Integrations fail in the parts nobody demos: the token that expires at 3am, the pagination that silently stops at page ten, the webhook delivered twice, the rate limit hit during a bulk import. A working integration is not one that succeeds on the happy path — it is one that behaves correctly when the other system misbehaves.

Who this is for

Companies connecting systems with no off-the-shelf integration. Teams whose existing integrations fail intermittently and unpredictably. Businesses adding AI provider APIs to production applications. Anyone who has discovered a broken sync through a customer rather than a monitor.

Problems we solve

Silent partial failures. A sync that stops halfway and reports success.
Duplicate processing. Webhooks delivered more than once with no idempotency handling.
Rate limit collapse. No backoff, so a burst turns into a cascade of failures.
Auth expiry. Refresh tokens that are never refreshed until something breaks at the worst time.
Incomplete pagination. Cursor handling that quietly drops records.
No reconciliation. No way to prove both systems actually agree.

What we build

REST and GraphQL integrations with correct pagination, filtering and partial-failure handling
OAuth flows with automatic token refresh and clear re-authorisation paths
Webhook receivers with signature verification, idempotency keys and replay protection
Retry with exponential backoff, circuit breakers and dead-letter queues
Rate limit awareness with request queueing rather than blind retries
AI provider integration with streaming, fallback and cost tracking
Reconciliation jobs that detect and report drift between systems
Monitoring and alerting on failure rates and sync lag

How we work

We design for the other system failing, because eventually it will. Every integration is idempotent, so a retry never doubles an effect. Sync state is tracked explicitly so a failure resumes rather than restarts. Reconciliation runs on a schedule and reports discrepancies, because an integration that cannot prove it is correct will eventually be wrong without anyone noticing.

Technologies we use

Laravel and Node.js for integration services, Redis for queues, locks and rate limiting, n8n where a visual workflow suits the use case better than code. OpenAI, Claude, Gemini and Ollama for AI provider integration. Structured logging with request tracing across system boundaries.

Business benefits

Systems stay in agreement without manual reconciliation
Failures alert immediately instead of surfacing weeks later
Duplicate charges, emails and records are prevented by design
Integrations survive the other system's outages and rate limits

Where it pays off

CRM synchronised with billing and accounting
E-commerce orders flowing into fulfilment and inventory
Payment webhooks driving provisioning and entitlement
Data warehouse pipelines from operational systems
AI provider APIs embedded in production features
Partner APIs exposed to or consumed from third parties

Common questions

What if the API has no documentation?

We work from observed behaviour, build a test harness against a sandbox where one exists, and document what we find. Undocumented APIs need more defensive handling, and we scope for that rather than discovering it late.

How do you handle breaking changes upstream?

Version pinning where the provider supports it, contract tests that fail loudly when responses change shape, and monitoring that catches schema drift before it corrupts data.

Can you fix an integration someone else built?

Usually. We start by adding logging and reconciliation to establish what is actually happening, since intermittent integration bugs are rarely where people assume.

Have a sync you no longer trust? Reconciliation reporting will tell you how bad it actually is.

AI & Third-Party API Integration