Announcing Paladio AI’s AEC Agent

The Architecture, Engineering, and Construction (AEC) industry runs on documents. Drawings, schedules, specifications, and revisions define scope, cost, and risk across the entire project lifecycle. Yet these documents were never designed for machines.

A single construction drawing set can exceed 100 pages and combine vector floor plans, dense schedules, scanned text, symbols, legends, and cross-referenced specifications. Meaning is distributed across pages and formats—not contained in linear text.

Unlocking this information reliably enables critical downstream workflows:

• Quantity takeoffs for estimating and procurement

• Value engineering (VE) to evaluate cost and scope alternatives

• Conversational analysis to query drawings and specifications directly

Despite recent advances in large language models (LLMs), production-scale construction document understanding remains unsolved.

Understanding construction documents at scale is not a model problem alone — it is a systems problem.

General Approach

The Paladio AEC Agent approaches construction document understanding as a coordinated, agentic workflow, rather than a single extraction task.

Instead of treating a drawing set as unstructured text or images, the system:

• Decomposes documents into semantically meaningful units

• Reasons about the role each unit plays within the overall drawing set

• Applies specialized processing paths accordingly

Multiple agents collaborate to interpret content, validate signals, and reconcile information across pages. This allows the system to construct a consistent, document-level representation from heterogeneous inputs.

This design enables robust handling of large, variable drawing sets while maintaining predictable behavior across accuracy, latency, and cost constraints.

Evaluation Scope & Methodology

Evaluating an agentic system for construction document understanding requires balancing accuracy, latency, and cost across different stages of the construction lifecycle.

The acceptable trade-offs between these factors vary by use case:

• Early design & conceptual evaluation
  Directional insight is often sufficient. Single-digit variance in quantities may be acceptable.

• Detailed estimating & construction execution
  Even small errors in quantities or specifications can propagate into procurement mistakes and downstream risk.

To ensure the Paladio AEC Agent performs reliably across these scenarios, we evaluate along three dimensions:

1. Extraction quality

2. End-to-end execution characteristics

3. Operational predictability

Rather than optimizing a single metric, the evaluation focuses on whether the system maintains stable, bounded behavior under real production constraints, including:

• Large document sizes

• Heterogeneous content

• Repeated re-runs after drawing revisions

The Real Constraints: Accuracy, Latency, and Cost

In production AEC workflows, success is defined by three non-negotiable factors.

Accuracy

Missed quantities or misclassified schedules propagate downstream into pricing, procurement, and bid risk.

Latency

Processing must align with estimating and procurement timelines, including frequent re-runs after drawing revisions.

Cost Predictability

Per-document inference costs must remain bounded across large project volumes.

Many off-the-shelf LLM pipelines are optimized for flexibility or small-scale tasks, but struggle when all three constraints are applied simultaneously.

We also evaluated popular off-the-shelf LLM workflows that process each page independently—either extracting content or performing takeoffs directly.

The observations below reflect internal evaluations conducted on representative construction drawing sets under specific production constraints.
Results may vary depending on document characteristics, system configuration, prompts, tooling, and model versions.
This analysis is not intended as a universal benchmark.

Early Takeaway: Task-Level Performance on a 100-Page Drawing Set

Before diving into detailed metrics, one result consistently surfaced during evaluations.

While generic pipelines may appear faster on isolated subtasks, end-to-end consistency and reconciliation proved to be the primary determinants of production reliability.

For a deeper breakdown across extraction categories, see the measured performance table below.

Benchmarking the Landscape: What We Evaluated

Before developing our AEC Agent, we evaluated multiple model categories and pipeline designs across real construction drawing sets.

Model Categories Evaluated

• Vision-based segmentation models (e.g., SAM)

• Layout-aware parsers (e.g., LayoutParser, Docling)

• General-purpose LLMs via OCR + LLM pipelines

• Hybrid OCR + LLM systems

• Domain-tuned internal models

Test Characteristics

• 50–150 page drawing sets

• Mixed plan-heavy and schedule-heavy documents

• High-density tabular schedules

• Real-world noise (inconsistent formatting, abbreviations, missing legends)

All evaluations were conducted using publicly available models and standard tooling configurations available at the time of testing.

Are Vision-Only and Layout-Only Approaches Enough?

Short answer: They help—but they are not sufficient for production-grade AEC takeoffs.

Key Findings

• Vision-only models reliably identify where content exists, but struggle to determine what that content represents.

• Layout-aware parsers improve text and table extraction, yet break down when:

  • Document structure varies

  • Cross-page reasoning is required

• Both approaches add preprocessing value, but fall short as end-to-end solutions.

What We Observed in Practice

Vision-Based Segmentation Models

• Accurately isolate tables, drawings, and annotations

• Lack semantic understanding to differentiate schedules from notes or legends

• Require extensive downstream logic to generate construction-ready quantities

Layout-Aware Parsers

• Improve separation of text blocks and tables over raw OCR

• Become brittle when layouts shift slightly

• Do not capture drawing semantics or reconcile information across pages

In isolation, these approaches perform well on individual pages. At scale, their limitations compound.

How Far Do Generic OCR + LLM Pipelines Go?

High-level takeaway: Generic OCR + LLM pipelines perform well on small subsets, but struggle to maintain consistency, predictability, and cost control at full document scale.

Key Findings

• Strong semantic reasoning on isolated pages

• Degrading performance as document size increases

• Rising latency and highly variable costs in production workloads

What Happens at Scale

As drawing sets grow larger and more complex, we consistently observed:

• Context fragmentation from text-based chunking

• Inconsistent outputs across independently processed pages

• Table truncation due to token limits

• Increased latency from parallel LLM calls

• Cost variability driven by retries and document size

Most critically, schedules, plans, and notes were processed using identical logic, leading to ambiguity during extraction and aggregation.

Why Generic Chunking Pipelines Break Down at Scale

Generic OCR + LLM pipelines fail at scale due to structural limitations:

• Layout is lost early
  Text chunking discards visual cues required for interpreting tables, symbols, and drawings.

• Page intent is ignored
  Schedules, plans, and notes are processed uniformly despite serving different roles.

• No cross-page reconciliation
  Related information is never aligned across pages.

• Retries compound cost and latency
  Partial failures scale poorly with document size.

From PDF to Takeoff: System-Level Architecture

The AEC Agent is designed around a single objective:

Input: Construction PDF
Output: Construction-ready quantity takeoff

To achieve this reliably, the system performs:

Intent-Aware Parsing

Pages are analyzed and routed based on relevance to takeoff workflows.

Structured Extraction

Tables, annotations, and symbols are processed using layout-preserving logic.

Document-Level Reconciliation

Quantities are aggregated and validated across the full drawing set.

Isolated Failure Handling

Errors are contained at the page level instead of cascading across the document.

These implementation details matter because they directly impact accuracy, latency predictability, and cost control.

Measured Performance (Internal Evaluations)

Results reflect internal evaluations under specific constraints and configurations.

Why End-to-End Latency Matters in Construction

In real construction workflows, takeoffs sit on the critical path between drawings and bids.

Drawing sets frequently undergo revisions, requiring re-runs under time pressure. In this environment, end-to-end document latency matters more than isolated model inference speed.

Developer Perspective: Reliability at Scale

From a systems standpoint, production pipelines must be:

• Predictable in runtime
  Workflows depend on consistent turnaround—not best-case inference speed.

• Stable under document variability
  Pipelines must handle mixed content, layout changes, and missing legends without cascading failures.

• Cost-controlled across volume
  Latency spikes often correlate with retries and token overuse, driving operational cost.

Contractor Perspective: Time-to-Takeoff

For contractors, latency directly impacts competitiveness:

• Delayed quantities slow pricing and subcontractor outreach

• Rework compounds time loss across bid cycles

• Reduced responsiveness limits adaptation to late or revised drawings

Reliable end-to-end turnaround is more valuable than fast but inconsistent page-level processing.

See the AEC Agent in Action

Construction document understanding only matters if it works under real production constraints.

The Paladio AEC Agent is designed to:

• Process full drawing sets

• Generate construction-ready outputs

• Operate predictably at scale

At Paladio, our agentic AI team is focused on extending human capability in building the world—by enabling fast, accurate understanding of complex construction documents.