Custom AI for Proactive Construction Hazard Identification
Syntora builds custom AI solutions for proactive hazard identification on small construction sites. We analyze site photos and reports to flag safety risks before incidents occur.
Key Takeaways
- Syntora builds custom AI solutions for proactive hazard identification for construction companies with 10-50 employees.
- The systems analyze daily site photos and text reports to detect safety violations and potential risks.
- We integrate directly with existing photo management tools like CompanyCam or project management systems.
- Automated hazard reports are generated in under 5 minutes, flagging issues hours before a manual review.
Syntora specializes in building custom AI solutions for proactive hazard identification on construction sites. Syntora engineers systems that analyze site photos against OSHA standards to flag potential safety risks. The approach involves tailoring AI models like Claude API to specific client safety protocols and site conditions.
The scope of such a system is primarily determined by the volume and structure of source images, alongside the specific OSHA standards that require verification. Integrating with a structured photo application like CompanyCam generally allows for a more direct implementation. However, if photos are sourced from unstructured channels like text messages, an initial setup phase would be needed to establish robust ingestion and metadata extraction. Syntora's approach prioritizes understanding your existing workflows to design a system that fits your operational context and delivers actionable safety insights.
Why Do Construction Safety Audits Still Rely on Manual Checklists?
Most safety programs rely on paper checklists or simple digital forms in Smartsheet. These tools only capture what a supervisor actively reports. They cannot see a potential hazard in the background of a routine progress photo, creating a significant blind spot in safety monitoring.
A site supervisor for a 30-person crew is focused on a concrete pour and snaps daily progress photos. In the background of one image, a ladder is placed at an unsafe angle next to an open, un-shored trench. The supervisor's daily checklist is marked 'all clear' because his attention was elsewhere. The safety manager sees the photo the next day, hours after a near-miss incident occurred.
Larger platforms like Procore or Autodesk Construction Cloud have safety modules, but they are designed for incident logging, not proactive detection. Their analysis tools are often limited to keyword searches in text-based reports. These systems cannot interpret the visual context of a photograph to identify a combination of factors, like equipment placement and missing barriers, that create a high-risk situation.
How We Build an AI to Proactively Identify Construction Hazards
Syntora's approach to proactive hazard identification involves a structured engagement focused on your specific safety protocols and existing data sources.
The initial phase would involve detailed discovery to understand your current photo ingestion methods and safety compliance requirements. We would audit your existing photo sources, whether an API connection to a service like CompanyCam, shared cloud storage, or other digital assets. Collaborating with your safety lead, Syntora would then develop and refine a detailed prompt for the Claude API. This prompt would incorporate specific OSHA regulations relevant to your operations, such as scaffolding safety (1926.451) or fall protection (1926.501). We have experience building document processing pipelines using the Claude API for financial documents, and the same pattern applies to analyzing images for compliance. This initial prompt engineering would involve iterating with a sample set of your historical photos to ensure accurate hazard detection.
The core of the system would be a Python service, likely deployed on AWS Lambda, designed to trigger upon the upload of a new photo. This service would securely transmit the image and the engineered prompt to the Claude API for analysis. The prompt would instruct the AI to evaluate the image as a certified safety inspector, cross-referencing against the defined OSHA codes to identify potential hazards. The AI would then return a structured JSON object, detailing any identified risks, their approximate location within the image, and the corresponding OSHA standard. Syntora would engineer this analysis pipeline to target a processing time under 45 seconds per image, depending on image complexity and API response times.
The JSON results, along with a secure link to the source image, would be stored in a Supabase database. To facilitate immediate action for high-severity issues, a webhook would be configured to send an alert to a designated Slack channel or Microsoft Teams group. This alert would include the flagged image, the specific hazard identified, and the relevant OSHA code, enabling prompt intervention by safety managers.
For monitoring and auditing, Syntora would build a simple dashboard using Vercel. This dashboard would display key operational metrics such as photos processed per day, types of hazards detected, and API response times. The underlying FastAPI service would be engineered for efficiency, with typical response times expected to be under 800ms. All analyses would be logged, creating a searchable record valuable for safety audits, compliance checks, and identifying recurring safety patterns over time.
A typical engagement for this complexity involves a 6-10 week build timeline, preceded by 2-3 weeks of discovery. The client would need to provide access to historical site photos, internal safety documentation, and dedicated time from a safety lead for collaboration and feedback. Deliverables would include the deployed, custom AI system, source code, detailed technical documentation, and training for relevant personnel.
| Manual Safety Audits | Syntora's AI-Powered Audits |
|---|---|
| Hazard Detection Time: 24-48 hours | Hazard Detection Time: Under 10 minutes |
| Coverage: Spot-checks on <10% of site activity | Coverage: Analyzes 100% of daily site photos |
| Reporting: Manual data entry into spreadsheets | Reporting: Automated alerts sent to Slack with flagged images |
What Are the Key Benefits?
Find Hazards in Minutes, Not Days
The system analyzes new site photos in under 60 seconds. You get notified of an unsecured ladder before the end of the workday, not during the next day's audit.
Pay for Results, Not Per-User Seats
A one-time build cost and a low monthly AWS Lambda hosting fee, often under $50. No recurring SaaS license that increases as you hire more crew members.
You Own the Code and Safety Data
You get the complete Python source code in a private GitHub repository. All analyzed data is stored in your own Supabase instance, not a third-party platform.
Alerts Where Your Team Already Works
Hazard notifications are sent directly to Slack or Microsoft Teams. Supervisors do not need to learn or log into another piece of software to get critical alerts.
Tuned to Your Specific Trade
The AI is prompted with OSHA standards that matter to you. A roofing contractor's system checks for fall protection; a plumbing contractor's checks for trench safety.
What Does the Process Look Like?
Scope & Data Access (Week 1)
You provide read-access to your photo management system and list your top 5 safety concerns. We define the specific OSHA codes for the AI to check against.
AI Prompt & Backend Build (Week 2)
We build the core Python service on AWS Lambda and engineer the Claude API prompt. You receive a sample analysis of 10 of your past photos to review.
Integration & Deployment (Week 3)
We connect the system to your live photo feed and configure the Slack or Teams alerts. The system begins processing images in a staging environment for validation.
Live Monitoring & Handoff (Week 4)
The system goes live. For 30 days, we monitor alerts for accuracy and tune the AI prompt. You receive a runbook detailing the architecture and maintenance.
Frequently Asked Questions
- What does a custom hazard detection system cost?
- Pricing depends on the number of photos processed daily and the complexity of the safety rules. A system for a single trade analyzing up to 100 photos per day is a straightforward build. Multiple trades with unique rule sets require more complex prompting and validation. We provide a fixed-price proposal after a discovery call at cal.com/syntora/discover.
- What happens if the AI misses a hazard or flags something incorrectly?
- The system is an aid, not a replacement for a human safety manager. False positives and negatives can occur. The alert system includes feedback buttons ('Correct Hazard', 'Incorrect Flag'). This feedback is logged and used to refine the Claude API prompt every 30 days, improving accuracy over time as the system learns your specific site conditions.
- How is this different from off-the-shelf construction AI like Indus.ai or OpenSpace?
- Those are project management platforms that use AI for progress tracking or site mapping, not proactive safety. Syntora builds a lightweight, single-purpose system that plugs into your existing tools. It focuses only on identifying hazards based on the specific OSHA codes you care about, delivering critical alerts without the overhead of a full platform.
- Does this work with video feeds from site cameras?
- The current system is optimized for still photos, as they are the most common daily record. Analyzing real-time video feeds requires a different architecture with higher hosting costs. We can build video analysis systems, but the scope is larger. Processing daily photo logs provides 80% of the value at 20% of the cost for most small to mid-sized contractors.
- Who on my team needs to be involved in the build?
- We need about two hours from your lead safety manager or a senior superintendent during Week 1 to define the safety rules. After that, we need an administrator to provide an API key for your photo management system and authorize the Slack or Teams integration. No technical staff are required on your end for the build or maintenance.
- What are the ongoing hosting costs after the system is built?
- Hosting costs are pay-as-you-go. A typical system processing 150 images a day runs on AWS Lambda, Supabase, and the Claude API. The combined monthly cost is usually between $40 and $100, depending on volume. You pay the cloud providers directly, so there are no markups or recurring license fees to Syntora after the initial build.
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