Planera, on April 15, launched «Manny,» an artificial intelligence-based scheduling assistant designed to aid construction professionals in analyzing schedules, understanding impacts of delays and changes and making quicker decisions during unforeseen events.
Embedded directly within Planera’s platform, Manny is designed to turn a static schedule into a real-time decision-making tool.
«Every great builder I know often wants to run scenarios,» says Nitin Bhandari, CEO and co-founder of Planera. «A problem could have three or four different ways to recover from it, but it’s hard to manually model out those scenarios. AI can model those for you, get you the answers, and then you can make a decision really quickly.»
Schedule analysis can be a slow, manual process limited to estimators, schedulers and, sometimes, project managers. Manual data entry is still the norm for many small- to medium-sized contractors. Planera AI’s Manny allows users to interact with schedule data using natural language. Teams can ask questions, surface critical path drivers and run “what-if” simulations in minutes.
Bhandari said the AI was trained for years during Planera’s beta testing to understand construction concepts such as the critical path.
«CPM scheduling is the heartbeat of a project, but traditional tools move too slowly for the modern jobsite,” he said. “Manny makes schedule analysis faster, more accessible, and more actionable. It doesn’t replace the scheduler’s expertise. It helps teams use that expertise faster and more effectively.»
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DPR Uses ConstructivIQ On 120+ Projects
DPR Construction has scaled deployment of ConstructivIQ—an AI-enabled platform for managing materials, logistics and procurement—to more than 120 projects across its enterprise.
Since signing an enterprise agreement in early 2025, DPR has implemented ConstructivIQ on some of the world’s largest and most complex data center projects—including its hyperscale data center projects in Abilene, Texas, and Louisiana—to support enterprise-scale procurement planning.
The use cases for procurement planning and material tracking that are now covered by ConstructivIQ run the gamut from SKIDs, owner-furnished equipment (OFCI), materials requiring field measurement, recessed materials and more, each with different planning and tracking needs.
«The way DPR and others are speeding up data center construction is by using skids, so everything gets integrated before it ships,» said Sadanand Sahasrabudhe, ConstructivIQ CEO, who gained 30 years of experience in software development before co-founding the company.
«The scenario you now have is you have a bunch of component materials, electrical transformers and other things that are getting shipped to an integrator,» Sahasrabudhe added. «The integrator is putting the skid together, and the skid is shipping to the site. You need to track the component materials, because they themselves can [have] long lead times. You need to track them arriving at the integrator. The integrator has to put the skid together. Now you need to track the skid. So you got materials that depend on other materials.»
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Atul Khanzode, chief technology officer at DPR Construction, said the platform’s AI capabilities have enabled DPR’s project teams to move from reactive to proactive planning, improving both team efficiency and reducing schedule risk across its portfolio.
«What becomes hard for people [to] make sense of is a lot of data analyzed together,» Khanzode said. «What is helping with this embedment of AI appropriately is what potential schedule activities are tied to particular equipment or material procurements. As the ConstructivIQ system is processing more and more, that knowledge or materials and equipment for particular construction activities only gets better.»
DPR is expanding its efforts from deployments to standardize procurement KPIs and best practices across its methodology. It is now establishing enterprise metrics for material management, improving procurement planning with trade partners and implementing submittal tracking.
Additionally, the company is exploring how this data can help internal supply chain teams advise project teams on equipment lead times, schedule impacts, and alternative materials.
