Before any tower construction could begin, the client’s pre-construction milestones—such as permitting, land acquisition, and engineering approvals—frequently stalled without visibility. While dashboards tracked what was delayed, no one could explain why.
Field managers resorted to email chains and spreadsheets to resolve bottlenecks, leading to reactive decisions, missed deadlines, and poor contractor coordination. The company needed an AI-driven system to anticipate delays before they cascaded.
Seawolf AI partnered with the client’s internal operations team to prototype a predictive milestone model powered by historical site data, status logs, and regional variables.
Key components included:
Milestone Embeddings: Translated text-based milestone descriptions into vector representations to capture patterns and context across regions.
Delay Prediction Model: Trained a machine learning model using milestone sequencing, weather, contractor history, and approval timelines to estimate risk of delay at each phase.
Explainable AI Layer: Highlighted top 3 contributing factors for each delay prediction (e.g., missing utility clearance, underperforming vendor).
LLM-Powered Cause Explorer: Allowed regional managers to query the system in plain English: “Why are permits delayed in Northern California?”
Interactive Dashboard: Visualized predictions, risk factors, and resolution recommendations within a unified field ops tool.
It was the first time we saw leading indicators of project risk—not just red flags after the fact. The AI didn't just warn us—it told us why, and what to fix.
Senior Director, National Field Operations
Enabled proactive interventions before critical delays occurred
Provided data-backed explanations for stalled progress
Unified cross-functional teams around a shared system of truth
Reduced reliance on tribal knowledge and guesswork in field ops
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