ConstructionAI implementation case study

Construction – Multi-Location GC (Southeast U.S.): Estimating-to-Field Dispatch AI System

Full AI automation for a multi-location construction company: intake, estimating handoff, crew dispatch, and job-site reporting in one orchestrated system.

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

Key takeaway

PrimeAxiom replaced fragmented manual coordination with a single AI-orchestrated pipeline from lead packet to dispatched crew, cutting errors and calendar slack across multiple Southeastern states.

The engagement

From operational friction to a connected AI system

01 · Context

Business overview

Industry: commercial and residential GC work. Company size: 340 employees across 6 branches. Operational complexity: shared estimators, union and non-union crews, bonded municipal jobs, and mixed ERP maturity. Existing systems: Salesforce Opportunities, Procore projects, legacy Excel macros, and SMS threads outside audit trails.

02 · Challenge

Core problem

Manual processes: coordinators triaged inbound leads from five channels, re-keyed scope into templates, and chased photos and site notes in chat. Bottlenecks appeared at estimating handoff (missing measurements), dispatch (who was actually free), and payroll (hours didn’t match dispatched tasks). At scale, duplicate bids, missed callbacks, and Friday overtime spikes broke margin assumptions.

03 · Approach

Solution

PrimeAxiom deployed an end-to-end automation layer: AI intake from unstructured requests, structured job packets for estimating, rule+model dispatch recommendations, and closed-loop field confirmations feeding ERP and payroll prep.

04 · Impact

Results

Efficiency: 62% reduction in manual coordinator steps per active job. Conversions: win rate on qualified bids +18% (faster response). Manual work: 380+ hours/month saved org-wide. Revenue: margin on rush overtime down 2.1 points; recognized revenue timing improved with fewer stalled starts.

System design

The full automation system

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

PrimeAxiom built a unified automation fabric—not a chatbot. Inbound triggers create a canonical Job Intent Record (JIR) with extracted scope, geo, and risk flags. Estimating receives structured packets plus AI-summarized site constraints. Dispatch uses an Operations Agent that reads crew skills, drive-time, and contract SLAs, proposing slots; humans approve exceptions only. Field techs confirm via mobile; hours and tasks sync to Procore and payroll staging. Reporting rolls up margin risk and backlog daily.

Workflow

How the automation runs

1. Trigger: web form, email, or voicemail transcription → Intake Worker. 2. AI intake: LLM extracts trades, timeline, bond requirements; validates service territory. 3. Data structuring: JIR written to PostgreSQL; attachments OCR’d to searchable fields. 4. Decision logic: rules engine scores job size; model suggests estimator assignment; compliance checks for municipal paperwork. 5. Actions: CRM stage updates; estimating tasks with deadlines; SMS to site super if walkthrough needed; calendar holds for bid review. 6. Human interaction: estimators adjust pricing; ops managers override dispatch in edge cases (logged). 7. Feedback loop: actual vs. estimated hours feed retraining features for dispatch model; win/loss reasons improve intake prompts.

Intelligence layer

AI agents and decision logic

Lead Intake Agent: normalizes multi-channel submissions, resolves duplicates, flags incomplete scopes. Estimating Liaison Agent: packages drawings and notes, drafts RFI lists for PMs. Dispatch & Routing Agent: optimizes crew assignment with constraint solving + LLM explanations for overrides. Field Confirmation Agent: parses SMS replies, updates status, opens incidents when SLAs slip. Reporting Agent: generates daily ops briefs for executives.

Under the hood

Technical architecture

Frontend: React admin for exception queues and dispatch board. Backend: Node.js services on Cloud Run with event bus (Pub/Sub). Database: PostgreSQL (jobs, agents, audit). Integrations: Salesforce REST, Procore webhooks, Twilio SMS, Gmail API, DocuSign for subs. AI layer: OpenAI for extraction/summarization; smaller classifier for intake triage; deterministic rules in OPA-style policy JSON. Observability: structured logs + trace IDs per JIR.

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