What we build
Production-grade integrations. Not Zapier flows.
Retry semantics, idempotency, typed contracts, rate-limit awareness, audit trails. The reliability primitives that turn an integration from a clever Zap into infrastructure.
Retry + replay queues
Every integration ships with retry semantics — exponential backoff, max-retries, dead-letter queues. Failed events replay automatically when the destination comes back; nothing silently drops.
Idempotency · safe to retry
Every operation keyed by request-ID so retries never double-fire. Charge once, email once, create once — even when the network drops mid-request. The discipline that lets you sleep with integrations running overnight.
Schema contracts · typed end-to-end
Schemas defined in code, validated at every boundary. Breaking changes caught in CI before they hit production. No 'we updated the field name and 14 webhooks broke silently' incidents.
Rate-limit + cost-aware orchestration
Adapters know each provider's rate limits and back off intelligently. Cost-aware routing for usage-billed APIs. No surprise overage bills, no 429 cascades, no manual back-off math in code reviews.
Error surfacing · safe to leave unattended
Errors classified, routed, alerted. Soft errors retry quietly; hard errors page. Schema mismatches queue for review; auth failures escalate. Operators see what actually needs attention, not a noise wall.
Audit trail per request
Every request, retry, response, and replay logged with input, output, latency, and outcome. Compliance reviewers replay any incident; ops debugs without paging engineering. Audit-grade from day one.
Architecture in production
Six layers. Composed from shared primitives.
Trigger, validation, orchestration, action, audit, AI assist. Each layer has shared primitives so we don't rebuild every integration from scratch — and so reliability discipline is baked in by default.
Trigger layer
Webhooks, schedulers, message queues, manual triggers. Every event captured at the boundary, normalised into a typed envelope, and queued for processing. No silent loss.
Validation + schema layer
Schemas validated at the boundary — payload shape, required fields, type coercion, enum membership. Schema mismatches queue for review with the original payload preserved; never silently drop.
Orchestration + retry layer
Adapters with retry semantics, idempotency keys, rate-limit awareness, circuit breakers. Failed requests retry with exponential backoff; persistent failures land in a dead-letter queue for review.
Action layer
The actual writes — to CRM, ERP, payment processor, analytics, AI. Idempotent by design; every action tagged with the request-ID so retries collapse cleanly. No double-sends, ever.
Audit + observability layer
Every request, retry, response logged with full context. Latency, error rate, cost, throughput tracked per integration. Alerts route to your existing on-call; nothing slips through the cracks.
AI assist layer
Where AI helps — schema-mismatch classification, anomaly detection, root-cause suggestions for failed requests, copy generation for downstream systems. Bolted to observability, not bolted on after.
AI-assisted integration explorer
Four integration shapes. Four jobs for AI.
Click between scenarios to see how systems connect — and where AI does the work that used to need a human. Schema drift, edge cases, lead scoring, model routing — every integration has a place AI earns its keep.
CRM ↔ ERP bidirectional sync · AI auto-maps fields when schemas change.
Where integrations land
Every shape of system plumbing.
CRM ↔ ERP, payment + billing, AI providers, marketing stacks, internal microservices, custom partner APIs. Same reliability discipline; tuned per provider.
CRM ↔ ERP sync
Salesforce ↔ NetSuite, HubSpot ↔ Stripe, Pipedrive ↔ QuickBooks. Bidirectional sync with conflict resolution; deal-stage updates, invoice posts, customer records — never out of sync.
Payment + billing integrations
Stripe, Adyen, PayPal — subscription webhooks, dunning logic, usage-based billing, payout reconciliation. Idempotent so failed retries never double-charge; audit-logged for compliance.
AI provider integrations
Multi-model orchestration — Claude, GPT, Gemini, custom hosted models. Routing by cost, latency, or capability; eval harness; prompt registry. The reliability layer from /ai-automation/ai-infrastructure.
Marketing stack integrations
Klaviyo, Mailchimp, Customer.io, Intercom, Twilio — event tracking, audience sync, lifecycle triggers. Reverse-ETL from your warehouse to operational tools; segments fresh on every send.
Internal API + microservice plumbing
Your services talking to each other — typed contracts, retry logic, circuit breakers, distributed tracing. The unglamorous work that keeps a microservices stack from collapsing in production.
Custom partner integrations
When the integration doesn't exist yet — custom partner APIs, B2B data feeds, legacy systems with quirky auth schemes. We scope the adapter, ship it with the same reliability discipline.
Tech stack
Modern stacks. Picked per workload.
Inngest for event-driven simplicity, Temporal for complex state machines, Zod for type-safe schemas, Datadog for tracing. We pick the right primitive for each integration, not the trendy one.
Why Axccelerate for API integration
Not a Zap that breaks at 3am.
Production infrastructure.
A Zap shows green and silently drops. Our integrations have retry semantics, idempotency, schema contracts, audit trails, distributed tracing — the reliability primitives that make a stack actually run unattended.
Pricing
Priced to your integrations — not pretend hourly rates.
Integration builds are scoped end-to-end. We cost against your providers, complexity, and reliability needs before quoting.
Glossary
The vocabulary behind every reliable integration.
A quick reference for the terms that show up in integration design, incident reviews, and reliability discussions — the language your engineering team will use during build and ops.
- Idempotent
- Safe to retry
An operation that produces the same result whether it runs once or ten times. Critical for integration reliability — a retry must never double-charge, double-email, or double-create.
- Webhook
- HTTP callback
An HTTP POST sent by a service when something happens — order placed, payment received, deal won. The standard pattern for real-time integration in modern SaaS.
- Replay queue
- Failed-event retry buffer
A durable queue of events that failed initial delivery, ready to replay when the destination is healthy again. Prevents data loss during downstream outages.
- Dead-letter queue
- Persistent-failure holding pen
Where events go when retries are exhausted — not deleted, just paused for review. Ops examines DLQ contents and either fixes the underlying issue or replays manually.
- Backoff
- Retry delay strategy
The pattern of waiting longer between successive retries — exponential, jittered, or fixed. Prevents retry storms; respects provider rate limits; stays gentle on degraded systems.
- Circuit breaker
- Fail-fast safety pattern
A pattern that stops sending requests to a failing destination after N errors, gives it time to recover, then probes before resuming. Prevents cascade failures.
- Rate limit
- Provider request ceiling
The maximum requests-per-second a provider accepts before returning 429s. Adapters know each provider's limits and back off intelligently to avoid hitting them.
- Schema contract
- Typed boundary definition
The agreed shape of data crossing a system boundary — fields, types, required vs optional. Validated at every boundary; breaking changes caught in CI.
- Reverse-ETL
- Warehouse → tools sync
Pushing computed data from your data warehouse back into operational tools — CRM, marketing platforms, ad audiences. Always-fresh data without manual CSV uploads.
- OAuth 2.0
- Delegated authorisation
The standard pattern for letting one service act on behalf of a user in another service — token-based, refreshable, scoped. The default for modern SaaS APIs.
- HMAC signature
- Webhook authenticity proof
A cryptographic signature on a webhook payload that proves it came from the claimed sender. Verified at the receiver; protects against spoofed webhooks.
- Distributed tracing
- Cross-service request follow
The pattern of tagging every request with a trace ID and propagating it through every service that handles it — so debugging an integration failure shows the full chain, not just one hop.
Scope the integration.
Sleep through the night.
30-minute scoping with a senior engineer. You'll leave with a topology map, reliability plan, and realistic timeline — not a sales pitch.