Construction & Design-Build

From AI Policy to Custom RFP Chatbot: How Primus Builders Implemented AI Without the Chaos

A design-build construction company moved from zero AI governance to deploying custom chatbots for complex bids. Here's exactly how they did it.

CLIENT
Primus Builders
Design-build construction specializing in cold storage, food processing, and manufacturing facilities
Primus Builders
20+
Years Experience
335k+
Sq Ft Projects
Woodstock, GA
Headquarters
9
AI Implementations

The Challenge: AI Tools Were Already Here

Like most companies in 2024, Primus Builders didn't need to decide whether to adopt AI. Their team was already using it.

The problem? Nobody knew who was using what, for what purpose, or whether they were exposing sensitive bid data to public AI tools.

What This Looked Like in Practice:

  • Project managers copying specs into ChatGPT to summarize documents
  • Estimators using free AI tools to analyze competitor bids
  • No way to track what proprietary information was being shared
  • Leadership hearing about AI benefits but seeing mostly risk

The question wasn't "Should we use AI?" It was "How do we make sure we're using it safely and getting actual business value?"

The Approach: Governance First, Then Implementation

Here's what actually happens when you implement AI in a mid-sized construction company. Not the theory. The reality.

PHASE 1: GOVERNANCE FOUNDATION

Built the Framework Before the Tools

1. Formed an AI Steering Committee

Not just a rubber-stamp group. Real decision-makers from operations, IT, and project management.

What This Meant:
  • • Monthly meetings to evaluate AI use cases
  • • Budget authority to approve pilot projects
  • • Responsibility for risk assessment and mitigation

2. Created Company-Wide AI Usage Policy

Simple rules that people would actually follow. See Company-wide AI Policy for framework details.

The Core Rules:
  • • No proprietary bid data in public AI tools (ever)
  • • Use approved enterprise AI tools for sensitive work
  • • Document any AI-generated content used in client deliverables
  • • When in doubt, ask the steering committee

3. Launched AI Champion Framework

Identified team members already using AI effectively. Trained them to onboard others. See AI Training Programs for implementation approach.

Quick Reality Check:

The best trainers weren't IT staff or consultants. They were project managers and estimators who'd already figured out practical AI applications for their daily work.

Timeline
6-8 weeks
PHASE 2: PROCESS AUTOMATION

Tackled the Most Painful Workflows First

4. Automated AP Invoice Processing with RPA

Construction companies process hundreds of invoices monthly. Manual entry was eating 15-20 hours per week.

The Part Nobody Mentions:

RPA sounds magical until you realize you need to standardize invoice formats from 50+ subcontractors first. That took longer than building the automation.

Before
  • • Manual data entry: 15-20 hrs/week
  • • Error rate: 3-5% requiring rework
  • • Processing delay: 5-7 days
After
  • • Manual review only: 3-4 hrs/week
  • • Error rate: <1% (automation is consistent)
  • • Processing delay: Same-day

Related capability: RPA Platform

5. Built AI Ideas Management Process

Implemented Aha.io to collect, evaluate, and prioritize AI use cases from across the company.

Think of It Like This:

Your team sees inefficiencies every day. Most companies lose those insights. Primus captures them, evaluates feasibility, and implements the winners.

Related capability: Ideas Portal

Timeline
10-12 weeks
PHASE 3: CUSTOM AI APPLICATIONS

Built Solutions for Their Specific Business Problems

6. Custom RFP Chatbot for Complex Bids

When you're bidding large multi-facility projects, you can't have estimators digging through 1,200 pages of specs manually.

What This Actually Did:
  • • Ingested full RFP documents, drawings, and specifications
  • • Answered natural language questions: "What are the insulation requirements for cold storage areas?"
  • • Cited specific page numbers and sections for verification
  • • Identified potential conflicts between specs and drawings
In Human Terms:

Instead of reading 1,200 pages to find specific requirements, estimators asked questions and got instant answers with page references. Cut bid prep time significantly.

Related capability: Custom Chatbots

7. Automated Project Summaries from Procore

Daily manpower logs and delay reports in Procore needed weekly executive summaries. That was 2-3 hours of manual work every Friday.

Automated Process:
  • • Pulls weekly data from Procore API
  • • AI summarizes key patterns and issues
  • • Flags projects with manpower shortages
  • • Identifies recurring delay causes
  • • Generates executive summary report
Business Impact:
  • • Project managers save 2-3 hrs/week
  • • Executives get summaries Monday AM
  • • Caught patterns humans missed
  • • Better resource allocation across jobs

Related capability: Document Intelligence

8. Custom MS Copilot: Specs vs. Drawings Comparison

Construction's biggest nightmare: specifications say one thing, drawings show another. Finding those conflicts manually took days.

How It Works:
  • • AI reads written specifications
  • • Analyzes construction drawings
  • • Identifies discrepancies automatically
  • • Alerts project team before construction starts
Quick Reality Check:

This isn't perfect. AI still flags false positives. But catching 80% of real conflicts before breaking ground saves orders of magnitude more than the time spent reviewing AI alerts.

Related capability: Access to Approved LLM (Copilot)

9. Contract Analysis & Redline Response Copilot

Legal reviews on construction contracts take weeks. Most changes are standard negotiation points. AI handles the routine stuff.

What This Means For You:
  • • AI flags high-risk contract changes for legal review
  • • Suggests responses to standard redlines based on company policy
  • • Compares terms against previous successful contracts
  • • Speeds up routine contract negotiations from weeks to days

Related capability: Document Intelligence

Timeline
16-20 weeks (rolling deployment)

The Results: AI Tools That Actually Get Used

The real measure of AI implementation isn't how many tools you deploy. It's whether your team uses them after the consultants leave.

Here's what Primus Builders achieved:

9
AI Implementations
From governance to custom chatbots to RPA automation
15-20hrs
Weekly Time Saved
AP automation alone recovered 15-20 hours per week in accounting
Zero
Security Incidents
Governance framework prevented data exposure to public AI tools

What Actually Changed:

Estimators prep bids faster with higher confidence
RFP chatbot means no more missed requirements buried in 1,200-page documents
Accounting staff focus on exceptions, not data entry
RPA handles routine invoices, humans handle the weird ones
Project managers catch issues before construction starts
Specs vs. drawings comparison prevents costly field changes
Executives get project insights without waiting for manual reports
Automated Procore summaries show patterns across all active jobs
Contract negotiations move faster
AI handles routine redlines, legal focuses on high-risk terms
The Part That Matters Most:

Six months after implementation, the team is still using these tools. That's rare. Most AI pilots get abandoned when the consultants leave. Primus succeeded because they built governance first, trained their people, and created tools that solve real problems.

Key Takeaways: What You Can Apply to Your Business

1. Start with governance, not tools

Primus didn't deploy AI first and figure out security later. They built the steering committee and usage policy before anyone touched a custom chatbot.

See: Company-wide AI Policy

2. Your best trainers are already on your team

The AI Champions weren't consultants or IT staff. They were project managers and estimators who'd figured out practical applications on their own.

See: AI Training Programs

3. Automate pain points, not possibilities

Every automation Primus built solved a problem people complained about daily. They didn't automate because they could. They automated because manual processes were killing productivity.

See: Ideas Portal for capturing team insights

4. Build for your business, not the demo

The RFP chatbot wasn't impressive because it used cutting-edge AI. It was impressive because it solved a real problem: estimators couldn't manually review 1,200-page bid documents fast enough.

See: Custom Chatbots

5. Perfect is the enemy of deployed

The specs vs. drawings comparison tool isn't perfect. It flags false positives. But catching 80% of real conflicts before construction beats catching 0% while waiting for a perfect solution.

See: Document Intelligence

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