Appsierra's cloud integration services connect the systems your business actually runs on — ERP, CRM, commerce, finance, HR and bespoke applications — across cloud and on-premise. We design API-led integration layers, event-driven pipelines and iPaaS flows, then make them survive reality with idempotent handlers, retries, dead-letter queues, contract tests and end-to-end observability.
Integration is mostly a design problem. The code is the easy part; the failure modes are where projects are won or lost.
We chart every system of record, the direction each piece of data moves, who owns it, how often it changes and what breaks downstream when it is late or wrong. Most integration problems are visible on that map before a line of code is written.
Synchronous API, asynchronous event, scheduled batch or file transfer — chosen per flow against latency, volume and consistency needs, rather than forcing one pattern across an estate because it is the tool already licensed.
Interfaces are built with idempotency keys, exponential backoff with jitter, circuit breakers, dead-letter queues and replay, plus the transactional outbox pattern where a database write and a published event must not diverge — then proven with system integration testing.
Correlation IDs, distributed tracing, per-interface dashboards and alerts on queue depth and error rate ship with the integration, along with runbooks and versioning rules so your team can extend it without breaking consumers.
Integrations rarely fail during the build. They fail months later, when a network blip causes a retry that duplicates an order, when an upstream team adds a field and an untested consumer rejects the payload, when a queue quietly backs up over a weekend, or when nobody can tell which of five systems dropped a transaction. Every one of those is a design decision that was never made: no idempotency, no versioned contract, no alert on consumer lag, no correlation ID. Building for the happy path is what makes integration estates fragile.
This page is about connecting systems. Two related disciplines are covered separately, and it is worth knowing which you need. Our cloud app development services cover building the cloud-native applications themselves — the product, its services and its front end. Our cloud infrastructure management services cover running the environment underneath: provisioning, scaling, monitoring, patching and cost management. If your problem is that two or more systems need to exchange data reliably, this page is the right one.
Every new connection made directly between two systems multiplies the number of links to maintain, until one retirement or version bump forces changes across a dozen places at once.
Without idempotency and reconciliation, a retried message or a partially applied update leaves two systems quietly disagreeing about an order, a balance or a customer record.
When a flow crosses four systems and nobody holds a correlation ID, diagnosing a missing transaction becomes an archaeology exercise across four separate log stores.
An upstream field renamed or an enum extended without versioning breaks consumers in production, because nothing in the pipeline tested the contract between them.
APIs, events, iPaaS flows, business-system connections and the testing that keeps them honest.
Well-versioned REST and GraphQL interfaces documented with OpenAPI, fronted by a gateway such as Azure API Management, Kong or Apigee for authentication, rate limiting, quotas and consumer analytics, with OAuth 2.0 and mutual TLS where the traffic warrants it.
Integration platform delivery on MuleSoft Anypoint, Dell Boomi, Azure Logic Apps and Workato — including rescuing estates where low-code flows have grown organically into undocumented, untestable business logic nobody wants to touch.
Publish and subscribe designs on Apache Kafka, Amazon EventBridge, Amazon SQS and SNS, and Azure Service Bus, with schema registries and Avro or Protobuf contracts, consumer groups, ordering guarantees and replay from a durable log.
Connecting SAP, Dynamics, NetSuite, Salesforce, HubSpot, Shopify and warehouse or logistics platforms so orders, inventory, invoices and customer records stay in step — including the feeds that populate a cloud data warehouse.
Wrapping or replacing SOAP, WSDL-defined and flat-file interfaces with documented REST APIs and events, using a strangler pattern so the legacy system keeps running while traffic moves interface by interface.
Consumer-driven contract tests with Pact, service virtualisation for systems you cannot call freely, and end-to-end flow verification — extended by API testing services for functional, security and load coverage.
An integration you cannot see into is an integration you cannot operate. Once a transaction crosses four systems, the only practical way to answer where did this order go is a correlation ID issued at the entry point and carried through every hop, with traces exported to a single place. We ship that with the interface rather than adding it after the first painful incident, alongside dashboards per interface and alerts on the signals that actually predict failure: consumer lag, queue depth, dead-letter volume, error rate and end-to-end latency.
Safety to change matters just as much, because integrations outlive the projects that create them. Contracts are versioned and evolved backward-compatibly, consumer-driven contract tests run in the pipeline so a breaking change fails before it reaches an environment, and service virtualisation lets teams test against systems they cannot call freely. Resilience is verified deliberately by injecting timeouts, duplicate deliveries and malformed payloads, so retry, idempotency and dead-letter handling are proven rather than assumed. The result is an estate a new engineer can extend without a two-week archaeology phase first.
A correlation ID is issued at the entry point and carried through every hop, with OpenTelemetry spans exported to one place, so a stalled transaction is located in minutes rather than reconstructed from four log stores.
Queue depth, consumer lag, dead-letter volume, error rate and end-to-end latency per interface are what predict an outage — far earlier than a downstream user reporting that an order never arrived.
Versioned contracts, backward-compatible schema evolution and contract tests in the pipeline let interfaces evolve without breaking consumers, the same discipline we apply in microservices testing.
Senior integration engineers, agreed reliability targets, and a working interface early rather than a design document.
A named senior integration engineer owns the outcome, and you talk directly to the people designing and building the interfaces.
System access, environment setup and agreed scope happen in week one, with the first interface delivered and tested from week two.
AI tooling speeds up mapping, schema translation and test generation, and senior engineers review every interface before it carries production data.
We agree measurable targets — latency, throughput, error budget, reconciliation tolerance — up front instead of billing open-ended hours.
ISO 9001 and ISO 27001 certified, CMMI Level 3 aligned and NDA-first, with credential handling and data-transfer rules agreed before any system is connected.
Scale the pod up for a migration programme and down for steady-state support, without a long lock-in on either side.
Cloud integration services connect applications, data stores and business processes that live in different systems, whether those run in the cloud, on-premise or both. The work covers designing the integration pattern for each flow, building and securing APIs, delivering iPaaS or event-driven pipelines, mapping and transforming data between models, handling failure with retries and dead-letter queues, modernising legacy interfaces, and adding the tracing, monitoring and contract testing that keep the connections reliable as the systems around them change.
An integration platform as a service such as MuleSoft, Boomi, Azure Logic Apps or Workato gives you prebuilt connectors, a visual designer and managed runtime, which is fast for common system-to-system flows and accessible to less specialised teams. Custom-built integration gives full control over performance, cost, testing and deployment, and suits high-volume, latency-sensitive or unusual logic. Most estates end up with both: iPaaS for breadth of connectivity, purpose-built services for the flows where volume, cost or complexity make a licensed platform the wrong fit.
Use a synchronous API when the caller genuinely needs an answer before it can continue, such as a payment authorisation or an availability check. Use events when several systems care about something that happened, when the producer should not wait for or depend on consumers, or when spikes need absorbing. Events give loose coupling, natural buffering and replay, at the cost of eventual consistency and harder debugging. In practice a healthy estate mixes both, chosen per flow rather than as a house style.
By assuming failure at design time. Handlers are idempotent so a retry cannot duplicate an order, retries use exponential backoff with jitter to avoid overwhelming a recovering system, and circuit breakers stop a failing dependency cascading. Messages that cannot be processed land in a dead-letter queue for inspection and replay rather than being dropped. Every message carries a correlation ID, distributed tracing links the hops, and alerts fire on consumer lag, dead-letter volume and error rate, so problems surface before a customer reports one.
Yes, and usually without a big-bang cutover. The typical approach is a strangler pattern: put a documented REST or event interface in front of the legacy endpoint, move consumers across one at a time, and retire the old interface only once nothing calls it. Along the way we replace brittle flat-file and scheduled FTP transfers with APIs or events where the data genuinely needs to be timely, keep batch where batch is honestly sufficient, and add the contract tests and monitoring the original interfaces never had.
Integration testing here is not a final end-to-end sweep. Consumer-driven contract tests using a tool such as Pact verify that a provider still satisfies what each consumer expects, and run in the pipeline so a breaking change fails before deployment. Service virtualisation stands in for systems that cannot be called freely, such as a payment provider or a licensed ERP sandbox. Above that we run end-to-end flow verification on realistic data, plus resilience checks that deliberately introduce timeouts, duplicate messages and malformed payloads.
Tell us what you're building, testing or scaling — a senior engineer sends a short, honest read and a low-risk way to start.
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Whether you are connecting a new commerce platform to an ageing ERP, untangling point-to-point sprawl, or moving off brittle file transfers, we will map the flows and scope a senior pod. Contact us to start with a short integration review.
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