purchasing. This creates risk (adverse reactions, wasted spend, eroded trust) and missed opportunity (higher confidence purchases, increased basket size, better retention).
Four audiences. One engine.
Intake, match, explain.
Built on tools I have already shipped with.
Every product is defined by its actual ingredient list, and every ingredient carries structured health metadata: conditions to avoid, conditions to flag, conditions it helps, allergen tags, vegan status. The engine scores each ingredient against the user's profile and rolls the result up to a product-level tier with plain language rationale.
Why rules over an LLM: a deliberate choice, not a shortcut. When the output is a health compatibility signal, every recommendation needs to trace back to an explicit, reviewable rule, not a language model's best guess. A rules engine can't hallucinate a contraindication that isn't in the data. It also runs free, at any volume, with no per-request latency. The tradeoff is real: rules don't scale to a thousand-SKU catalogue as gracefully as an LLM could, but for a safety-adjacent recommendation surface, traceability won over flexibility.