




Uptake is an AI-driven asset performance platform that uses machine learning and IoT data to predict failures, optimize maintenance and drive operational insights in heavy-asset industries.
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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.
Makini is SOC 2 Type 2 compliant and undergoes penetration testing twice annually. We use industry-standard encryption protocols including TLS 1.2+ for data in transit and AES-256 for data at rest. Customer credentials are encrypted using secure key management practices. Our infrastructure follows security best practices including network segmentation, access controls, and regular security audits. For highly regulated industries or customers with strict compliance requirements, we offer self-hosted deployment options that keep all data within your infrastructure. We've successfully met security requirements for enterprises including financial institutions and government contractors.
500-level errors indicate issues on Makini's side or with the connected system. These are typically temporary and retrying the request after a brief delay often succeeds. Implement exponential backoff for retries—wait a few seconds, then progressively longer intervals. If errors persist beyond a few retries, check the Makini status page for service disruptions. The error may also stem from the connected system experiencing issues rather than Makini itself. For persistent 500 errors, contact support with the request ID from the error response. Include details about when the error started, which operations are affected, and which connections are impacted. Our support team can quickly identify whether the issue is systemic or connection-specific.
Makini provides several performance monitoring capabilities. API responses include timing information showing request processing time. The dashboard includes performance metrics showing average response times, throughput, and error rates over time. You can set up alerts for performance degradation or error rate increases. Each request generates a unique request ID that enables detailed performance analysis. For workflow-based integrations, execution logs show per-step timing, helping identify bottlenecks. We recommend implementing client-side monitoring to track end-to-end latency including network time. Monitor trends over time rather than individual requests—occasional slow requests are normal, but sustained increases may indicate issues requiring investigation.
