Research, product updates, and practical insights from the Arlyn team.
The difference between a copilot that waits for prompts and an agent that acts on events. Why 'chat with your data' tools are failing GC teams, and what autonomous AI actually looks like in a construction workflow.
Analysis of 200 federal projects shows that the average GC loses 14 days of total float in the first 30% of project duration, and 90% of that loss is detectable weeks before it becomes critical.
Today we're launching multi-agent orchestration: the ability for a schedule delay agent to automatically trigger a cost impact agent and a subcontractor notification agent in sequence.
We surveyed 300 ENR-ranked general contractors on AI adoption, ROI measurement, and deployment barriers. Here's what we found, and what it means for where the industry is heading.
Training LLMs on construction contracts, RFIs, and schedules without using customer data. A look at the synthetic data strategy and domain fine-tuning approach behind Arlyn's agent reasoning layer.
The average ENR 400 GC processes 847 change orders per year. 34% are disputed. 12% are never resolved. We analyzed the root causes and designed an AI agent to address each one.
We rebuilt the platform from the ground up. New reasoning engine, 3x faster agent response times, and a new UI that makes it easier for project managers to see what agents are doing, and why.
Post-mortem analysis of a $320M commercial project that went 32% over budget. The culprit wasn't the subcontractors. It was fragmented data across 11 systems that nobody was synthesizing.
Why every agent decision needs a reasoning trace. How we built the Arlyn audit log, and why construction teams actually use it, unlike the black-box tools they abandoned before.
No noise, just original thinking on construction AI, once a week.