Document AI Agent — ai document parsing for aec drawings, PDFs, and DWGs
The Document AI Agent extracts structured data — door and window schedules, mechanical components and dimensions, RFI responses, submittal answers, and plan-archive search results — from AEC drawing PDFs, scanned drawings, DWG files, IFC exports, and contract documents. It is available as a bespoke Labs engagement under MSA + Appendix, scoped to each firm’s archive and workflows.
- Window and door takeoffs extracted directly from drawing PDFs, with quantities and dimensions exported for window and door takeoff from PDF workflows, calibrated per deployment against the firm’s title-block and symbol conventions.
- Plan-archive semantic search across the firm’s drawing and model archive, with sub-second queries once indexed and ingest time calibrated per deployment to the firm’s storage volume and construction plan archive search scope.
- RFI and submittal responses drafted from contract-document and model context, ready for engineer review, with every statement cited back to the specific clause, sheet, or detail callout that supports it.
What the Document AI Agent does.
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Drawing-PDF parsing for takeoff and schedules
Reads architectural, structural, and mechanical drawing PDFs — both vector exports from Revit or AutoCAD and scanned drawings via OCR — and extracts components such as windows, doors, equipment, and fittings with their dimensions, counts, and sheet references. Output lands as structured JSON or CSV that can feed the BOQ Takeoff Agent or a downstream cost tool, and can be reconciled against Revit schedules via the VitruAI + Revit integration.
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Plan-archive semantic search across projects
Indexes the customer’s archive of project drawing sets (PDFs, DWGs, and selected IFC exports) into a searchable corpus tuned for `A-101`, `M-402`, and similar sheet conventions. Practitioners ask questions such as “show stair details from healthcare projects in the last 5 years” and get answers cited to the specific sheet, detail callout, and revision. This underpins firm-wide construction plan archive search and knowledge reuse across offices.
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RFI and submittal drafting from project record
Reads contract documents, specifications, addenda, and drawing markups to draft RFI responses and submittal answers grounded in the issued-for-construction set. The agent proposes language, but the engineer always reviews and signs; every paragraph carries citations to the clause, sheet, or sketch that supports it. Drafts can reference model-based checks from the Dubai Villa Code rule library when paired with the code-compliance workflow.
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DWG and IFC ingestion alongside PDFs
Ingests native AutoCAD DWG and IFC files alongside PDFs so the agent can correlate a takeoff line in a DWG against the architectural intent in a Revit-exported IFC. This helps answer questions like “is this façade mullion line in the DWG the same element as that IFC curtain-wall panel?” and reduces double-counting in BOQ workflows. The same parsing stack can support ai pdf takeoff style use cases across disciplines.
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Archive calibration and parser-pack build
Each Document AI deployment starts with a calibration sprint against the firm’s real documents: title blocks, symbol libraries, typical detail sheets, and contract templates. VitruAI tunes the parser pack to those conventions under MSA + Appendix and documents the extraction schema for takeoff, schedule, and RFI outputs. Once signed off, the pack runs as a steady-state service across new projects and backfile archives, with per-project accuracy reports calibrated per deployment.
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Integration into BIM and QA/QC workflows
Connects parsed outputs into existing BIM and QA/QC workflows, including Revit models, DWG-based detailing, and downstream cost tools. Door and window counts can be compared to Revit schedules through the VitruAI + Revit bridge, while extracted clear-height or setback data can be checked against rule libraries such as Dubai Villa Code. This keeps document parsing aligned with model-based checks and BOQ preparation in a single pipeline.
Document AI Agent — common questions
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What document formats does the Document AI Agent read?
The Document AI Agent reads vector PDFs exported from Revit, AutoCAD, and other CAD tools, as well as scanned drawing PDFs via OCR. It also ingests native DWG, IFC, and common image formats such as JPG and PNG for detail snapshots. Handwritten markup is parsed where legible, but unclear annotations are flagged for manual review instead of guessed.
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How accurate are the extracted takeoffs?
Accuracy depends on drawing quality, symbol consistency, and how standardised the title blocks and schedules are across projects. Each Labs engagement ships a per-project accuracy report calibrated to the customer’s drawing conventions and a ground-truth sample graded by the project team. Uncertain extractions — for example overlapping hatch patterns or partially obscured tags — are flagged for human review rather than silently included or excluded.
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Can it handle our archive of older scanned drawings?
Yes, scanned-PDF OCR is a core part of the Document AI stack, so older plan archives can be brought into the same search and takeoff workflows as newer digital sets. Extraction quality improves with higher scan resolution and clear linework; very faint blueprints or skewed scans may require extra preprocessing. Archive ingest is typically a 1–2-week pre-deployment step in a Labs engagement, during which we profile a sample of projects and tune the OCR pipeline for your archive and target use cases like construction plan archive search.
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Does it draft contract-document responses without engineer review?
No, the agent always drafts and the engineer always reviews and signs before anything leaves the office. Every RFI or submittal draft includes citations to the specific clauses, drawings, and addenda that support the language, so reviewers can check the basis quickly. Where the documents are ambiguous or conflict with model data from VitruAI + Revit, the draft calls out the conflict instead of choosing a side.
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How is this different from generic document-AI tools?
Generic tools treat drawings as generic PDFs, while the Document AI Agent uses AEC-specific schemas for door schedules, sheet indices, detail-callout patterns, and IFC entity hierarchies. It is tuned to questions that matter to project teams, such as reconciling a PDF door schedule with a Revit model or preparing quantities for the BOQ Takeoff Agent. Evaluation rubrics and parser packs are built against real AEC projects, not general web documents, and integrate with rule libraries like Dubai Villa Code when compliance is in scope.
Need this on a real project?
Document AI is co-built per customer document class. The first 2–3 weeks are a calibration sprint against your archive — by week 4 you have a parser pack running on real project documents.
Scope a Labs engagement