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.
Where enterprise projects fail
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?"
Your product pricing, inventory levels, and customer data can be hours -- sometimes days -- out of date on customer-facing pages.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.
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.
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.
What we deliver
The Integration Problem Nobody Wants to Own
Every enterprise runs on a constellation of systems — ERP for operations, CRM for customer data, PIM for product information, payment processors for revenue. The problem isn't the systems themselves. It's the brittle, undocumented, hand-wired integrations between them that break at 2 AM and cost your team weeks of debugging.
We've seen this pattern more times than we'd like: a $200M company running SAP, Salesforce, Akeneo, and Stripe with point-to-point integrations held together by cron jobs and CSV imports. One schema change in the ERP cascades into stale product data on the website, mismatched customer records in the CRM, and failed payment reconciliation. Nobody actually knows where the data lives.
That's the integration architecture problem. And it's what we solve.
Our Architecture: Gateway-First, Event-Driven, Fully Typed
We build API integration layers as first-class infrastructure — not afterthoughts bolted onto the frontend. The architecture follows three core principles.
Unified API Gateway
Every backend system — ERP, CRM, PIM, payments — connects through a single gateway layer. This isn't a pass-through proxy. It's a typed orchestration layer that normalizes data models, handles authentication, manages rate limiting, and provides a single contract for all downstream consumers.
For Next.js frontends, this means API routes and React Server Components fetch from one consistent interface. Your product detail page pulls pricing from SAP, descriptions from Akeneo, reviews from your CRM, and inventory from your WMS — all through a single typed query. No frontend developer needs to understand the quirks of each upstream API.
Typed GraphQL and REST Contracts
We use GraphQL where relational queries matter — product catalogs with variant trees, customer profiles with order histories, content models referencing PIM records. The schema mirrors your business entities, not your database tables. Every field is typed. Every resolver is tested. Every breaking change is caught at build time.
REST stays the right choice for transactional endpoints: payment captures that need idempotency guarantees, webhook receivers that need simple request/response cycles, health checks that monitoring tools expect. We don't force GraphQL where it doesn't belong.
The key is code generation. We generate TypeScript types from GraphQL schemas and OpenAPI specs, creating end-to-end type safety from your ERP's data model through the gateway to the React component rendering it. A field rename in SAP becomes a compile error in your frontend, not a runtime bug in production.
Event-Driven Data Flow
Batch syncs are the enemy of data consistency. When a price changes in your ERP, your website should reflect it in seconds, not hours. We implement event-driven architectures where upstream systems emit change events — product updates, inventory adjustments, customer record modifications — and downstream consumers react in near-real-time.
Simple flows use webhooks. Complex workflows that need retry logic, dead letter handling, and guaranteed delivery use message queues — Redis Streams, AWS SQS, or Inngest for serverless orchestration. The gateway acts as both event consumer and query provider: events come in, processed data goes out.
Technology Stack in Production
Our integration architectures run on proven, observable infrastructure:
Gateway and Orchestration
- Next.js API routes and middleware for edge-level request routing and transformation
- tRPC or GraphQL Yoga for typed server-side APIs with automatic TypeScript inference
- Inngest for durable, event-driven workflow orchestration with built-in retry and observability
- Zod schemas for runtime validation at every system boundary
Data and Caching
- Supabase or PostgreSQL for integration state management — sync cursors, transformation logs, conflict resolution records
- Redis (Upstash) for caching frequently-accessed cross-system data with TTL-based invalidation
- Edge caching via Vercel with ISR for pages that aggregate data from multiple backend systems
Observability
- Structured logging through every integration hop with correlation IDs that trace a request from the frontend through the gateway to the upstream system and back
- Sentry for error tracking with custom contexts showing which integration path failed
- Custom dashboards monitoring sync lag, API response times, error rates per integration, and data freshness guarantees
Observability isn't optional here. When five systems feed one frontend, you need to know exactly where a stale price or missing product image originated. We instrument every boundary.
How This Plays Out at Scale
We've built integration architectures handling real production load:
For a national directory platform managing 137,000+ listings, the integration layer synchronizes data from multiple upstream providers, normalizes it into a consistent schema, and serves it through a typed API that powers both the Next.js frontend and third-party consumers. Sync operations complete in minutes, not hours.
On a content platform with 91,000+ dynamically generated pages, the gateway aggregates data from a headless CMS, custom calculation engines, and user preference systems. Every page gets assembled from three or four data sources at build time, with real-time updates pushed through event-driven invalidation.
For a Korean manufacturer's 30-language hub, the integration architecture connects a PIM system with translation management, regional pricing engines, and locale-specific content — all routed through a gateway that serves the correct data variant based on edge-detected locale.
Our auction platform hits sub-200ms bid latency through an integration layer that coordinates real-time pricing, user authentication, payment pre-authorization, and bid validation across multiple systems in a single request cycle.
Across all of these: Lighthouse scores of 95+, because the integration layer is optimized for the frontend, not the other way around.
Why In-House Teams Struggle With This
Integration architecture sits in an organizational no-man's-land. The ERP team owns SAP. Marketing owns the CMS. The platform team owns the frontend. Nobody owns the connections between them.
The result is predictable: each team builds their own integration their own way. The ERP team writes a SOAP endpoint. Marketing configures a Zapier flow. The platform team writes a custom Node script. Six months later, you've got twelve undocumented point-to-point integrations, no observability, and a frontend that's slower than it should be because it's making nine sequential API calls to render a single product page.
We come in as the team that owns the integration layer as a product. We design the data contracts, build the gateway, implement the event flows, add the observability, and hand your team a typed SDK that makes consuming integrated data as simple as calling a function.
Delivery Model
Integration architecture projects follow a phased approach:
Phase 1: System Audit and Contract Design (2-3 weeks)
We map every system, every data flow, every sync mechanism currently in place. We identify data ownership, conflict resolution rules, and freshness requirements. Output: typed API contracts and an integration architecture document.
Phase 2: Gateway Build and Core Integrations (4-8 weeks)
We build the gateway layer, implement the highest-priority integrations (typically ERP → frontend and PIM → frontend), and deploy observability. Your frontend team starts consuming typed APIs immediately.
Phase 3: Event-Driven Flows and Secondary Systems (3-5 weeks)
We add real-time event processing, connect secondary systems (CRM, payments, analytics), and implement the caching and invalidation strategies that keep data fresh without hammering upstream APIs.
Phase 4: Handoff and Hardening (2 weeks)
Documentation, runbooks, SLA dashboards, and team training. We define alert thresholds, escalation paths, and degradation strategies for when upstream systems go down.
Total timeline: 11-18 weeks depending on the number of systems and complexity of data transformations. We provide ongoing support SLAs for the integration layer with guaranteed response times for critical sync failures.
The Business Case
Enterprise API integration isn't a technical exercise — it's an operational one. Companies we've worked with report 15-25% reduction in operational costs from eliminating manual data reconciliation. Frontend teams ship faster because they're consuming clean, typed APIs instead of wrestling with raw ERP responses. And when something breaks, the observability layer tells you exactly where and why in minutes, not days.
The alternative is patching point-to-point integrations until the next ERP upgrade breaks everything. We've seen that play out. It doesn't end well.
See this capability in action
Frequently asked
How do you handle schema changes in upstream systems like SAP or 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 or REST — how do you decide which to use for each integration?
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.
What happens when an upstream system goes down?
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.
How do you ensure data consistency across ERP, CRM, and PIM systems?
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.
Can this integration layer support our existing frontend or does it require a rebuild?
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.
What does observability look like across the integration layer?
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.
How long does a typical enterprise integration architecture project take?
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.
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