Equipment platforms where a click becomes a factory spec.

Configurators, design-to-factory pipelines, and the AI now sitting inside them, for sports brands with their own product teams. Nike's Equipment Builder has run in production for more than four years, used by NFL and MLB athletes.

Nike4+ year partnership
NFL + MLBAthletes using the platform
MillimeterSpec precision
Web, mobile, PWAOne configuration engine

What we build for sports tech

Enspirit designs, builds, and adds AI to sports equipment software for companies that run their own product teams. Sports tech has a split personality: consumer-polished up front, factory-accurate at the spec layer. An athlete configuring custom gear needs both. The pipeline between them has to actually work. We've built that pipeline in production, from Nike's NFL gloves to MLB chest protectors, where a millimeter off means the gear doesn't fit.

Equipment configurators

Web and mobile tools where athletes and designers build custom gear in 3D. Every click resolves into production-ready geometry, not a render meant to impress.

Design-to-factory pipelines

The part no demo shows: the connected path from configurator output to CNC specs, approvals, and the factory floor. What gets designed is what ships.

Athlete and coach platforms

Performance tracking, biometrics, roster and program management. Built so data moves between coaches, trainers, and the front office without CSV export theater.

Fan-facing products

Companion apps, fantasy interfaces, matchday tooling. Engineered to hold up at kickoff and peak traffic, not just during QA.

Sports tech is a production engineering problem

The hard part of sports tech is the gap between the prototype and the production system. A configurator demo shows an athlete selecting materials and seeing a 3D render update in real time. What the demo doesn't cover: whether the spec file that gets generated is actually readable by the factory's CNC system, whether the approval workflow handles the case where a team wants a colorway that violates league rules, or whether the system holds up when every equipment manager in a league is placing orders the week before training camp.

Fan-facing products live or die at peak load. They're often built for average traffic and then deployed to a situation where it spikes in the minutes before kickoff. A stadium app that performs well in QA and stalls during the opening drive isn't a minor bug. It's the product's most visible moment, and it has to hold at exactly the time it matters most.

Athlete and performance platforms have their own pattern. They generate data but don't always complete the loop to a decision. Coaches have dashboards full of metrics with no clear path to action. The engineering is solid but the product doesn't map to how a coaching staff actually uses information during a game week. We've rebuilt platforms where the data was excellent and the interface made it unusable.

Where AI fits in a design-to-factory pipeline

Constraint checking, spec validation, and order routing are the steps that burn hours between an athlete's order and the factory. They are also where AI inside the existing pipeline pays back first, measured in cycle time rather than demos.

For fan and athlete-facing products, we engineer for peak load from the first sprint, not as a late-stage optimization. CDN-first asset strategy, graceful degradation when upstream services are slow, and load testing against realistic traffic profiles before launch. The sports calendar is fixed. There's no negotiating a soft launch around the Super Bowl.

We've worked with Nike for over four years on the Equipment Builder platform, shipped fan-facing products for NFL and MLB contexts, and built athlete performance tools used by professional coaching staffs. That depth means we know which constraints are real and which are convention, and we can move faster on sports tech problems because we've already solved the ones that tend to surprise teams building in this space for the first time.

Case study

Nike Equipment Builder: custom gear for pro athletes

NFL and MLB athletes configure custom gloves, chest protectors, and fielding gear through a web, mobile, and PWA platform that writes directly to factory specs. 4+ year partnership.

Read the case study

Common questions

Yes, and making that guarantee is the point. In our Nike work, configurator output fed directly into factory spec files. When a player changed a glove colorway, that change flowed through approval and into CNC parameters without manual transcription. The key is building the data model to carry all production-relevant dimensions, not just a visual representation. We've shipped this in production for NFL and MLB gear, where tolerance errors have real consequences.
Yes, though the integration path depends on which system you're running and how much of its API is documented. SAP, Arena, and proprietary manufacturing ERPs all have different access models. We scope PLM and MES integration as a separate workstream and map what data needs to flow in each direction before writing a line of code. Where the vendor API doesn't reach, we design the handoff protocol (file export format, data schema, approval trigger) so there's no manual gap in the pipeline.
A configurator covering a single product category with 3D visualization, color and material selection, and export to a spec format typically takes 4 to 6 months to reach production. Add a design-to-factory pipeline connecting to a live manufacturing system and you're looking at 6 to 9 months depending on integration complexity. We scope every engagement before we quote it, and the right starting point is understanding which product lines you're starting with and what the factory needs to receive.
The 3D side is engineering work, not a black box. We build the configurator layer (product model, constraint logic, real-time pricing, and spec output) and integrate the rendering layer (Three.js, Babylon.js, or a vendor SDK if you have one) so they behave as one system. For the Nike Equipment Builder, we handled both sides. If you already have a rendering partner or SDK license, we work with it.
Fan-facing apps at kickoff or peak traffic are an architecture and load testing problem, not a faith problem. We design for peak load from the start: async data loading, CDN-first asset strategy, graceful degradation when upstream services are slow. We've shipped fan-facing products for NFL and MLB contexts. The spec isn't 'handles average load.' It's 'holds when the game starts.'
Sports tech

Running an equipment or athlete platform and want the pipeline to hold?

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Related services: Product engineering, AI product development, AI Integration.