Sprinkler Age A Publication of the American Fire Sprinkler Association
SMARTER, MORE EFFICIENT WORKFLOWS
Fire sprinkler contractors are facing growing pressure. Project designs are becoming more complex and experienced designers are harder to find. As a result, firms are forced to look for practical ways to improve productivity without lowering quality or increasing risk.
In this environment, artificial intelligence (AI) is beginning to emerge as a tool fire sprinkler design teams need to carefully consider. Fire sprinkler design is governed by strict codes, reviewed by Authorities Having Jurisdiction (AHJs), and directly tied to life safety, so any new technology must earn trust through careful and responsible use. When applied in a focused manner, AI can help design teams work faster, reduce rework, and improve consistency while keeping engineering judgment firmly in human hands.
THE CURRENT DESIGN ENVIRONMENT
Across the industry, the pace of work is accelerating and margins for error are shrinking. The shortage of qualified sprinkler designers has become a recurring discussion point in Sprinkler Age, AFSA, and the industry as a whole. Many firms are facing the same challenge: demand is rising, but the talent pipeline is struggling to keep up.
Tools that can increase efficiency are not just about convenience, they are becoming a business necessity.
APPLYING AI RESPONSIBILITY TO SPRINKLER DESIGN
How can artificial intelligence be applied to fire sprinkler design in a way that is both efficient and responsible? To answer this, Caident brought together NICET-certified sprinkler designers, fire protection engineers, and software developers with experience in machine learning and AI. Caident has also partnered with fire sprinkler designers and installers to test new tools in real project environments. All development has been guided by real-world design conditions and shaped by how design teams actually work day-to-day.
The goal is to help designers do more work, faster, while maintaining, and in many cases improving, overall quality. AI can be used to take over repetitive tasks that slow teams down, allowing designers to spend more time on system-level thinking and technical judgment. The sections that follow highlight where thoughtful deployment of AI is delivering real value in sprinkler design workflows.
WHERE AI CAN BE USED TO SUPPORT SPRINKLER DESIGN WORKFLOWS
AI has shown the most success in sprinkler design when it is applied to focused tasks with clear rules and clear outcomes. Today’s most practical AI use cases support documentation, validation, code research, and information management.
Checking Occupancy-Specific and Jurisdiction-
Specific Requirements: One of the strongest use cases for AI is speeding up code research. Designers often need to identify sprinkler design criteria, hazard classifications, fire pump requirements, standpipe triggers, and special protection features based on a building’s occupancy, code edition, and local amendments.
AI tools can quickly surface relevant code sections, tables, and amendments for a given occupancy and code year. This helps engineers narrow down where to look and identify possible code paths faster. These results must always be verified against the adopted codes and official amendments, but when used consciously, AI can reduce hours of manual research to minutes, allowing engineers to focus more on interpretation and application instead of flipping through documents.
Creating Institutional Memory Through Project Metadata: Another useful application of AI is building internal knowledge bases from past projects. This makes it easier for teams to find relevant design examples, see how certain conditions were handled previously, and learn from prior outcomes. Over time, this creates a shared tool across the design team that improves consistency and reduces the need to reinvent solutions on every job. Designers can quickly locate relevant information without relying on ad hoc file searches in the company archives.
Understanding Project Costs Earlier in the Design Process: AI can also help teams understand system costs earlier in design. Tools that estimate material costs can give designers and engineers early visibility into how design choices affect an entire project.
With cost feedback, designers can more carefully aim for, and often beat, material estimates used when bidding a job. Teams can spot unusually expensive layouts, over-designed sections, or opportunities to simplify the system while it is still being developed. Over time, this also builds stronger institutional knowledge, improving the accuracy of material bids on future projects and reducing pricing risk across the business
Managing Material Submittals and Product Documentation: Material submittals remain an administrative effort in each sprinkler design project. Each job requires gathering product data sheets, compliance documents, and organized submittal packages that match the approved equipment schedule.
AI-assisted tools like Caident’s Submittal Generator can automate much of this work—mapping selected components to manufacturer documents, organizing files into AHJ-ready formats, and generating structured submittal packages. The result is less manual effort and fewer instances of missing or mismatched documentation.
Accelerating Training and Consistency for New Designers: AI can also improve training and consistency for new designers by embedding firm-specific standards directly into daily workflows.
As designers work, these tools can flag missing items, non-conforming layouts, and documentation issues before drawings reach senior review. This shifts routine corrections earlier in the process and reduces rework. It also allows senior engineers to spend more time on high-value technical review instead of repetitive formatting checks.
DEFINING THE BOUNDARY BETWEEN AI ASSISTANCE AND ACCOUNTABILITY
As AI becomes more common in sprinkler design workflows, developing clear limits is essential. AI should not replace sprinkler designers or fire protection engineers. Judgment, interpretation, and decision-making must remain with the designer or engineer of record. Accountability for code interpretation, design decisions, and technical risk continues to rest with qualified professionals.
However, within this structure, AI presents significant value—accelerating code research, improving cost visibility, streamlining submittals, and supporting training—all while reducing repetitive manual work and improving consistency across the team
AN EVOLVING DESIGN LANDSCAPE
Over time, AI is likely to become background infrastructure in sprinkler design, much like CAD and BIM did in earlier decades. As these tools mature, their value will be measured less by novelty and more by how well they support disciplined design processes under growing industry pressure.
The future of fire sprinkler design will be defined by engineering judgment paired with AI that strengthens daily workflows.
ABOUT THE AUTHOR: Daniel Price, P.E., is a licensed fire protection engineer and technology entrepreneur with 15-plus years of experience in leading engineering teams, founding and scaling companies, VC investing, and delivering innovative fire protection solutions. He is the managing member at Caident, leading an innovative technology company applying AI to fire sprinkler design, developing tools that help design teams work faster and smarter. At Engineered Fire Systems, Price oversees residential and commercial fire protection design and holds NICET certifications in water-based system design and fire alarm design. Price is a Rhodes Scholar with dual degrees from Oxford University (MBA and Engineering MS) and a summa cum laude graduate of UC Berkeley (two Engineering BS degrees and Physics Minor).