CartLogic

Turning volatile, unstructured retail data into a shopping and nutrition tool people can actually trust — live in production today.

Client type

B2C consumer app · B2B clinical

Engagement

Systems architecture & build

Status

In production

The challenge

Weekly grocery circulars, BOGO deals, and multi-pack pricing change constantly and don't follow any consistent format. Building a shopping or nutrition app directly on raw parsing — or handing the whole problem to an AI model — means the numbers people rely on for their household budget or their health can quietly be wrong.

For a nutrition or budget product, "probably close enough" isn't good enough. The client needed prices, discounts, and macros that are always exactly right — with none of the usual hand-waving that comes from routing everything through a language model.

The approach

We split the system down the middle, and never let the two halves blur together:

Deterministic core

Exact price and discount math — BOGO logic, multi-pack pricing, macro calculations — runs as ordinary, testable code. No model ever touches a number that ends up on a receipt.

AI-assisted layer

Language understanding is reserved for what it's actually good at — matching ingredient names, writing recipe descriptions, and summarizing a week's deals in plain language.

That boundary let the same underlying system power two very different front ends: a consumer app helping households cut grocery costs, and a white-label reporting tool for health clinics and wellness networks — without either one inheriting the other's risk.

Where it stands

CartLogic runs in production today, serving both the consumer household-budget use case and the institutional clinical use case from one shared core — proof that the deterministic/AI split scales across very different customers without a rebuild.