From Digital Building Analysis to Better Decisions: MBH’s Multifaceted QA Approach
- 4 hours ago
- 3 min read
At MBH, our digital building model is at the center of our design process. We use it as a multifunctional environment for analysis—where ideas can be explored, reviewed, and strengthened before they become field conditions. When we can interrogate the building clearly in 3D, we make better decisions in the real world.
Design Technology supports that approach by building the standards, workflows, and review systems that keep the digital building model reliable. The goal is simple: enable stronger collaboration, keep data trustworthy, and make interoperability practical as information moves between people, disciplines, and tools.
We integrate A.I. into project teams to augment review, analysis, and communication, working in tandem with experienced mentors. A.I. delivers the greatest benefit when the underlying data is structured and reliable, because quality inputs lead to quality results.
Model-based analysis changes how we design
Because the model is a shared analytical environment, it has to perform under real project conditions: multiple authors, multiple disciplines, shifting deadlines, and constant change. Without active stewardship, issues like file drag, warnings, clashes, interoperability friction, or subtle data drift can quietly compound. Our job is to keep the model stable and information-rich so it stays useful for design decisions, coordination, and documentation.
Quality is a cadence, not a checkpoint
If review only ramps up at milestones, teams spend time reacting, redoing work, chasing coordination, and shifting decisions. We run a steady cadence of QA so issues surface early, updates stay clear, and collaboration stays productive throughout the project.
Strategy, partnership, and a culture of learning
This work starts with strategy and planning—it’s not accidental. We set expectations, align on standards, and define a review rhythm that matches the project’s complexity. Then we stay in close partnership with design teams, creating space for constructive critique so ideas can improve without slowing momentum.
Underlying it all is a group of people who care deeply about design, and who are curious about new ways to explore. 3D models, XR environments, and analytics don’t replace imagination; they expand it. They help teams see more, test more, and learn faster than they could on drawings alone.
We implement best practices through a people-centered approach that combines layered quality assurance, ongoing mentorship, and robust change management. Our process puts ideas through a rigorous series of steps, including adherence to standards, monitoring model health, coordinating across disciplines, validating data, and reviewing documentation, all supported by immediate assistance and constructive input. By using version control and visual tools to track changes in models and documents, we ensure updates remain clear and maintain seamless interoperability as information flows among team members and platforms.
Here’s what that QA gauntlet looks like in practice:
Model health monitoring: Use dashboards and data analytics to track performance, warnings, and standards compliance so risk is visible early.
Proactive maintenance: Prevent avoidable slowdowns and keep model behavior predictable as the project evolves.
Coordination for collaboration: Surface clashes and spatial conflicts early and drive clear, shared resolution.
XR visual review: Use real-time and immersive XR to evaluate the design across roles and job levels—not just in 2D.
Change management with visibility: Use version control plus change/model/document visualization to communicate deltas and reduce churn.
Data validation and auditing: Verify room/area data, quantities, and schedule consistency—then audit and analyze it so teams get useful information, not just raw data.
Documentation clarity: Maintain graphic standards and annotation quality so sets remain readable and aligned.
Together, these layers keep the digital building model trustworthy and make it a stronger platform for collaboration, mentoring, and A.I.-assisted workflows as projects evolve.
How we review in XR and 3D
We review the work directly in the 3D model. XR reviews help teams evaluate the building holistically, and analytics help teams spot patterns and outliers that are easy to miss in day-to-day production. The result is shared understanding across job levels—and fewer late-stage surprises.
Data auditing and interoperability
The digital building model is also an information system. To stay useful, its data has to be accurate, structured, and transferable—so interoperability works across tools, consultants, and owners. Through data auditing, we turn raw model data into useful information: what changed, what’s missing, what doesn’t reconcile, and what needs attention. That discipline also makes A.I. augmentation more reliable, because quality inputs lead to quality results.
What this delivers
Design Technology shows up in outcomes: smoother collaboration, clearer documentation, and more confident decisions. By putting ideas through a planned QA gauntlet—supported by mentoring, constructive critique, XR visualization, and data analytics—we keep the digital building model reliable. That reliability carries into construction and helps deliver better buildings with fewer surprises.
