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Makini supports over 2,000 industrial systems across ERP, CMMS, and WMS categories. This includes major platforms like SAP (ECC, S4/HANA, Business One), Oracle NetSuite, Microsoft Dynamics, IBM Maximo, and specialized industrial systems. We support both cloud-based and on-premises installations. If you need to connect to a system we don't currently support, we're committed to building that integration for you at no additional charge—most new integrations are completed within one business day. You can view our full list of supported systems at makini.io/integrations.
Integration timelines vary by complexity. For standard implementations with no customizations, connections can be live within 1-2 weeks. This includes authentication setup and basic workflow configuration. For implementations requiring custom workflows or specific business logic, timelines typically range from 2-6 weeks depending on the scope. Complex enterprise deployments with multiple systems and custom requirements may take 6-10 weeks. These timelines are significantly shorter than traditional integration projects, which often take 2-24 months.
Makini provides webhook testing tools in the dashboard where you can trigger test webhook deliveries to verify your endpoint configuration. Test webhooks use sample payloads matching actual event structures. Verify your endpoint receives the webhook, validates the signature correctly, and responds with a 200 status code within 10 seconds. Test webhook retries by having your endpoint return error codes or timeout, then verify Makini retries as expected. Test duplicate handling by processing the same webhook multiple times. For local development, use tools like ngrok to expose your local endpoint for webhook testing. The webhook logs in the Makini dashboard show delivery attempts, response codes, and timing, helping debug delivery issues.
For bulk operations, we recommend batch processing with appropriate rate limiting and error handling. Makini Flows provides built-in batch processing capabilities with configurable batch sizes, delays between batches, and error handling. For API-based bulk operations, implement pagination when retrieving large datasets—our API returns results in pages with continuation tokens for fetching subsequent pages. When writing large volumes of data, break operations into smaller batches (typically 50-100 records per batch) with delays between batches to avoid overwhelming the target system. Implement comprehensive error logging to identify which specific records fail in a batch. For very large operations (thousands of records), consider asynchronous processing patterns where you queue operations and process them in the background.
