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Enterprise / Enterprise API Integration Architecture
Enterprise Capability

Enterprise API Integration Architecture

Typed API gateways connecting your ERP, CRM, PIM, and payments

CTO / VP Engineering / VP Digital at 200-5000 employee company with 3+ backend systems needing unified frontend integration
$75,000 - $300,000
137,000+
listings managed
NAS directory platform with multi-provider data synchronization
91,000+
dynamic pages indexed
Content platform aggregating 3+ data sources per page
30
languages deployed
Korean manufacturer hub with PIM and translation integration
sub-200ms
real-time bid latency
Auction platform coordinating pricing, auth, and payment systems
Lighthouse 95+
performance score
Across all enterprise integration projects
Architecture

Unified API gateway built on Next.js API routes with tRPC or GraphQL Yoga, providing typed contracts generated from upstream ERP/CRM/PIM/payments specs via Zod and codegen. Event-driven sync through Inngest handles real-time data flow with retry logic and dead letter queues, while Supabase manages integration state and Redis provides edge-level caching with TTL-based invalidation. Full observability via correlation IDs, structured logging, and Sentry integration traces every request across system boundaries.

Où les projets enterprise échouent

Here's the thing about most enterprise stacks we inherit -- they're held together with undocumented point-to-point integrations between the ERP, CRM, PIM, and frontend Nobody wrote down how they connect. Nobody drew the map. So when a developer in Atlanta updates a field name in SAP, a Shopify storefront in production starts throwing 500 errors at 2am, and your team spends four hours figuring out which of the six systems actually broke first. That's the cascading failure problem. And it's not rare -- it's basically the default state of any stack that's grown organically over three or four years without a dedicated integration layer. The real kicker is there's no debugging path. You're just... guessing. Checking logs in five different systems, correlating timestamps manually, hoping someone remembers why that webhook exists. We've seen revenue-impacting outages drag on for six-plus hours simply because nobody could answer "where does this data actually come from?"
Batch sync jobs running on cron schedules are quietly killing conversions Your product pricing, inventory levels, and customer data can be hours -- sometimes days -- out of date on customer-facing pages. And honestly, that gap costs real money. A customer in Chicago sees a price that expired yesterday, buys it, and now you've got a support ticket, a margin problem, and a refund to process. We've seen stale inventory data alone generate hundreds of tickets a month on mid-size catalogs. Cron-based sync felt reasonable in 2015. In practice, it just doesn't hold up anymore.
No observability across integration boundaries means when something looks wrong on the frontend, nobody actually knows which system is lying Is the price wrong because the ERP didn't sync? Because the PIM overwrote it? Because the caching layer is serving a stale response? There's no source of truth anyone can point to with confidence. So debugging becomes a group archaeology project -- pulling logs from three different teams, none of whom can see each other's systems. That's not a technology problem. It's a structural one.
Frontend teams shouldn't be writing integration code But without a proper gateway layer, that's exactly what happens -- your React developers end up reverse-engineering Salesforce responses and hand-rolling data transformations just to ship a product page. Feature velocity drops 40-60% fast. We've watched teams in that situation where the frontend lead was spending more time reading ERP documentation than building UI. That's expensive, demoralizing, and completely avoidable.

Ce que nous livrons

Typed API Gateway

We build a unified GraphQL and REST gateway with TypeScript types auto-generated directly from upstream system specs. So your frontend team consumes one consistent, documented interface -- doesn't matter if the backend is SAP, Salesforce, Akeneo, or some legacy REST API from 2011. The complexity gets absorbed at the gateway layer. Your developers just see clean, typed endpoints that behave predictably. No more digging through Confluence pages trying to figure out what field SAP actually returns for a product's base price. It's all handled before it ever reaches them.

Event-Driven Sync Engine

Real-time data propagation runs through Inngest workflows with automatic retry, dead letter handling, and guaranteed delivery. Batch cron jobs are gone. So is the data staleness they cause. When a price changes in your ERP, that update moves through the pipeline immediately -- not at 3am when the next sync window opens. And honestly, once you've seen how clean this is compared to debugging a failed cron job at midnight, you won't miss the old way at all.

Runtime Schema Validation

Zod schemas sit at every integration boundary, and they catch upstream changes before anything broken reaches the frontend. So when a vendor updates their API and renames a field -- which happens constantly -- you get a logged, alerted incident instead of silent data corruption spreading through your catalog. It's not glamorous. But it's the difference between catching a schema drift at 9am and discovering it cost you conversions at 9pm. We've seen that second scenario play out too many times to leave this part to chance.

Edge Caching with Smart Invalidation

Redis and Vercel ISR caching with event-driven invalidation gets you sub-second page loads without sacrificing freshness. TTL strategies are tuned per data type -- pricing refreshes in seconds, product descriptions in minutes. You're not picking between fast and accurate. You get both, because invalidation fires when the data actually changes rather than on some arbitrary timer. That distinction matters more than most teams realize until they've burned a week debugging stale cache serving incorrect prices to real customers.

Full-Stack Observability

Correlation IDs trace every request from the browser through the gateway all the way to each upstream system. Dashboards show sync lag, error rates, and data freshness per integration -- so when something's off, you know exactly where the chain broke. Automated alerting and runbooks mean your on-call engineer isn't starting from scratch at midnight. There's no "check everything and hope" anymore. You open the dashboard, see which integration is misbehaving, and follow the runbook. That's it.

Graceful Degradation and Circuit Breaking

Per-integration circuit breakers serve cached data with staleness indicators when upstream systems go down. SAP's in a maintenance window? Your site keeps serving product pages. A third-party logistics API times out? Checkout still functions. We define the degradation strategy up front so "upstream outage" stops meaning "site outage." And look -- every system goes down sometimes. The question is just whether you've decided in advance what happens when it does, or whether you're making that call under pressure at 11pm.

Questions fréquentes

Comment gérez-vous les changements de schéma dans les systèmes en amont comme SAP ou Salesforce ?

Every integration boundary runs Zod validation schemas that catch structural changes at runtime before anything broken propagates downstream. Plus, we generate TypeScript types directly from upstream API specs -- so schema drift surfaces as a build-time error, not a production incident you find out about from a customer. The gateway's transformation functions do the heavy lifting of isolating upstream changes from what your frontend actually consumes. Your React components never see a raw SAP response. Ever.

GraphQL ou REST — comment décidez-vous lequel utiliser pour chaque intégration ?

GraphQL makes sense for relational, read-heavy queries -- product catalogs, customer profiles, content aggregation -- where frontend teams need flexible data fetching without over-fetching entire resources. REST handles transactional writes that need idempotency: payment captures, order submissions, webhook receivers. Most enterprise projects we build honestly use both. But they're unified behind a single typed gateway, so your frontend team doesn't have to care which protocol a given operation uses under the hood.

Que se passe-t-il quand un système en amont tombe en panne ?

Circuit breakers and graceful degradation get configured per integration, not as a blanket fallback. Cached data keeps serving reads with staleness indicators so customers aren't staring at broken pages. Event queues buffer writes with automatic retry and dead letter handling so nothing gets lost during an outage. And observability dashboards surface exactly what's down and what's affected -- no more "something's wrong, check everything." We nail down the degradation strategies during architecture design, not during an incident at 2am.

Comment assurez-vous la cohérence des données entre les systèmes ERP, CRM et PIM ?

Here's the thing most teams skip: clear system-of-record ownership for every data entity. Pricing lives in the ERP. Product descriptions live in the PIM. Customer records live in the CRM. The gateway enforces those ownership rules programmatically. Event-driven sync keeps downstream mirrors fresh within seconds. And conflict resolution logic gets defined during the architecture phase -- not left to whoever's on call when two systems disagree about a price.

Cette couche d'intégration peut-elle supporter notre frontend existant ou faut-il une reconstruction complète ?

The gateway exposes standard GraphQL and REST endpoints, so any frontend can consume it -- Next.js, React, Vue, even a legacy server-rendered app that's been running since 2014. We typically wire the gateway to your existing frontend first while building new Next.js pages in parallel. Migration is incremental. You don't need a full rebuild to start getting clean, typed API access to your backend systems. That's kind of the whole point.

À quoi ressemble l'observabilité dans la couche d'intégration ?

Every request gets a correlation ID that traces through the gateway into each upstream system call. We log response times, payload sizes, and error rates per integration -- not just aggregate metrics, but per-system breakdowns. Custom dashboards show real data freshness: how stale is your product pricing right now, how long since the last Salesforce sync. Alerts trigger on sync lag thresholds, error rate spikes, and upstream latency degradation. Each alert has a runbook attached. So your team knows what to do, not just that something's wrong.

Combien de temps prend un projet typique d'architecture d'intégration d'entreprise ?

Eleven to eighteen weeks from audit to production handoff -- the range depends on how many systems we're connecting and how gnarly the transformation logic gets. But your frontend team isn't waiting until week eighteen to see anything. The first typed APIs are available within six weeks. We phase delivery deliberately so your developers start building against real data early. No big-bang launch. No six-month blackout where nothing ships.

Voyez cette capacité en action

Headless CMS Development

Our CMS implementations connect through the same typed gateway layer, ensuring content from Sanity or Contentful integrates cleanly with ERP and PIM data.

Next.js Enterprise Development

The frontend layer consuming the API gateway, built with React Server Components and edge rendering for sub-second page loads from integrated data.

E-Commerce Platform Architecture

Commerce implementations where the integration layer coordinates product data, pricing, inventory, and payment processing across multiple backend systems.

Performance Optimization

Edge caching, smart invalidation, and query optimization strategies that keep integrated pages fast despite pulling from five or more data sources.

Multi-Language Platform Development

The 30-language Korean manufacturer hub where PIM integration with translation management demonstrated gateway architecture at scale across locales.
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