Why asset intel

 Asset Intel exists to solve a common problem in facilities maintenance: survey outputs that look fine on paper but fail when they hit live operations, compliance reporting, or CAFM uploads. We combine nearly two decades of M&E surveying experience with a quality-controlled delivery approach to produce data you can trust and actually use. 

 Many surveys fall down at the point of import: inconsistent naming, missing attributes, poor hierarchy placement, and datasets that don’t match real CAFM templates. We work backwards from your end-use requirements—your CAFM fields, coding rules, hierarchy structure, and reporting outputs—so the dataset is load-ready and aligned to how your teams operate day-to-day. The focus is practical usability: maintenance planning, compliance oversight, lifecycle forecasting, and credible decision-making.

Good asset data is not just “captured”—it’s governed. We create and apply clear rules around hierarchy structure, naming conventions, coding, and attribute population so your estate remains consistent across buildings, surveyors, and future survey phases. This is especially important when multiple stakeholders touch the data over time. The outcome is a structure that scales, stays auditable, and supports operational reporting without constant rework.

 Survey quality isn’t subjective when you have a defined standard, quality checks, and corrective actions. Our approach is quality-controlled: we apply consistency checks, validation sampling, and clear acceptance criteria so that issues are identified early and resolved properly. That means fewer surprises at handover, fewer disputes, and a dataset that stands up to scrutiny when used to justify budgets, backlog positions, or compliance actions. 

 If another supplier has completed survey work, you don’t want to discover the gaps after you’ve started uploading or basing investment decisions on the data. We provide independent QA audits to identify inconsistencies, missing assets, unreliable condition grading, duplicated records, incorrect hierarchy placement, and weak evidence. You receive a clear, actionable output: what’s acceptable, what needs correction, what requires re-survey, and the most efficient route to reach a dependable dataset. 

 Surveys are rarely conducted in perfect conditions. We have experience working in operationally complex environments where access planning, stakeholder management, safety, and disruption control matter. That means realistic survey programming, sensible assumptions, transparent limitations, and professional conduct around live operations—so you get progress without compromising safety or disrupting critical functions.

 You’ll always know what we’re doing, why we’re doing it, and what “good” looks like. We define scope, standards, outputs, QA rules, and assumptions early—then deliver against them. If something isn’t achievable due to access constraints, data limitations, or site realities, we document it clearly and provide pragmatic options. That transparency protects you.