Half the European powersport channel
is invisible to its own brands.
Dropship arrangements expose every transaction to the manufacturer by default. Stock-holding arrangements expose nothing. Manufacturers plan production on six-month-lagged distributor feedback for half their distribution. Resellers operate without context. End customers get inconsistent service. The whole channel settles for less because nobody is positioned to see across it.
Only one position can see the whole graph.
Every player in the powersport supply chain has partial visibility. The only structural position that can see across all of them is a shared vertical platform that they voluntarily plug into. PowersportOS is that position.
Brand HQ
Catalog rights holder
sees only sell-through to distributors
Distributor
Channel partner
sees only sell-through to resellers
PowersportOS
Operating layer
sees across the whole graph
Retailer
Reseller storefront
sees own store only
End customer
Buyer
sees own search
Shopify Analytics
per-store · cookie-gated in EU · policy-bound against cross-merchant aggregation
Google Analytics
per-property · half of EU traffic missing because of consent banners
ACES · PIES · TecDoc
distribute catalog content · do not capture consumption
ERP and WMS systems
stop at the dealer's own four walls
PowersportOS
catalog · fitment · stock · searches · orders · all converging through one operating layer the resellers themselves run
Chain, but not in chains.
The intelligence and coordination of a fully-owned retail chain. Distributed across independent operators who keep their autonomy. Federated commerce in the Best Western / Visa / MLS pattern, sized for the powersport vertical.
Best Western
1946
Independent hotels share booking, brand, marketing reach. Each property independently owned and operated.
hospitality
Visa & Mastercard
1958 · 1966
Independent banks share payment-network infrastructure. No single bank needed to build a global card network from scratch.
payments
Real-estate MLS
1908
Independent brokerages share property listings via a common data system. No agent needs to be a giant chain to see the full market.
real estate
PowersportOS
2026
Independent powersport retailers and the manufacturers they carry share an operating layer that gives them chain-scale visibility without chain-scale overhead.
powersport · EU
What every one of these patterns delivers: chain-scale advantages without chain-scale overhead. No HQ to run, no central inventory or staff to manage, no leases on stores you don't operate, no cultural homogenization across markets, no risk concentration in a single corporate entity. Each participant remains entrepreneurial and locally adapted; the network handles the layers where scale-dependent leverage lives.
Not a deck. A platform with live customers, shipping releases, integrated where powersport commerce already happens.
PowersportOS is in early access today, with the operating layer of the platform live and in active development. The capabilities below are what already exists; the data-network layer described in the next section is the milestone on top.
catalog
Central catalog & brand subscriptions
Multi-tenant catalog with brand subscriptions that auto-activate parts for resellers. Tenant-owned parts and vehicles on top. PIM-grade per-part data: dimensions, EAN, multi-OEM cross-references, category tree, multi-image galleries, sanitised-HTML descriptions, fitment position and notes, manufacturer-side lifecycle status.
ymm-fitment
Submodel-precise YMM & fitment engine
Year / make / model / submodel / generation precision. Wildcard submodels. CSV imports with range expansion. The fitment engine is the differentiator vs generic e-commerce: powersport buyers need precision that mainstream platforms don't deliver.
store-network
Dealer locator & reseller stock network
Store locator with multi-location stock for retail chains. Reseller stock network with manufacturer-side "find this part at a nearby reseller" widget, opt-in stock sharing with k-anonymity at the dealer level, Haversine-sorted nearest-with-stock query.
shopify-push
Phase D outbound Shopify push
One-click push of catalog data into the reseller's Shopify store: products, variants, images, metafields, SEO, dimensions, fitments. Per-tenant push profile, bulk push with progress, stock-feed loop guard. Customer-managed Dev Dashboard credentials work on every Shopify plan tier.
channel-communications
Channel Communications
Multi-kind message bus. Publish once, route to every reseller subscribed to brands you represent: blog posts auto-published to their Shopify blog, product-release digests, MSRP changes, safety recalls force-delivered with a 48-hour acknowledgement SLA. Adapter pattern from day one; Shopify Admin in v1, WordPress / intranet CMS / webhook plug in via the same interface.
multi-store
Multi-Store parent/child inheritance
Chain affiliates running multiple storefronts (domestic + B2B + regional country sites) maintain one canonical catalog with child storefronts pulling subsets, each with its own pricing and stock.
platform
Operational foundation
Multi-tenant architecture. Tenant-level RBAC with four roles. 2FA self-service on both admin and customer-portal. Append-only audit log. EU-resident infrastructure (Hetzner Helsinki). Encrypted-at-rest credentials. GDPR-compliant data flows with cookieless analytics.
data-network
Analytics & Data Network
Phase 0 shipped May 2026 (self-hosted Umami analytics for the marketing site, surfaced inside the admin). Per-tenant analytics in the customer portal is next. Network aggregates after that. See section 05 below.
Every release is documented in the public changelog. The platform ships every few weeks. Founding partners onboarded today get the platform that exists now; the roadmap above lands on top as it ships.
The four data points an S&OP team would build a dashboard around, if they could.
Sell-through velocity per SKU per region. Inventory aging across the network. Search-to-sale ratio per SKU. Regional demand patterns. The data nobody has today because nobody is structurally positioned to collect it. Below, the four data points named with a CFO-language definition and what business decision each one actually drives.
01
Sell-through velocity
per SKU · per region · in days
Time from dealer-receipt to end-customer sale. The core inventory-turn KPI. Production-planning, slow-mover identification, successor-SKU timing all use this. Manufacturers see it dropship-side automatically; they have never had it stock-holding-side.
example value · illustrative
02
Inventory aging
across the network · by SKU
Days-on-shelf aggregated across resellers. Surfaces overstock before return requests arrive. Saves reverse-logistics cost and gives the manufacturer time to coordinate clearance or reallocation instead of accepting the return hit later.
example value · illustrative
03
Search-to-sale ratio
per SKU · network-wide
How often a part is searched vs how often it actually sells. Distinguishes hot demand from idle curiosity. Drives advertising spend, content investment, and discontinuation decisions. The killer use case for manufacturer marketing teams.
example value · illustrative
04
Regional demand patterns
by ISO country · trend over time
Geographic variance in demand. Critical for finished-goods allocation, regional warehousing, country-level marketing. Today this comes back filtered through distributor-channel feedback, lagged by 3-6 months, often anecdotal. Live network data replaces the anecdote.
example value · illustrative
non-negotiable guard-rails
- Per-tenant opt-in. Default private. Every tenant decides what they share.
- K-anonymity techniques. Aggregates display only when enough tenants contribute that no individual tenant's data can be reverse-engineered from the total.
- No per-tenant attribution in cross-tenant views. Manufacturers see totals; reseller A can never infer reseller B's stock or sales from any aggregate they have access to.
- End-customer privacy intact. We aggregate business data (catalog membership, search interest, stock totals, SKUs sold), never end-customer-attributable activity.
The more we are,
the more we know.
Marginal cost per additional tenant on the data side is essentially zero. Marginal value per additional tenant compounds. The early tenants who opt in are the ones who shape what the network looks like when it matures.
01
More resellers join PowersportOS
02
Catalog views, stock signal, searches accumulate
03
Datasets become richer and more representative
04
Manufacturers push their resellers onto the platform
This is the same loop Stripe gets with payment data, Toast with restaurant data, Shopify with merchant data. The moat grows with tenant count while the delivery cost barely moves. With a handful of tenants the dataset is not dense yet; the position is built, and compounding starts the day you opt in.
Early access is open.
We are talking to retailers, manufacturers, distributors, and capital partners who see what we see. Founding partners shape the roadmap, get pricing locked in, and (where relevant) participate in shaping the data-network guard-rails. If that is you, let us talk.
retailer
For retailers & reseller chains
YMM, dealer map, multi-location stock, Shopify integration, cookieless analytics, optional opt-in to the data network. The day-to-day operating layer.
Read the retailer pitchmanufacturer
For manufacturers
Brand-controlled catalog content distributed to every reseller, channel-wide communication, network demand signal, sell-through visibility. The intelligence layer.
Read the manufacturer pitchdistributor
For distributors
Chain, but not in chains. The HQ view of a fully-owned retail chain, distributed across the independent resellers you already supply.
Read the distributor pitch