How to Build an AI Review API Integration

Integrating AI review into your pipeline doesn't have to be complicated. This guide walks through the key steps of building a robust API integration with Verified Workflows — from initial authentication to production monitoring.

Step 1: Authentication Setup

Start by generating an API key from your Verified Workflows dashboard. Store it in environment variables, never in code. Your key grants access to specific workspaces and task types — scope it to only what your integration needs. Rotate keys periodically and use separate keys for development and production environments.

Step 2: Task Submission

Submit tasks via a POST request to the tasks endpoint. Each task needs a type (text-review, code-review, image-review, etc.), the content to review, and routing preferences. Include a unique client-generated ID in your request — this becomes critical for idempotency later. The API returns a task ID that you'll use to track progress and retrieve results.

Step 3: Routing Configuration

Configure how tasks are routed to reviewers. You can specify reviewer expertise requirements, priority levels, and timeout thresholds. For sensitive content, route to verified reviewers with domain expertise. For high-volume, lower-stakes review, use automated pre-screening with human spot-checks. Routing rules can be updated via the API without redeploying your integration.

Step 4: Webhook Setup

Don't poll for results — use webhooks. Configure a webhook endpoint in your dashboard and register it with your integration. When a review is complete, Verified Workflows sends a POST request to your endpoint with the full result payload. Include a signature header so you can verify the request is authentic. Your webhook endpoint should return a 200 status code within 5 seconds, or the system will retry.

Step 5: Result Handling

Process webhook payloads asynchronously. The result includes the review decision (approve, reject, flag), confidence score, reviewer comments, and metadata. Store results in your database for audit trails. If the review is flagged, route it to your internal escalation process. Design your result handler to be idempotent — duplicate webhook deliveries should not create duplicate processing.

Step 6: Error Management

Implement retry logic with exponential backoff for transient failures. Handle rate limiting by reading the Retry-After header and backing off accordingly. For permanent errors (invalid task type, missing required fields), log the error and alert your team rather than retrying. Keep a dead-letter queue for tasks that fail repeatedly so you can investigate without losing data.

Step 7: Idempotency

Always include an idempotency key with task submissions. If your network drops a request or your server retries, the idempotency key ensures the task isn't submitted twice. Use a deterministic key derived from the content and a unique identifier — for example, a SHA-256 hash of the content combined with a UUID. The API caches idempotency keys for 24 hours.

Step 8: Rate Limiting

Respect rate limits to avoid being throttled. The API enforces limits per workspace and per key. Check the X-RateLimit-Remaining header on every response and implement client-side throttling when you approach the limit. For high-volume integrations, contact support to request a rate limit increase before you go live, not after you start hitting 429 errors in production.

Step 9: Testing

Use the sandbox environment for development and testing. It mirrors production behavior but uses synthetic reviewers and doesn't count against your task quota. Test the full lifecycle: submit a task, receive the webhook, process the result, and handle error cases. Pay special attention to timeout scenarios — what happens when a reviewer doesn't complete a task within your SLA?

Step 10: Production Monitoring

Monitor key metrics from day one: task completion rate, average review latency, webhook delivery success rate, and error frequency. Set up alerts for anomalies — a sudden drop in completion rate might indicate a routing problem, while spiking latency could signal reviewer capacity issues. Use the API's analytics endpoint to track quality trends over time and adjust your routing rules as needed.