



Arvist is an AI-driven warehouse-quality and operations-management platform that helps logistics operators and warehouses monitor compliance, detect workflow issues and ensure quality control by analysing data from cameras, scanners and WMS systems.
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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.
Makini provides several debugging tools. The dashboard shows detailed request logs including request/response payloads, headers, status codes, and timing. Each API request generates a unique request ID included in responses—provide this when contacting support for faster investigation. For workflow-based integrations, Makini Flows includes execution logs showing each step's input/output, timing, and any errors. Connection health monitoring shows sync history, error rates, and connection status over time. API responses include detailed error information with error codes and messages. For development, we recommend using API clients like Postman or Insomnia to interactively test API calls and inspect responses. Our API documentation includes request/response examples for all endpoints.
Makini uses cursor-based pagination for retrieving large datasets. API responses include a `next_cursor` field when additional results are available. To retrieve the next page, include the cursor value in your next request: `GET /api/v1/purchase-orders?cursor=CURSOR_VALUE`. Cursor-based pagination is more reliable than offset-based pagination because it handles data changes between requests—if records are added or deleted while you're paginating, you won't miss records or see duplicates. Page size is configurable up to a maximum limit (typically 100-500 records per page depending on entity type). For optimal performance, use the largest page size your application can handle efficiently. The API response also includes total count when available from the source system.
