Case Study: AI Quantity Takeoff

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Case Study: AI Quantity Takeoff Desktop Application by Akoode Technologies

Automating construction estimation with privacy-first, offline AI

Client Overview

The client operates in the construction and estimation domain, where speed, accuracy, and data confidentiality are critical to project success. With large engineering drawings and complex blueprints forming the core of their workflow, manual quantity takeoff had become a bottleneck, slowing teams down and increasing the risk of costly errors. They partnered with Akoode Technologies to build a robust AI-powered solution that could modernize estimation workflows while keeping sensitive project data fully secure and under their control.

How Akoode Technologies transformed a manual estimation process into an AI-driven product

Case Study - AI Quantity Takeoff

How Akoode Technologies transformed a manual estimation process into an AI-driven product

Client's Requirement

The client needed a production-ready AI application that could integrate seamlessly into existing estimation workflows without relying on cloud infrastructure.

A desktop-based solution compatible with Windows, macOS, and Linux.
Automated material and fixture detection from architectural and engineering drawings.
Ability to process large, high-resolution PDFs without loss of detail.
Offline AI processing to ensure complete data privacy.
Interactive controls for manual review, adjustments, and pricing.
Professional exportable reports for downstream cost estimation.

Our Process

Our Approach to Every Project

Step 1

Problem Analysis & Feasibility

We studied real estimation workflows to understand how estimators interact with drawings, where time is lost, and where errors occur. This shaped both the AI pipeline and the user experience.

Step 2

AI & System Design

We designed an offline-first architecture combining a desktop UI with a local AI engine capable of handling massive blueprint files without cloud dependency.

Step 3

Model Integration & Optimization

Object detection models were trained and optimized to accurately identify architectural elements, even in dense and complex layouts.

Step 4

Deployment & Validation

The final application was packaged with all dependencies, tested across platforms, and delivered with a secure offline activation system.

Step 1

Problem Analysis & Feasibility

We studied real estimation workflows to understand how estimators interact with drawings, where time is lost, and where errors occur. This shaped both the AI pipeline and the user experience.

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Challenges We Faced

Every project comes with unique hurdles, and this one was no different. While building a custom WordPress solution, we encountered a few key challenges that required innovative thinking, technical expertise, and close collaboration with the client to overcome.

Technical Complexity

Processing large-scale drawings while maintaining detection accuracy required careful optimization and intelligent image handling.

AI Accuracy at Scale

Detecting small objects across high-resolution blueprints demanded advanced slicing and inference strategies.

Security & Offline Constraints

Ensuring zero cloud dependency while maintaining performance and licensing control added an additional layer of complexity.

Technical Complexity

Processing large-scale drawings while maintaining detection accuracy required careful optimization and intelligent image handling.

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Delivered Solution

Akoode Technologies delivered a fully offline, AI-powered desktop application that automates quantity takeoff with speed and precision. The solution combines a modern desktop interface with a powerful local AI engine, enabling estimators to process drawings, review detections, adjust counts, and generate cost reports—all without exposing sensitive data. Designed for real-world use, the platform runs efficiently on standard office machines and scales with growing project demands.

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