00/Architecture
Instead of a feature list, DataGlass is organised in two tiers. Top: what sellers see — true profit optimisation, automated decision making, transparency. Below: the engine that makes those claims real — operations research, financial modelling, machine learning. Each pillar cites the blog posts and research that prove it.
↳ Pillars
01 · Pillar
Margin is the goal, not GMV.
DataGlass models true profit per SKU — net of platform commission, transaction fees, seller-funded vouchers, ad attribution, and fulfillment — and surfaces the moves that compound it. Every recommendation respects the SKU's own break-even bar, not a single account-wide ROAS target.
↳ Cited in
↳ Key terms
02 · Pillar
One-click deploys for AI-powered actions.
DataGlass turns the ranked profit-maximising action queue into single-click deploys against the platform native flow. Each action — bid adjustment, voucher tier, listing optimisation — runs through a profit-and-risk gate before deployment, with post-deployment tracking against expected lift.
↳ Cited in
↳ Deep dive
03 · Pillar
Every recommendation comes with the math.
No black-box deploy. The model's reasoning, the inputs it weighed, the downside risk it priced in, and the post-deployment realised vs expected delta are all surfaced in-line. The seller stays the auditor and the approver — DataGlass is the operator that does the work and shows it.
↳ Cited in
↳ Foundations
— Without selling more or spending more, where does profit come from?
Operations research answers the question rigorously: budget allocation across heterogeneous campaigns, dynamic pricing under demand uncertainty, inventory policy under stochastic lead time, voucher-tier discipline against category-margin constraints. The math that says "cut here, reallocate there, wait for that" — proven before it deploys, with Bayesian and causal methods used inline as the underlying inference machinery.
↳ Cited in
↳ Research
↳ Deep dive
— The finance + uncertainty modelling that keeps the optimum honest.
Robust optimisation puts a hard floor under every decision. Worst-case scenarios are modelled and capped before any action ships — CVaR-95 below the margin floor is not deployed, regardless of how high the expected value sits. Margin reconstruction down to the order line, returns-reserve provisioning, working-capital modelling under campaign-driven demand spikes, and probabilistic price-elasticity estimation per SKU make the cost stack precise enough that the risk math has something real to bound.
↳ Cited in
↳ Research
↳ Key terms
↳ Browse functional solutions
Join the sellers using DataGlass to turn shop data into the next profit-maximizing action.
