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CAMP Systems provides aviation maintenance tracking and inventory management software for business jets, helicopters, and commercial aircraft operators.
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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.
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.
All Makini webhooks include a signature header for verification. The signature is an HMAC hash of the webhook payload using your webhook secret as the key. To verify a webhook, compute the HMAC using your secret and compare it to the signature header using constant-time comparison to avoid timing attacks. Never process webhook data without verification, as this could expose your system to forged requests. Your webhook secret is provided when you configure webhooks and should be stored securely. Webhook verification ensures that only legitimate requests from Makini are processed by your application.
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.
