ConstructionAI implementation case study

Construction – Heavy Civil Subcontractor (Sun Belt): Change-Order & Lien-Exposure Automation

AI agents automate change-order intake, cost exposure scoring, GC notifications, and lien-calendar orchestration for a heavy civil subcontractor.

This case study documents a real client implementation. Identifying details may be withheld where confidentiality applies.

Key takeaway

End-to-end AI automation tied field reality to legal calendars and GC systems so change orders became traceable, timely, and financially controlled instead of chat-thread chaos.

The engagement

From operational friction to a connected AI system

01 · Context

Business overview

Industry: heavy civil and infrastructure subs. Company size: 210 employees, projects $2M–$45M. Complexity: bonded jobs, joint-check agreements, multi-GC portals. Systems: Viewpoint, GC-specific web portals, fragmented photo evidence in phones.

02 · Challenge

Core problem

Manual transcription from radios and texts, inconsistent CO numbering, and no single source of truth for what was approved vs. in dispute. Bottlenecks at PM review and legal sign-off. At scale, one missed lien window erased margin on entire jobs.

03 · Approach

Solution

PrimeAxiom implemented a full workflow: voice/SMS capture → structured change requests → AI cost-impact drafts → automated GC notices → calendarized statutory deadlines with owner/legal escalation.

04 · Impact

Results

Missed notice incidents fell 91%; average change-order approval cycle shortened 35%; finance recovered 22 hours/week of paralegal-adjacent tracking; DSO on disputed extras improved 12%.

System design

The full automation system

The implementation connects triggers, AI decisions, business rules, human approvals, and downstream actions in one controlled workflow.

Event-driven automation with AI document understanding for CO PDFs from GCs, voice-to-structure for field reports, exposure scoring (cost + calendar), and agent-driven notifications to GCs and internal counsel prep packs.

Workflow

How the automation runs

1. Trigger: field MMS, email PDF, portal scrape job. 2. AI intake: classify CO vs. RFI; extract amounts, dates, references. 3. Structure: canonical CO object; link to job and subcontract. 4. Decision: rules for bond notice; model ranks urgency. 5. Actions: portal posts where APIs exist; email/fax bridges otherwise; tasks for PM; lien calendar entries. 6. Humans: PM approves dollar exposure; legal reviews high-risk. 7. Loop: outcomes update model features for GC behavior patterns.

Intelligence layer

AI agents and decision logic

CO Ingestion Agent, Exposure Scoring Agent, Notice & Calendar Agent, GC Comms Agent (tone-safe drafts), Legal Prep Agent (packet assembly).

Under the hood

Technical architecture

Node workers, PostgreSQL, Redis queues, object storage for docs, LLM with citation prompts, integration adapters per GC portal pattern.

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