The view a client keeps after an engagement. The practices are on the home page.
An operator-built, AI-native inventory and sales intelligence platform. Live in production today, onboarding new brands in days, not quarters.
A typical brand's operating picture is split across an ERP, an e-commerce platform, wholesale portals, 3PL reports, and a layer of spreadsheets holding it all together. Nobody holds a source of truth, so the questions that drive margin get answered late, or not at all.
The platform ingests whatever a brand can export, with no system replacement and no IT project, normalizes it into one canonical model, computes the analytics deterministically, and puts an AI layer on top that explains, but never invents.
Excel, CSV, even PDFs, from ERPs, Shopify, wholesale portals, and 3PLs. Files are recognized by content, not by filename.
Each brand's raw vocabulary maps to a shared schema via a per-brand config: configuration, not custom code.
Aging, tiers, sell-through, realizable value, size curves: computed by verified logic, locked by automated checks.
Role-appropriate dashboards, exports, and an AI analyst that answers questions against the computed numbers.
This is not a roadmap. The platform runs live, from full-platform flagship deployments to a single focused module, for brands across contemporary fashion, sportswear, and outerwear. Same core, configured per brand, with data isolation between brands as a hard rule.
Global inventory visibility, aging, order book, and realizable value across warehouses and regions.
Sales, returns, customer health, sizing, seasonal planning, performance, and the AI analyst.
Retail-partner sell-through by account and door. The platform scales down as cleanly as it scales up.
And for anyone who asks: a fully working demo brand running real production data, deterministically anonymized and leak-verified. Not screenshots. The live product.
Each module is independently switchable per brand: start with what your data supports, light up more as sources come online. And the library itself compounds. New modules are scoped with a real brand that needs them, then productized for every brand after.
Unified stock state, aging, and tier classification across every location.
Open orders, WIP, in-transit, and pipeline coverage against stock.
Shipped sales, velocity, sell-through, and pacing by channel.
Return rates, RMA history, and problem-product detection.
Account base, top customers, and early warning on slowing accounts.
Category-by-channel size curves and season-over-season trend.
What aged stock is actually worth, graded by size-run integrity.
Buy-decision worksheets grounded in real selling history, with exportable planned orders.
Full-price vs off-price sell-through, discount depth, and movement.
Retail-partner sell-through by account, door, and style.
Scoped with the brand that needs it. When it lands and your data supports it, it simply appears in your workspace.
Plus the cross-cutting surfaces every brand gets: composed views for location, tier, velocity, and aged detail, a flexible export builder, and self-serve data upload.
The platform is built AI-natively, which is why a lean team ships weekly what a traditional vendor ships quarterly. What makes that safe is the verification architecture around it.
Every brand has a canary: a locked set of expected values recomputed on every change. If a number moves that shouldn't, the change doesn't ship.
Separate verification programs recompute the analytics from raw data by a second path and must agree byte-for-byte with production.
Per-brand data stores, NDA-grade confidentiality, role-based access, and a demo environment whose anonymization is machine-verified for leaks on every refresh.
The platform usually arrives inside the work: the inventory read, the AI work inside operating consulting, the wholesale plan. It stays after, refreshed from the brand's own exports.
We take the brand's existing exports and stand up a configured environment. Analysis first, so the first thing the team sees is their own data telling them something new.
Brands run a lightweight base subscription and add modules as needs surface. No monolithic license, no seat-count games. The platform earns each expansion.
Early brands shape the roadmap and keep preferential terms as the platform matures. Advisory work is available as a separate, clearly scoped stream.
A fully working demo environment, running real production data that has been deterministically anonymized, is available on request. Fifteen minutes in the live product says more than any page.