The Design Risk Register: Closing the $1.8 Trillion Gap Between Design and Constructability
Why Tier 1 Builders Are Moving Beyond Clash Detection to Construction Intelligence Management
The construction industry doesn't fail on geometry - it fails on information. Building Information Modeling (BIM) solved the problem of pipes hitting beams, but it failed to address the invisible data errors that bleed profit margins. Bad data and miscommunication accounted for $1.84 trillion in global construction losses, with 14–22% of all rework directly attributed to inaccurate or inaccessible project data.
CIM Build introduces the Design Risk Register - an AI-native audit layer that acts as the spellcheck for construction documentation, catching the discrepancies that geometric tools miss. This technology is transforming how Tier 1 builders manage project risk, ensuring that design intent translates cleanly into construction reality.
Table of contents
  • Foreword
$1.84T
Global Construction Losses
Bad data and miscommunication
14-22
% Rework from Data Errors
Inaccurate or inaccessible information
52%
Project Cost Overruns
From design errors and changes
$1,080
Cost Per RFI
To review and process
Foreword
To the builders, design managers, and developers who build our world:
We founded CIM Build to solve the construction industry’s overrun problem.
We’ve lived the frustration of projects losing momentum not because of major disasters, but because of the invisible data disconnects. The silence in design meetings when a critical question halts progress. The trade that stops for three days while an RFI bounces between consultants. The slow erosion of commercial leverage as every variation turns a straightforward build into a defensive battle over scope.
We realized that while the industry has mastered geometry - stopping pipes from hitting beams - it has failed on logic. The problem isn’t the physical clash; it’s the information clash. It’s the specifications that don't match the drawings, the schedules that contradict the scope, and the compliance risks that hide in plain sight.
We built CIM Build to be the logic engine for construction. Drawing on deep experience in both project management and high-scale AI infrastructure, we created a system that reads, reasons, and validates your documentation with a precision no human team can sustain. We don't just draw lines; we understand them.
Our goal is simple: to give you back your leverage, protect your margins, and let you build the project you designed - without the surprise change orders.
Sincerely,
Jacky Wong, CEO
Kevin Le, Head of Construction
Design complexity is compounding
This has been driven by 5 key forces from factors.
Regulatory
Driven by expanding regulatory and compliance obligations
Market
Rising costs of materials and labour driving late design changes
Architectural
Driven by increasingly complex geometries and advanced material systems
Technological
Driven by the integration of smart building systems, data platforms, and IoT
Environmental
Driven by sustainability targets and performance certification requirements
Complexity now surpasses human coordination capacity.
This has led to major issues across projects.
48%
Rework from Poor Collaboration
Teams operating from different versions and misaligned information
26%
Rework from Scope Gaps

Caused by miscommunication across disciplines
$1M
Typical RFI Processing

On projects with 800+ RFIs before construction
The Design Risk Register
The Design Risk Register is a living, AI-powered ledger that audits design sets against themselves. It stops "drift" - where scope creeps or compliance is lost across hundreds of drawing revisions. Where BIM checks if two objects occupy the same space, CIM checks if two documents say the same thing. Where manual QA/QC reviews 80 sheets per week, AI processes thousands in minutes.
Change Audit
Automatically tracks every modification across all document versions. Identifies where specification updates didn't propagate to corresponding drawing schedules or technical schedules.
Contract Validation
Validates that all design deliverables align with contract requirements, scope of work, and specification standards. Flags mismatches between tender basis and as-designed conditions.
Drift Identification
Catches scope creep and specification divergence before construction. Compares architectural, structural, and services documentation to identify silent contradictions.
Compliance Status
Validates adherence to compliance and project-specific performance criteria. Maps requirements to evidence across all documentation sources.
The Register operates continuously through design development, not as a one-time validation but as an always-on assurance layer. Teams receive real-time feedback as they author documents, not weeks later when manual review eventually catches up. The result: design sets that arrive at construction with trades that work from aligned documentation from day one, and projects that execute the design that was approved - without surprise change orders.
Methodology: How the Design Risk Register Works
Define
Configure "rules of engagement" for AI validation. Project-specific compliance requirements, contract scope, and performance standards establish the audit baseline.
Audit
AI processes 100% of text, symbols, schedules, and annotations across PDFs, models, and specifications. Surface contradictions invisible to manual review. Process thousands of sheets in minutes, not weeks.
Coordinate
Automatically cross-reference architectural vs. structural vs. services documentation. Validate that scope propagates consistently across all authoring platforms and document types.
Protect
Generate "Clean Set" for trades where all documentation aligns. Risk Register categorized by severity: Critical issues demand immediate attention; Major items flagged before construction; Minor comments logged for continuous improvement.

Boundaries of AI
Manual checking limits Design Managers to reviewing <10% of total documentation depth. The Design Risk Register audits 100% of text, symbols, and schedules across all drawing revisions, then automatically cross-references architectural vs. structural vs. services documentation.
Validation Reliability by Task Type
Different validation workflows operate at different confidence levels. Understanding these boundaries ensures appropriate human review is applied where AI reliability is lower.
Text & Attribute Verification (Highly reliable)
Direct comparison of specification text, schedules, annotations, and document metadata to identify contradictions, missing references, and mismatches across disciplines.
Symbol & Object Detection (Medium reliability)
Recognition of drawing symbols, legends, and graphical annotations, with accuracy dependent on scan quality and symbol standardization - outputs flagged for review when confidence falls below threshold.
Spatial Logic & Cross-Referencing (Low reliability)
Inferring relationships between elements across different sheets and disciplines (e.g., penetration schedules vs. structural openings); AI flags probable inconsistencies while engineering judgment determines constructability impact.
Engineering Judgment (Non-automatable)
Design intent interpretation, buildability assessment, cost-benefit trade-offs, and resolution decisions remain the responsibility of qualified design managers - AI surfaces risks, humans decide how to act on them.
Where BIM is not enough
BIM: Geometry Engine
BIM coordinates spatial relationships - ensuring ducts don't intersect beams, that penetrations align with structural openings. It operates in three dimensions: X, Y, and Z.
When a duct runs through a beam, BIM flags it immediately. But when architectural specifications call for acoustic-rated ceiling treatment while services schedules annotate standard bulkheads, BIM is blind. These are informational, not geometric, errors.
CIM: Logic Engine
CIM coordinates semantic relationships - ensuring documents say the same thing, that specifications align with schedules, that standards referenced in one section match evidence in another.
Where BIM operates in 3D space (X, Y, Z), CIM operates in n-dimensional attribute space. It reads text, interprets annotations, validates compliance matrices, and maintains consistency across document topology.
The Collaboration Gap
Current workflows create fragmentation. BIM authoring happens in Revit or ArchiCAD. Document management lives in Procore or Aconex. Specifications authoring runs in separate platforms. No tool connects the dots.
RFI response times exemplify the gap: median wait time is 9.7 days for a single RFI response. Construction waits. Costs accrue. Teams idle. When the answer finally arrives, it's often a PDF markup that now needs to propagate back through BIM models, specification schedules, and cost plans - completing the cycle of delay.
The Design Risk Register's objective: eliminate the need for the RFI in the first place. By ensuring documentation consistency pre-construction, teams never reach the state where questions must be asked.
Visual Analysis
Reading lines, symbols, and annotations embedded in drawing sheets across all disciplines.
Text Recognition
Extracting specifications, notes, schedules, and embedded data from PDFs and structured documents.
Rule Logic
Checking compliance requirements, performance standards, and contract scope against all documentation sources.
The Construction Intelligence Stack
Common Data Environment
Single source of truth for all project information
  • Document management (Procore, Aconex)
  • BIM model hosting (BIM 360, Trimble Connect)
  • Version control and audit trails
BIM Authoring Platforms
Geometric coordination and spatial validation
  • Revit, ArchiCAD, Vectorworks authoring
  • Clash detection and resolution
  • Quantity takeoffs and scheduling
Construction Intelligence Layer
AI-powered audit and assurance
  • Design Risk Register validation
  • Specification coherence checking
  • Compliance status reporting
Construction technology has evolved through distinct phases. The Common Data Environment (CDE) solved document management - centralizing PDFs, models, and correspondence in one platform. BIM authoring engines solved geometric coordination - preventing spatial conflicts before construction. But neither addresses informational consistency: whether all documents tell the same story, whether specifications align with drawings, whether compliance evidence exists across all required sources.
The Construction Intelligence layer wraps around existing systems - reading from CDE, validating against BIM models, and generating risk registers that cut across platform boundaries. Unlike monolithic tools that force migration, this layer enhances existing workflows. Teams continue using Revit for geometry, Procore for documents, and specifications authoring platforms for technical requirements. The AI-layer operates in parallel, ensuring consistency without disrupting productivity.
Case Study: $191M Mixed-Use Development
Project Overview
Developer: Major builder in Australia
Value: $191M AUD
Scope: 18-level mixed-use tower with retail, commercial, and residential components
Challenge: High volume of design changes during late-stage value engineering, introducing scope creep and documentation divergence across three design consultants
7 Critical Structural Issues
Identified before construction via automated specification-cross checking. Structural steel schedules referenced different load assumptions than architectural specifications for 4 separate transfer beam locations.
$107,000 Rework Avoidance in 1 structural package
$107K in preventable rework identified in one structural package - completely automated. Extrapolated across ~40 packages, projected savings potential exceeds $4M.
ROI Before First Pour
License fees amortized across project timeline. Payback period: 11 weeks from deployment to critical issue identification. Project profitability improved through rework avoidance alone.
Key Insight: The project team initially expected geometric clashes to dominate the audit findings. Instead, 100% of flagged items were informational discrepancies: specification drift, annotation mismatches, compliance evidence gaps. BIM coordination had already resolved spatial conflicts. What remained invisible - until AI audit was semantic inconsistency and incorrect documentation.
Conclusion: The Future Is Now
The construction industry stands at an inflection point. For two decades, BIM solved geometric clashes - preventing ducts from intersecting beams, coordinating spatial relationships before concrete pours. But geometry was never the root problem. Information is the root problem. The invisible discrepancies that geometric tools cannot see: specification drift where one document calls for premium-grade treatment while another references standard bulkheads; compliance gaps where updated standards don't propagate to supporting evidence; scope creep where 500 drawing revisions introduce contradictions invisible to manual review.
CIM's Design Risk Register solves what BIM cannot. Where BIM operates in 3D space, CIM operates in n-dimensional attribute space - reading text, interpreting annotations, validating compliance matrices, maintaining consistency across document topology. AI-native validation processes thousands of drawing sheets in minutes, not weeks. Automated cross-referencing validates that architectural specifications align with structural schedules, that services annotations match fire engineering evidence, that contract scope propagates consistently across all authoring platforms.
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