Automating Construction Safety Audits with Amazon Nova
By Braincuber Team
Published on February 12, 2026
Construction sites are dynamic environments where safety compliance is critical. A missing fire extinguisher, an obstructed emergency exit, or a worker without a hard hat can lead to severe penalties or, worse, accidents. Traditional manual audits are time-consuming and prone to human error.
In this tutorial, we will explore how ApexBuilders, a fictional construction giant, automates their "Site Safety Readiness" using Amazon Nova models on Amazon Bedrock. By combining the speed of Amazon Nova Pro for object detection with the reasoning of Claude 3.5 Sonnet, we can build a system that verifies site safety in seconds.
Why Amazon Nova?
- Hyper-Fast Detection: Nova Pro is purpose-built for identifying objects (like safety gear) with high precision and speed.
- Cost-Effective: Significantly cheaper than running general-purpose LLMs for simple visual tasks.
- Multimodal: Can process site photos directly without needing a separate OCR engine.
The "SiteGuard" Architecture
The solution involves two pipelines: a Preparation Pipeline (to teach the AI what to look for) and an Audit Pipeline (for real-time verification).
1. Preparation Pipeline (The Knowledge Base)
- Input: Reference photos of approved equipment (e.g., "Class ABC Fire Extinguisher", "Standard Hard Hat").
- Processing: Use Claude 3.5 Sonnet to analyze these images and generate detailed textual descriptions and "Anti-patterns" (e.g., "A red bucket looks like an extinguisher but lacks a nozzle").
- Output: A DynamoDB table containing robust detection rules for every item on the safety checklist.
2. Audit Pipeline (Real-Time)
- Input: Site supervisor snaps a photo of a "Safety Zone".
- Processing: Amazon Nova Pro scans the image against the safety checklist rules stored in DynamoDB.
- Output: The app draws green boxes around compliant items and red alerts for missing or obstructed gear.
Step 1: Generating Detection Rules
First, we need to generate descriptions that help the AI distinguish between correct and incorrect equipment. We use Claude 3.5 Sonnet for this "reasoning" step.
import boto3
import json
bedrock = boto3.client(service_name='bedrock-runtime')
def generate_safety_equipment_description(image_bytes):
"""
Asks Claude to describe the safety gear to create a master definition.
"""
prompt = """
Describe this safety equipment in technical detail for an object detection model.
Focus on:
1. Shape and Aspect Ratio
2. Distinctive Colors (e.g., 'Safety Orange', 'Fire Engine Red')
3. Key components (e.g., nozzles, reflective strips)
4. Common potential false positives (what looks like this but isn't?)
"""
body = json.dumps({
"anthropic_version": "bedrock-2023-05-31",
"max_tokens": 1000,
"messages": [
{
"role": "user",
"content": [
{"type": "image", "source": {"type": "base64", "media_type": "image/jpeg", "data": image_bytes}},
{"type": "text", "text": prompt}
]
}
]
})
response = bedrock.invoke_model(
modelId="anthropic.claude-3-5-sonnet-20240620-v1:0",
body=body
)
return json.loads(response['body'].read())['content'][0]['text']
Step 2: The Audit (Detection)
Now, when a supervisor uploads a zone photo, we use Amazon Nova Pro. Why Nova? Because it supports high throughput and is optimized for visual understanding tasks like finding multiple small objects (e.g., 5 hard hats and 2 extinguishers) in a single wide shot.
def audit_site_zone(site_image_bytes, checklist_items):
"""
Uses Amazon Nova Pro to verify if checklist items exist in the photo.
"""
# Construct a prompt that lists what we are looking for
checklist_text = ", ".join(checklist_items)
prompt = f"Detect the following safety items: {checklist_text}. Return bounding boxes for each."
body = json.dumps({
"schemaVersion": "messages-v1",
"messages": [
{
"role": "user",
"content": [
{"image": {"format": "jpeg", "source": {"bytes": site_image_bytes}}},
{"text": prompt}
]
}
],
"inferenceConfig": {"temperature": 0.1} # Low temp for precision
})
# Invoke Amazon Nova Pro
response = bedrock.invoke_model(
modelId="amazon.nova-pro-v1:0",
body=body
)
# Nova returns structured data with bounding boxes
results = json.loads(response['body'].read())
return results
Conclusion
By automating the "eyes" of the safety audit, ApexBuilders can ensure 100% compliance without slowing down construction. The combination of Claude 3.5 Sonnet (for understanding what things should look like) and Amazon Nova Pro (for finding them quickly in the wild) creates a robust, scalable safety layer that protects both workers and the company's bottom line.
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