DAQS is not limited to specific platforms. The output is structured data — formatted to the receiving system's specification. Whether that's a structured file, a direct API connection, or a feed into a data platform depends on what the system expects. Integration is defined during onboarding based on your requirements.
Solutions — Model data for operational systems
Your operational
systems are
only as good as what
feeds them.
Every system that runs on construction data — whether it plans, orders, automates, or decides — depends on the model being correct. When that data is incomplete or inconsistent, every connected system inherits the problem. DAQS validates model data at the source, so what reaches your systems is something they can actually use.
The problem
Operational systems are connected to the model. The data quality problem is not.
Teams invest heavily in operational systems — planning tools, ERP, AI platforms, digital twins. The assumption is that the input data is reliable. For construction model data, that assumption is rarely justified.
⊘
Incomplete data at the point of use
Fields left empty, properties never filled in, naming conventions ignored. By the time model data reaches an operational system, it's often missing the exact attributes that system needs to function correctly.
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Inconsistency across models and projects
Each project, each engineer, each subcontractor produces data structured differently. Operational systems built to work with consistent input have to handle variation instead — through cleaning logic that's never complete and always growing.
⧗
No visibility until it breaks downstream
Data quality issues in models are invisible until they surface as errors in the systems that consume them. By then, the source is remote, the cause is unclear, and the fix requires going back to the model — and through the entire chain again.
The DAQS approach
Fix the input. Everything downstream improves.
The data quality problem in construction isn't a system problem — it's a source problem. The model is where the data originates. That's where it needs to be governed.
DAQS puts validation in Revit, applied to every engineer's work-in-progress model, before anything is exported. Requirements are defined once — by the BIM coordinator, the system owner, or both — and enforced automatically across every model on every project.
The system doesn't get better data because you cleaned it on the way in. It gets better data because it was governed before it left the model.
What reaches your operational system has already been checked against its requirements. The transformation from model data to system input is governed end to end — not patched at the point of ingestion.
Requirements defined upstream
What your system needs from the model is translated into validation rules. Those rules run in Revit — on every model, by every engineer — before any export happens.
→
Data validated at the source
Engineers see where their model data doesn't meet the requirement. They fix it in context, in Revit, before it moves downstream. The issue never propagates.
→
Structured to each system's specification
Validated data is transformed into the exact format each connected system requires. One model, multiple outputs — each mapped to a different downstream specification.
→
Delivered directly
Output goes directly to the receiving system — no manual re-entry, no intermediate files, no handoff that introduces new variation.
Where it appliesPlanning & scheduling ERP & finance AI & machine learning Digital twin & construction OS
Any system that depends on model data.
The validation and structuring layer DAQS provides is not specific to one system type. The same governed data feed applies wherever model data needs to be reliable enough to act on.
Quantities and element data that reflect what's actually in the model
Planning tools that draw on BIM data for quantities, material take-offs, or dependency sequencing need that data to be complete and consistent. DAQS ensures the attributes your planning system reads have been validated before export — so the plan reflects the model, not a partially filled version of it.
Structured cost and quantity data without manual extraction
ERP systems that receive model data for cost planning, procurement, or project control depend on consistent, structured input. DAQS transforms validated element data into the format your ERP expects — directly, without a manual step in between that introduces errors or delays.
Consistent, governed data for training and inference
AI systems are only as reliable as the data they run on. Inconsistent model data — different naming conventions, missing attributes, varying structure across projects — produces unreliable results. DAQS governs the input so that what enters the AI pipeline is consistent and complete, both for training and for production use.
A verified as-designed state from day one
Digital twins and construction operating systems need a reliable, continuous feed of model data across all projects and disciplines. DAQS provides that feed with governance enforced upstream — so the twin or the platform starts from verified data, not from whatever state the model happened to be in at handover.
The shift
From patching on ingestion to governing at the source
The operational system doesn't change. What changes is the reliability of what it receives — and where the responsibility for that reliability sits.
Before
Data quality is handled in the ingestion pipeline. Cleaning logic grows with every new project and every new edge case. The root cause is never addressed.
↓
After
Data is governed at the source. What reaches the ingestion pipeline is already valid. The pipeline processes rather than repairs.
Before
System reliability depends on how carefully each engineer filled in the model. That's invisible from outside the modelling environment and impossible to enforce.
↓
After
Requirements are enforced at the source — in Revit, from IFC, or from other model formats. Compliance is measurable. System reliability is built into the process, not dependent on individual behaviour.
Before
Each connected system has its own data requirements. Managing consistency across all of them means maintaining separate cleaning and mapping logic indefinitely.
↓
After
Each system's specification is defined once in DAQS and applied upstream. Adding a new system means defining its requirements — not building new cleaning infrastructure.
The scale of the problem
What unreliable model data costs across the industry
The cost of bad construction data doesn't sit in one place. It compounds through every system, every decision, and every project that depends on it.
95%
of construction data is captured but never used — not because it isn't needed, but because it can't be trusted
41%
of business decisions in construction are negatively affected by bad or missing data
€1.58T
estimated global cost of bad construction data annually — rework, waste, and failed decisions
Frequently asked
Questions about model data for operational systems
Can DAQS connect to any system, or only specific platforms?
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Who defines what the system requires from the model?
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Typically a combination of the system owner and the BIM coordinator. The system owner defines what data the system needs — fields, formats, values. DAQS translates that into validation rules applied upstream in Revit. The BIM coordinator oversees compliance. In practice, DAQS works through this definition process with you at the start of an integration.
What if different projects or contractors produce models in different ways?
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That's exactly the problem DAQS is built to solve. Governance rules are defined once and applied consistently — regardless of which contractor produced the model or how their Revit environment is set up. Variation in the authoring environment doesn't propagate to the output. Your system receives consistent data across all connected projects.
Does this require changes to existing operational systems?
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No. DAQS maps to the format your system already expects. The system receives what it's built to handle — the change is upstream, in how that data is produced and validated, not in how the system consumes it.
Can the same model data feed multiple systems simultaneously?
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Yes. A single validated model can feed multiple downstream outputs, each structured to a different system's specification. Adding a new connected system means defining its requirements in DAQS — not creating new extraction or transformation logic from scratch for each project.
Let's talk about what your systems need from the model.
We'll map your current data flow — where it originates, what it needs to become, and where it breaks down today.