Merit’s Intelligent Drawing Extraction turns complex, inconsistent construction drawings into structured, contextual data that strengthens coordination, reduces rework, and enables truly data-driven project delivery.
Design drawings are the backbone of every construction project, but the intelligence they contain is still difficult to use at scale. Most organisations know the pain: hours spent interpreting design files, reconciling mark-ups, and cross-checking specifications across thousands of DWGs, PDFs, and scanned blueprints. Critical information is there — just not readily accessible, searchable, or verifiable.
Intelligent Drawing Extraction (IDE) is shifting this dynamic, but not all IDE approaches are equal. At Merit, we focus on something most solutions overlook: the ability to interpret multi-discipline drawings as interconnected systems, not isolated sheets. Our IDE frameworks combine computer vision, domain-trained NLP models, and graph-based reasoning to understand components, relationships, and engineering context - the level of interpretation required for real-world design coordination.
The result is a pipeline that turns drawings from static documents into structured, machine-readable intelligence that flows directly into planning, procurement, and execution. Instead of extracting shapes and text alone, Merit’s IDE interprets meaning, enabling faster validation, fewer coordination errors, and a more connected construction lifecycle.
Construction data is rarely clean. Even within a single project, teams work with mixed drawing formats, inconsistent layering conventions, digitised legacy files, and hybrid PDFs that blend vector data with handwritten mark-ups. Revisions accumulate across disciplines, standards vary by consultant, and drawings often lose structure as they move through the design–review–construction chain.
This variability isn’t just inconvenient. It’s the core barrier to automation.
Merit’s IDE pipelines are built specifically for this reality. Instead of assuming neatly layered CAD files, they standardise the full spectrum of project drawings: vector DWGs, scanned PDFs, hybrid sheets, and multi-revision packages. The system normalises geometry, text, symbols, and metadata into a consistent representation that can be searched, compared, and cross-referenced across disciplines.
Where traditional document management systems stop storing files, our IDE frameworks interpret them, aligning versions, reconstructing relationships, and preparing heterogeneous drawings for downstream reasoning and analysis at scale.
The first stage of IDE is ingestion. Not just making drawings readable, but normalising them into a consistent, high-fidelity structure that downstream reasoning models can trust.
At Merit, this stage is powered by a multi-engine ingestion stack purpose-built for construction data. For vector formats, our custom CAD parsers extract layers, blocks, entities, and metadata with high precision, preserving coordinates, scale, units, and relational structure so drawings can be aligned with BIM models or geospatial references.
For scanned or hybrid drawings, we apply image-processing workflows tuned specifically for construction artefacts, reducing noise, correcting distortion, enhancing faint linework, and isolating handwritten annotations. OCR and detection models trained on construction-specific fonts, symbols, and notation then identify text, tags, and component types with higher accuracy than generic models.
A graph-based reconstruction layer connects geometric and textual entities, producing a structured, machine-readable representation. Crucially, this process also supports alignment across revisions and disciplines: the system anchors drawings to consistent coordinate frameworks, allowing changes, conflicts, and additions to be compared reliably over time.
This creates a unified digital foundation, clean, normalised, and fully traceable, that enables deeper analysis and multi-discipline reasoning in later stages.
Recognising text in a drawing is the easy part. Understanding what that text means within an engineering context is the real challenge. This is where Merit’s domain-trained NLP models differentiate our IDE approach.
Construction drawings rely heavily on shorthand, consultant-specific abbreviations, overloaded symbols, and context-dependent labels. A term might carry different meanings across mechanical, electrical, and structural disciplines; a dimension might imply a component type; a tag might map an entire hierarchy in the project specification system.
Merit’s NLP layer is trained on real-world construction datasets across multiple disciplines, enabling it to:
For example:
This contextual linking enables automated cross-verification between design intent, specifications, and downstream planning systems. Instead of simply extracting labels, Merit’s IDE interprets how those labels function within the engineering logic of the project, allowing inconsistencies, omissions, and clashes to surface early and reliably.
Real construction drawings don’t follow clean rules. Inconsistent layer naming, half-scanned legacy sheets, consultant-specific symbols, faint handwriting, and OCR errors all introduce ambiguity that can distort downstream analysis if not managed correctly.
Merit’s IDE framework is engineered for this variability. Instead of relying on broad “human-in-the-loop” claims, we use selective human reviews guided by granular confidence scoring. The system evaluates how certain it is about every extracted element - text labels, geometric entities, symbols, and relationships. When confidence is high, results flow automatically; when the model detects ambiguity, only those specific elements are routed to analysts for quick validation.
These analysts don’t need engineering expertise. They simply confirm whether the model interpreted what is visibly present on the drawing correctly. This keeps oversight lightweight and scalable while ensuring accuracy in the noisy, edge-case scenarios that occur frequently in real project data.
Validated corrections are then fed back into controlled retraining pipelines, enabling the models to continually improve their handling of inconsistent symbols, fuzzy scans, or consultant-specific notations, all without compromising project confidentiality.
This targeted approach balances automation with pragmatic oversight, making the extraction pipeline more resilient to the messy, multi-format reality of construction drawings.
In a mixed-use development project, the structural team issued a revised floor framing plan that increased the depth of a primary beam by 50 mm to address load redistribution. The change was reflected in the structural drawings but had not yet propagated to the MEP layouts.
When Merit’s IDE pipeline processed the updated drawing set, it didn’t just extract dimensions. It cross-linked entities across disciplines. The system matched the new beam depth against previously extracted MEP duct routes and identified a conflict: a major supply duct was now intersecting with the deeper beam along a primary corridor.
The IDE output flagged this as a high-impact clash, highlighting:
Because the conflict surfaced before coordination meetings, the project team adjusted the duct elevation and updated the routing in the mechanical model. This avoided what would have otherwise been late-stage RFIs, redesign time, and potential ceiling height compromises.
This example illustrates how Merit’s IDE goes beyond isolated extraction. It reasons across disciplines, tracks revision deltas, and identifies inconsistencies that typically emerge only during coordination or on-site installation. By connecting structural changes to MEP and architectural dependencies, the system acts as an early-warning engine that improves design completeness and reduces downstream rework.
Extracting data from drawings creates value only when it flows naturally into day-to-day project systems. The real impact of an IDE comes from how well it plugs into the wider construction ecosystem, not just through generic APIs, but through integrations that match how teams work.
Merit’s IDE architecture is built with repeatable, production-grade connector patterns, allowing seamless integration with platforms already core to construction operations, including:
Because these connectors are not one-off scripts but part of a scalable integration layer, teams can operationalise IDE outputs immediately. A detected dimension conflict, for instance, can update the BIM model, generate an RFI in Procore, and trigger a procurement check, keeping design, site, and commercial teams fully aligned.
This level of ecosystem-ready integration moves organisations from document-centric workflows to data-centric execution, improving visibility, reducing rework, and strengthening project accountability.
Construction projects evolve constantly, and an IDE must evolve with them. Merit’s framework is designed for continuous learning, adapting to variations in project types, drawing standards, annotation styles, and symbol libraries without requiring teams to rebuild processes from scratch.
As engineers, estimators, and project managers interact with the system, their feedback is captured and used to refine extraction models. Over time, this builds a library of patterns that helps the IDE recognise nuances such as discipline-specific notations, contractor-specific conventions, or unique detail drawings from different vendors.
To improve performance across projects while keeping every client's data fully protected, Merit supports a privacy-preserving learning layer. Instead of sharing raw drawings or project files, the system aggregates model improvements, so each deployment benefits from broader learnings without exposing any private project data.
The result is an ecosystem where every project makes the IDE smarter, more accurate, and faster to adapt, continuously improving its ability to serve diverse construction environments.
The value of IDE extends well beyond automation or faster document handling. By transforming design documents into structured, machine-readable data, organisations create a more transparent, auditable, and reliable foundation for project decision-making.
Key advantages include:
These benefits don’t just improve efficiency. They strengthen governance. When design data becomes structured, verifiable, and consistently traceable, leaders gain confidence that every decision is rooted in reliable information, reducing ambiguity, and lowering the chances of disputes downstream.
Merit’s strength lies in combining deep construction knowledge with advanced data engineering and applied AI, an intersection few vendors operate in. Our IDE frameworks aren’t generic document-processing tools; they are purpose-built systems designed for the complexity, variability, and interdisciplinary nature of construction and infrastructure projects.
What sets Merit apart:
1. Domain-trained models built specifically for construction
We develop LLMs/SLMs and computer-vision pipelines trained on real architectural, structural, and MEP drawings, not generic datasets. This gives our models the ability to interpret discipline-specific symbols, conventions, sheet structures, and annotation patterns with far greater accuracy.
2. A unified reasoning framework that goes beyond extraction
Merit combines computer vision, language models, and graph-based reasoning to interpret how components relate across drawings, schedules, and specifications. This enables cross-disciplinary checks - structural vs. MEP, equipment loads vs. capacities, layouts vs. code constraints - driving genuine design intelligence, not just data capture.
3. Composable IDE components that fit real project environments
Our architecture is built from modular components - extraction, validation, reconciliation, change tracking, reasoning - that plug directly into Autodesk Construction Cloud, Bentley, Procore, and major ERP ecosystems. These are repeatable, operationalised integrations, not one-off scripts.
4. A data engineering heritage unmatched in the sector
Most AI vendors start with models and add pipelines later. Merit starts with robust data engineering: scalable cloud-native pipelines, lineage tracking, version control, and quality checks that ensure IDE outputs are reliable enough for design, procurement, and audit workflows.
5. Proven success in multi-discipline, high-complexity environments
Our frameworks are deployed across large infrastructure and commercial projects where data comes in dozens of formats, from scanned blueprints to federated BIM models. This experience shapes our ability to handle fragmented, inconsistent, and evolving project information at scale.
By turning drawings into structured, connected, and contextualised data, Merit enables teams to move beyond basic digitisation toward intelligent design understanding, where every drawing becomes a living digital asset that improves coordination, accuracy, and confidence across the project lifecycle.
