Spec Book AI Agent for Stevens Industries

An AI agent that reads 500MB spec books and answers questions in under 30 seconds, with page-level citations. Live in production since 2025.

The client

Stevens Industries is the largest manufacturer of commercial casework and architectural millwork in the United States. Known for excellence in producing high-quality, customized solutions.

The Business Goal

Stevens Industries needed a solution to streamline document-heavy workflows, specifically aimed at efficiently navigating and extracting critical data from extensive PDF specifications.

Employees spent significant time manually processing documents—searching, interpreting, and extracting crucial information, resulting in workflow bottlenecks during bid preparations and material evaluations.

The AI Agent Goal

The objective was to implement an AI-powered system capable of rapidly and accurately extracting information from PDFs, structured using a construction industry standard known as Master Format.

The solution needed to significantly reduce document processing time, increase data extraction accuracy, simplify interaction, and easily adapt to changing project specifications.

The AI Agent was built to quickly surface key information from large PDF files — up to 500MB — by separating specification pages from drawings and focusing only on what matters.

It aimed to reduce the time and errors involved in manual searches, make it simple for users to interact with the system and give feedback, and stay flexible enough to adapt to new document formats and project needs in the future.

Outcome

AI system that speeds up search, improves accuracy, and adapts with user feedback.

Time Savings — Substantially decreased the time users spent searching and extracting key details. A process step that used to take upwards of 30 minutes now takes less than 30 seconds.
Enhanced Accuracy — The AI provided context-based answers, meaning it understood exactly what users were looking for in the documents. This significantly reduced the errors people usually make when manually searching through lengthy PDFs.
Scalability — The AI Agent can easily be updated or expanded. For example, if Stevens Industries starts working with different types of documents or needs to find new kinds of information, the system can be quickly adjusted without requiring major technical changes or costly redevelopment.
User Empowerment — An intuitive interface, transparent traceability of information, export functionality (CSV files) significantly enhanced user satisfaction and allows for automated workflow connections.
Built-in feedback loop — The system evolves through user interaction, reducing maintenance cost and increasing long-term accuracy.

This image shows the AI Agent in action. Users can choose from pre-set questions. The system then finds the answers inside large PDF files. It helps them extract key details without reading the whole document.

Discovery Phase: 3 Key Stages

We broke the project into three clear stages to keep things focused and manageable. Each step helped us move from understanding the problem to building the solution — and planning what’s next

These slides show the planning for the next stage of the AI agent. They highlight features that will make it easier for users to track file history, leave comments, and manage their documents more efficiently.

Stage 01

Problem Analysis & Project Scoping

We began by closely examining Stevens Industries’ document workflows through collaborative workshops. The team analyzed sample PDFs and clearly identified challenges, workflow bottlenecks, and frequent tasks.

We then finalized the project’s objectives, identifying exactly how the AI Agent would solve key business problems.

stage 02

AI Agent Development & Deployment

During this stage, we built the AI Agent’s user interface using React, and developed the backend with Python (FastAPI). We designed and optimized precise prompts that allowed the AI to accurately understand and extract important details from the PDFs.

We also included tools that allowed Stevens Industries’ admins to easily manage and update the types of questions asked and how the AI searches through documents. After testing and validation, we deployed the AI Agent into the client’s workflow.

Stage 03

Next Steps: Detection Model Feasibility Tests

In the upcoming phase, the team will investigate more advanced AI capabilities, particularly the feasibility of automatically identifying relevant document sections without predefined MasterFormat codes.

We will also explore additional AI models for continuous improvements in speed, accuracy, and operational efficiency.

Deliverables:

Custom AI solution with a user-friendly interface, admin control, transparent validation, and full documentation—seamlessly integrated into Stevens Industries’ workflow.

[1]

AI Agent Integration

Operational AI Agent integrated into Stevens Industries’ workflow.

[2]

Interactive UI & Export

User-friendly React-based interface for document interaction and data export (CSV).

[3]

Admin Panel Access

Admin panel allowing easy updates to questions and search parameters.

[4]

Validation with Citations

Transparent validation mechanism (page and paragraph citations).

[5]

Full Project Guide

Complete project documentation, including AI prompt guidelines, usage instructions, and future enhancement roadmap.

[6]

AI Monitoring with LangSmith

LangSmith platform for monitoring AI interactions and collecting user feedback and dataset improvements.

How does the AI agent work?

The AI agent analyzes documents, understands user queries, and returns precise answers with source references — all through a simple interface.

STEP 01

Upload the document you need to process.

STEP 02

Once uploaded, a chat panel appears on the right with a list of predefined questions tailored to the tasks you typically perform.

STEP 03

Select a question, and the AI Agent responds instantly in the chat with relevant information extracted from the PDF.

STEP 04

You can provide feedback on the AI's answer, helping it learn and improve accuracy for future queries.

STEP 05

Once uploaded, a chat panel appears on the right with a list of predefined questions tailored to the tasks you typically perform.

Final Touch

You also have access to conversation history and user feedback, which helps improve response quality over time. With integrations like LangSmith or DeepEval, you can monitor results and easily refine the system — all without needing developer support.

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

[email protected]