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Makini maintains a comprehensive data model built from analyzing thousands of industrial systems. When data flows through Makini, we automatically transform it from the source system's format into our standardized structure. For example, purchase orders from SAP, NetSuite, and Dynamics all return with consistent field names, data types, and structures. This normalization happens in real-time as data passes through the API. You also have access to raw data if needed for specific use cases. The unified model covers common entities like purchase orders, work orders, inventory items, vendors, and assets, with extensive field coverage across systems.
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.
Write operation limitations vary by system. Common limitations include: field-level restrictions (some fields may be read-only), business rule validation (orders may require certain fields or valid vendor codes), permission requirements (the connected account needs specific permissions), timing restrictions (some systems prevent modifications after certain workflow states), and rate limits on write operations. Custom fields in target systems may not be writable through standard APIs. Some systems have transactional requirements—for example, purchase order line items must be created in the same transaction as the order header. During implementation, we identify write operation limitations for your specific use cases and design workflows that work within those constraints.
When a system becomes unavailable, Makini detects the connectivity failure and marks the connection status accordingly. Scheduled syncs will fail with connectivity errors. API requests to the connection will return error responses indicating the system is unreachable. Makini continues attempting scheduled syncs using exponential backoff—initial retries happen frequently, then progressively less often to avoid overwhelming the system when it comes back online. Webhooks notify you of the connection status change. When the system comes back online, normal operations resume automatically. For temporary outages, no action is required. For extended outages, you may want to notify the customer. Connection credits remain consumed during outages since the connection configuration persists.
