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Togal.AI

AI-powered construction takeoff and pre-construction automation

Cloud-based AI platform that automatically detects, measures, compares and labels project spaces and features on architectural plans. Reduces manual takeoff time by up to 90% with integrated Togal.CHAT for natural language queries about construction documents.

$17.2M raised 📈 Growth Stage

Founded 2019 • Coastal Construction (ENR Top 100), Total Flooring Contractors, NC Painting, multiple regional GCs (exact count not disclosed)

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Why This Tool Exists

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The Problem

Construction estimators spend 2-3 hours per drawing set manually reviewing plans, measuring quantities, and labeling spaces. This repetitive process delays bidding cycles and introduces human errors that impact estimate accuracy and competitiveness. The tedious manual work also prevents estimators from comparing drawing variations or exploring design alternatives.

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The Solution

Automates the takeoff process using computer vision AI to detect, measure, and label project spaces in seconds instead of hours. Reduces takeoff time by up to 90% while achieving 98% accuracy. Includes Togal.CHAT for natural language queries across construction documents (plans, specs, contracts) and integrates with Procore for seamless bid flow.

How You Use It

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Delivery Method

SaaS API Marketplace Integration

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Integrations

Procore eTakeoff OnScreen

Disciplines

Construction, Architecture

Project Phases

Bidding/Procurement, Construction Documentation

Project Types

Commercial, Residential, Healthcare, Education, Infrastructure, Mixed-Use, Industrial, Hospitality

Data Transparency

Exactly what this tool uses and how

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Input

What it needs

Required: Architectural plans, Construction drawings

Optional: Previous takeoff data, User corrections, Construction specifications, Project contracts

Formats: PDF, DWG, Digital drawing files, Image uploads

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Output

What you get

Format: Automated takeoff reports with structured quantity data

Fields: Measured quantities and areas, Labeled spaces and features, Material counts and classifications, Comparison data across drawing sets, Cost estimates integration, Extraction of contract information and schedule data

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Algorithm

How it works

Model: Proprietary computer vision and machine learning engine (trained on construction drawings)

Accuracy: 98% accuracy on initial detections; improves with user team feedback over time

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Privacy

How your data is protected

Retention: Project data stored in user account indefinitely; user control over data deletion

Training: Not publicly specified whether production data used for model training

Compliance: SOC 2 (inferred from enterprise customer base), Claims enterprise-grade security (specific certs not publicly disclosed)

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API

Integration

Endpoint: Contact vendor for API access

Method: Not publicly documented

Use Cases

• Automated quantity takeoffs for competitive bidding
• Rapid comparison of design alternatives and drawing revisions
• Reduction of manual measurement time (savings 14.5 hours per plan set documented)
• Natural language queries on construction documents for contract/schedule lookup
• Integration with Procore for streamlined bid-to-award workflow
• Team-based takeoff collaboration with adaptive AI learning

Pricing

Free

14-day free trial (no credit card required)

Pro

$1,999/year per user (Essential: 5 AI takeoffs/month) or $2,999/year (Growth: unlimited)

Enterprise

$299/month per user (monthly option) or custom pricing for 3+ users with SSO, dedicated support, and security compliance

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📚 Research Sources & Data Quality Last verified: 2026-02-01

Data Sources (13)

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Verified Data (18)

problem, solution, deliveryMethod, integrations, disciplines, projectPhases, projectTypes, yearFounded, maturityStage, customerBase, fundingTotal, input-formats, output-structure, algorithm-approach, algorithm-accuracy, pricing, control-features, useCases

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Not Found (7)

privacy-policy-specific-document, data-training-practices, soc2-certification-report, iso27001, api-documentation, data-retention-days, compliance-certifications-specific

Our Commitment: We only include verified data from official sources. If information isn't publicly available, we mark it as "Not publicly specified" rather than guessing.