Spec Book AI Agent for Stevens Industries

An AI agent that reads spec books and answers estimators’ questions in seconds, with page and paragraph citations.

The client

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

The Business Goal

Stevens Industries needed a way to improve document based workflows, with a particular focus on navigating and extracting information from extensive PDF specifications.

Employees spent considerable time manually searching through documents, interpreting technical specifications, and identifying relevant information. This created workflow bottlenecks, particularly during bid preparation and material evaluation.

Results

30 seconds

to locate the relevant specification section

80%

fewer errors compared with manual document search

What we built

Key features

[1]

Predefined question sets

The estimator uploads a specification book and selects from questions their team regularly asks:

which product series fits the specification?
what core and front construction is required?
what hardware is specified?

[2]

Scope search across divisions

Casework scope is often distributed across multiple sections. The agent finds the relevant information wherever it appears in the specification, including Division 6 millwork, Division 12 manufactured casework, and related Division 5 metal work.

[3]

Fast bid qualification

Before starting a takeoff, the estimator can quickly check what the job requires. Even with a 500MB spec book, the agent highlights key pricing factors, such as concrete countertops or formaldehyde limits for hospitals and schools.

This helps the estimator decide within minutes whether the bid is worth pursuing.

[4]

Hallucination-proof answers

The agent answers only from the document, and each answer links to the exact page and paragraph where the information was found. If the specification book does not contain the required information, the agent states this rather than making an assumption.

The product

How the AI agent works

The AI agent analyzes documents, understands user queries, and returns precise answers with source references.

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.

Upload the document

Upload the document you need to process.

Pick from predefined questions

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

Get an answer from the PDF

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

Rate the answer

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

Manage the questions

Admins edit the list of predefined questions in the AI Instructions panel and choose which ones appear in the chat, without developer support.

Under the hood

Technical challenge

Spec books arrive as 500MB PDFs where drawings and specifications are mixed together. So the first challenge was to separate them and keep only the sections that matter. The hardest part was making answers trustworthy: an agent that guesses is worse than none. So we scoped it to answer only from the document, added page and paragraph citations, and monitor every answer through LangSmith.

[1]

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.

[2]

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.

[3]

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.

What we delivered

Project deliverables

[1]

AI agent integration

The AI agent reads specification PDFs and extracts the details estimators need. It runs inside the existing Stevens Industries process rather than as a separate tool.

[2]

Web interface and CSV export

A browser-based interface where a user uploads a spec, reviews the extracted answers, and exports them as a spreadsheet file (CSV) for use in estimating.

[3]

Admin panel

A settings area where Stevens Industries staff edit the list of questions the agent asks and how it searches the document. Changes take effect without a developer.

[4]

Citation system

Each answer the agent gives includes the page and paragraph of the spec it came from. An estimator can open that spot in the original document and confirm the result.

[5]

Project documentation

A written guide covering how the agent works, how to use it, how its instructions (prompts) are structured, and which improvements are planned next.

[6]

Monitoring platform

A monitoring platform (LangSmith) that logs each interaction with the agent and collects user feedback. The data is used to measure accuracy and improve the agent over time.

Spec Book AI Agent: a spec book open next to the agent's chat panel with predefined questions

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.

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]