Takeoff Tool Discovery for Stevens Industries

4-week feasibility study that proved AI could read architectural drawings - and produced the technical plan that took the project to prototype.

The client

Stevens Industries is the largest manufacturer of commercial casework and architectural millwork in the United States.

Despite having a high-tech factory with millions of dollars in equipment, a small team still spent hundreds of hours manually reviewing architectural drawings, identifying project requirements, and creating quotes. This process was slow and inefficient, creating a big opportunity for improvement.

The Business Goal

Stevens wanted to automate their manual estimation process to reduce project estimation time by at least 20%.
Minimize human error in processing complex architectural blueprints.
Improve scalability by freeing up their team’s time for higher-value tasks.

The Discovery Goal

Understand the problem in depth by identifying challenges, defining requirements, and reviewing existing processes.
Explore whether an existing AI solution can help solve the problem before developing a custom approach.
Plan the Proof of Concept (PoC), including tool selection, implementation strategy, and evaluation criteria

Discovery Phase Outcome

The discovery phase confirmed that machine learning could be used to automate the estimation process.

Researched available datasets and existing Large Language Models.
Selected a suitable LLM for further development.
Conducted a 3-hour session with the client’s team to analyze their manual workflow, identifying time-consuming tasks and inefficiencies.
Used these insights to create a structured plan for the Proof of Concept (PoC).

Discovery Phase: 3 Key Stages

The Discovery phase focused on validating the technical feasibility and defining a clear implementation plan. It began with in-depth research led by COXIT team, to assess whether machine learning could effectively solve the problem

We needed them to take an existing, manual process and design software automation to help replicate the current business process at a high speed and level of error proofing. Scope involved

Manipulating large PDF based files
Deploying a custom model library for object detection and recognition
Backend data architecture to manipulate and reconnect data to drive value back to the end user

Stage 01

Technical Feasibility Study

Iryna (LinkedIn) , the founder and engineer behind COXIT, led deep research and testing to ensure the project was viable.

Iryna consulted with ML experts to assess whether the problem could be solved and how they would approach it. After confirming feasibility, an ML tech lead was brought in to research existing models and datasets.

The team defined how data should be annotated, planned the Proof of Concept (PoC), and identified the necessary team for implementation.

They have done a great job of communicating both the high-level details (timeline, budget, and milestones) as well as the technical details (next sprint topics, current roadblocks, creative solutions).

stage 02

Onboarding & Problem Understanding

When the project was deemed viable, COXIT’s team kicked it off with a 3-hour work session alongside Stevens' experts to understand every nuance of the quote generation workflow.

This helped map out the full workflow and pinpoint key inefficiencies in the manual estimation process.

These documents outline the complete manual estimation workflow used by Stevens, detailing terminology, document markup procedures, key challenges, and the roles involved.

Stage 03

Next Steps: Detection model feasibility tests

Going ahead, instead of building a full system prototype, COXIT ran targeted AI feasibility tests. They segmented blueprints into smaller parts to reduce complexity and improve precision.

The key goal of the discovery phase was to understand how to approach a complex task that no off-the-shelf solution could solve. In situations like this, the research is an essential step to avoid wasting development hours on ineffective approaches.

What is detection model?

Unlike general AI models like GPT, this is a specialized deep learning model trained by the team over the past two years. It has been continuously refined and retrained to detect and classify objects with high accuracy based on project-specific needs.

Deliverables:

A clear project mindmap, technical architecture, data processing plan, timeline, and budget — all outlining the scope, approach, and resources for next steps.

What is detection model?

[1]

Mindmap of the project

A visual overview of the project's structure, key workflows, and core components.

[2]

Technical architecture

A detailed plan of how the system will be built, covering infrastructure, data flow, and integrations.

[3]

Processing proposal

A step-by-step guide on how data will be handled, from annotation to processing, to ensure accuracy and efficiency.

[4]

Project timeline

A roadmap outlining key milestones, phases, and deadlines to keep the project on track.

[5]

Budget

A breakdown of costs, resources, and investments needed to successfully complete the project.

Receive a sample of deliverables

Just drop your email to get the docs

part 02

POC stage and weeding out the mistakes

Let's collaborate

Tell us a bit about your project or challenge, and we'll get back to you shortly.

Volodymyr Hresko Co-Founder & COO

2026 Wood Industry 40 Under 40 Honoree Woodworking Network

Reach out directly

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