Commerce platforms at catalog scale.

B2B procurement, DTC storefronts, and the AI now inside catalog, pricing, and promotion workflows, for retailers and distributors with their own tech teams. 10M+ products from 10 manufacturers on one platform. 400% faster promotional campaigns at three major US retail chains.

10M+Products, Innovative IDM
200%Speed improvement, IIDM
400%Faster campaign time to market, Omnioffer
500+Store locations, SODA

What we build for e-commerce

Enspirit designs, builds, and adds AI to commerce software for companies that run their own product teams. Enterprise and mid-market commerce spans B2B procurement, consumer storefronts, and connected-product experiences. Each has different data models, different buyer flows, and different integration requirements. We've built across all of them, and the most interesting work is increasingly at the boundaries between them.

B2B platforms

Procurement portals, RFQ systems, bulk ordering, and account-based pricing for enterprise buyers.

DTC experiences

Consumer-facing storefronts with personalization, recommendation engines, and conversion optimization.

Catalog management

Product information management for 10M+ SKU environments. Categories, attributes, search, and filtering at scale.

ERP and OMS integration

Direct integrations with financial systems, warehouse management, and order management platforms. SAP, NetSuite, and custom ERPs.

Pricing and promotions

Account-based pricing, tiered contracts, promotional logic, and rule engines for catalogs where no two buyers see the same price.

Commerce is rarely just a storefront

Most enterprise commerce work isn't a storefront sitting in front of a warehouse. It's a procurement portal with account-based pricing, or a connected product where the purchase is the start of an ongoing relationship, or a marketplace where the catalog itself is generated rather than photographed. The commerce surface is the easy part. The hard part is what's behind it.

We built a print-on-demand marketplace where products are manufactured only after an order is placed. There's no static photo library. Product visuals are generated dynamically at the point of listing using Cloudinary's transformation pipeline, so sellers can add products without a separate photography workflow. The platform manages seller catalogs, triggers production at order time, and handles fulfillment routing without the seller coordinating any of it manually. That's a B2B2C model where both sides of the platform have to be designed together rather than bolted up later.

Safedecisions is a different shape. A customer buys a physical safe through the web store, then manages it through a companion app. Remote unlock, access control, service requests. The commerce layer and the post-purchase product experience are one integrated system, not two products that happen to share a user account. Treating the platform as a product lifecycle system from the start, rather than a transaction engine with an app bolted on later, is what makes the experience feel like one product instead of two.

Where AI goes inside commerce operations

Dynamic product imagery is already a practical requirement for catalogs that grow faster than photography budgets. We're building on this with Cloudinary integrations now. The next step is AI-generated contextual product imagery that places a product in a relevant scene without a separate shoot. For print-on-demand and configurable product businesses, this changes what's possible at catalog scale.

The bigger gap in most commerce platforms is between purchase data and the marketing stack. The data exists: what a customer bought, when they're likely to upgrade, what complementary products fit their history. It just doesn't automatically flow to the tools that could act on it. Clean integrations between the commerce layer, the CRM, and marketing automation are more valuable than they look. Most teams underinvest in them until they're running manual segmentation exports every week.

AI-powered support is also shifting from optional to operational. Not FAQ chatbots. Support systems that understand order state, product lifecycle, and account history well enough to resolve most requests without a human. For connected products like Safedecisions, where the device is tied to the account, the support system can pull context the customer would otherwise have to explain from scratch every time they call. Each of these is a workflow you already run. We add AI to it inside your existing platform and measure the change against your own baseline.

Case study

Innovative IDM: 10M products from 10 manufacturers

Unified B2B platform with custom ERP, customer portal, and PWA. 100 product categories, dynamic pricing, order management.

Read the case study

Common questions

Product data, promotions, and store requests stop depending on manual handoffs. One workflow at a time, each measured. Promotional campaign automation at one client reached 400% faster time to market; a signage platform for 500+ stores ended email-based requests.
The split is in the data model and the buyer flow, not just the UI. B2B platforms carry account-based pricing, tiered contract terms, RFQ workflows, multi-location shipping, purchase order numbers, and approval chains. DTC platforms optimize around conversion, personalization, and repeat purchase. They share catalog and checkout infrastructure but diverge quickly in how the buyer authenticates, how price is determined, and how an order routes through fulfillment. We've built both, often for the same client, and the starting question is always: what does your buyer's day actually look like.
Catalog at that scale is a data architecture problem. Attributes, categories, relationships, and search indices have to be structured so they stay manageable as the catalog grows. We've built catalog systems handling 10M+ SKUs across multiple manufacturer data feeds for IIDM. The work included a custom PIM layer, attribute normalization across supplier schemas, and real-time search and filtering that stayed fast at scale. We build the data model first, not the storefront.
We've integrated with SAP, Oracle NetSuite, custom ERPs, and warehouse management systems. Integration complexity depends on what the system exposes and whether it's a legacy system with batch-only APIs or a modern system with real-time event hooks. We map integration requirements in the scoping phase and build to them directly. No middleware abstraction layers that add latency and maintenance cost.
Migration starts with an honest assessment of what's worth keeping. The data model (catalog, orders, accounts) almost always migrates. Business logic encoded in years of Magento module layers often doesn't. We run a structured audit of the existing platform before committing to an approach: what carries over, what gets rebuilt, what gets simplified. The answer depends on what your platform actually does, not what the ticket says it does.
Scope depends on B2B vs DTC, catalog volume, how many ERP and WMS integrations are involved, and whether you're building new or migrating an existing platform. A focused B2B procurement portal for a single product category runs 4 to 6 months. A full enterprise platform with ERP integration, multi-warehouse inventory, B2B and DTC channels, and custom pricing takes longer. We scope every engagement before we quote it. The right starting point is a conversation about your specific constraints.
E-Commerce

Running commerce at scale and want AI inside the catalog, not beside it?

Four weeks of Enspirit Delivery OS on one workstream. Fixed fee, cycle-time guarantee, baseline taken from your own Jira and Git history.

Start with four weeks

Related services: AI-native product design, Product engineering, AI Integration, AI Integration for retail.