This study investigates the integration of error budgeting frameworks and advanced cost estimation methodologies within contemporary construction and surveying practices. The convergence of financial Site Reliability Engineering (SRE) principles with traditional construction planning and surveying technologies presents a nuanced landscape that necessitates robust analytical approaches. Error budgeting, traditionally applied in high-reliability engineering contexts, has recently been adapted to financial operations, highlighting the necessity to anticipate, quantify, and mitigate deviations in both project timelines and fiscal allocations (Dasari, 2026). Concurrently, advancements in airborne LiDAR systems and digital terrain modeling have transformed surveying accuracy and project monitoring capabilities (Baltsavias, 1999; El-Sheimy et al., 2005). This paper provides a comprehensive theoretical and empirical analysis of integrating these domains, emphasizing methodological rigor, accuracy calibration, and cost-control mechanisms. Through an extensive literature review, analytical synthesis, and critical discussion, the study examines systemic biases in data acquisition, budget estimation frameworks, and contingency planning processes in complex infrastructure projects. By drawing parallels between construction cost escalation factors, predictive modeling in digital surveying, and financial risk management, this research delineates a holistic model for enhancing project reliability and minimizing unexpected deviations (Shane et al., 2009; Gūnhan & Arditi, 2007). The study contributes to both academic scholarship and professional practice by offering a conceptual framework capable of harmonizing technological precision with fiscal accountability. Implications for future research include the development of hybrid frameworks that leverage real-time data acquisition systems, AI-assisted predictive budgeting, and enhanced financial SRE protocols for high-stakes engineering projects.

error budgeting, financial SRE, construction cost estimation, airborne LiDAR

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