DataGlass is built in two tiers — the pillars sellers see, and the mathematical foundations that make each one real.
Three promises that drive every recommendation.
Margin is the goal, not GMV.
What you get
DataGlass models true profit per SKU — net of platform commission, transaction fees, seller-funded vouchers, ad attribution, cancellations, returns, VAT, and logistics. Every recommendation respects the SKU's own break-even bar, not a single account-wide ROAS target.
The math behind it
Margin reconstruction at order-line granularity, returns-reserve provisioning, and probabilistic price-elasticity per SKU make the cost stack precise enough to act on.
From decisions to deployment, not dashboards.
What you get
Ranked, profit-maximising Actions. Bid adjustments, voucher tiers, and listing optimisations all ship in one click against the native Shopee, Lazada, and TikTok Shop flow — with post-deployment tracking against expected lift.
The math behind it
Operations research drives the ranking — budget allocation across heterogeneous campaigns, dynamic pricing under demand uncertainty, inventory policy under stochastic lead time, voucher-tier discipline against category-margin constraints.
See the reasoning, not just the recommendation.
What you get
Every recommendation shows why it was chosen, what it expects, and what could go wrong. Named methods (Robust Optimization, Bayesian inference) are surfaced in the UI — not hidden behind a black box. Post-deployment tracking compares actual outcomes against predictions so sellers can audit the engine's reasoning.
The math behind it
Full cost-stack decomposition is visible per SKU. Risk guardrails (margin floors, CVaR-95 bounds, budget caps) are shown alongside every Action so the seller sees the constraint, not just the recommendation. Every model input is traceable.
The science that makes every Action pass muster.
Operations Research finds the optimum. Mathematical Modeling builds the shop's real economics that OR optimizes over. Risk-Aware Optimization bounds every Action before deploy. Bayesian and causal inference run inline across all three.
Without selling more or spending more, where does profit come from?
How DataGlass represents the shop's real economics — not an industry average.
The finance + uncertainty modeling that keeps the optimum honest.
Join the sellers using DataGlass to turn shop data into the next profit-maximizing action.
