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Makini is a unified API platform for industrial systems integration. We provide connectivity to over 2,000 ERP, CMMS, and WMS systems through a single, standardized API. Instead of building separate integrations for each system, you connect once to Makini and gain access to all supported platforms. This approach transforms integration projects that typically cost tens of thousands of dollars and take months into a manageable operational expense with deployment times of 1-2 weeks.
Industrial systems are often heavily customized, and Makini is built to handle this. For reading data, Makini can access virtually any field or custom table in connected systems. Through the connection settings interface, you can specify custom fields, tables, or entities to include in API responses. These show up alongside standard fields in the unified model. For custom objects not in our default model, you can request them through the interface and they'll be available immediately. For writing data, customization support varies by system but covers most common scenarios. During implementation, we work with you to identify required customizations and ensure they're properly configured before going live.
Design your webhook receiver to handle duplicates and out-of-order webhooks, as network issues or retries can cause both scenarios. Keep the receiver lightweight—ideally writing incoming webhooks to a queue or reliable storage—then process them asynchronously. This prevents timeouts and allows your system to handle high-volume webhook spikes. Respond with a 200 status code immediately after receiving the webhook, before processing begins. Implement idempotency by tracking processed webhook IDs and skipping duplicates. Use constant-time comparison for signature verification to prevent timing attacks. If webhook processing fails, log the error but still return 200 to prevent unnecessary retries. Set up monitoring and alerts for webhook failures so you can investigate issues promptly. For critical workflows, combine webhooks with periodic polling as a fallback mechanism.
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.
