Automations
This pillar covers site intelligence workflows that coordinate drones, robots, and edge sensors to monitor progress, inspect quality, and compare built reality to digital plans. Content should show how a custom field-orchestration workflow improves visibility, reduces manual inspection overhead, and closes the loop between project systems and physical execution.
This foundational page details the architecture for a central orchestration layer that schedules, dispatches, and coordinates fleets of drones and ground robots across a construction site. It explains how custom multi-agent workflows reduce manual flight planning and data wrangling, improve asset utilization, and create a unified data feed for project management systems, directly tying the build to faster decision cycles and lower field coordination costs.
This page covers a custom workflow where daily drone-captured point clouds are automatically registered and compared against the 4D BIM to detect deviations. It details the agentic pipeline for discrepancy flagging, severity scoring, and routing alerts to superintendents, showing how this automation reduces rework costs, prevents schedule drift, and integrates with platforms like Autodesk Construction Cloud or Bentley iTwin.
This page explains a workflow where drones and site cameras automatically identify, log, and verify material deliveries against purchase orders and laydown plans. It covers the architecture for OCR, computer vision, and integration with ERP systems like SAP or Oracle, demonstrating how it eliminates manual ticketing, reduces theft/loss, and improves inventory accuracy for project controllers.
This page details a drone-based automation that autonomously maps stockpiles, calculates volumes using photogrammetry, and generates compliance-ready reports. It explains the edge processing, approval gates for significant changes, and integration with accounting software, showing how it replaces costly manual surveys, reduces material waste, and provides auditable data for project financials.
This page outlines a workflow where ground robots and drones monitor concrete placement, track pour sequences, and document temperature and slump tests. It covers the integration of sensor data, automated report generation, and exception alerts for potential cold joints or delays, demonstrating how it ensures quality compliance, reduces manual inspector hours, and creates a defensible construction record.
This page describes a real-time computer vision workflow using fixed cameras and drones to detect PPE violations and unsafe proximity to equipment. It details the alerting logic, integration with site access systems, and escalation paths for superintendents, showing how it proactively reduces incident rates, lowers insurance premiums, and automates OSHA documentation burdens.
This page explains a workflow that fuses IoT telemetry, drone imagery, and RFID to create a real-time map of equipment location and idle/active status. It covers the dashboard integration, predictive maintenance triggers, and reporting for fleet optimization, demonstrating how it eliminates manual equipment walks, improves lease vs. buy decisions, and increases asset ROI.
This page details a drone-LiDAR workflow that compares cut/fill volumes against grading plans daily. It explains the agentic process for calculating balance, identifying out-of-tolerance areas, and updating project schedules automatically, showing how it prevents over-excavation, optimizes trucking cycles, and keeps earthmoving subcontractors accountable to design.
This page covers a workflow where drones and scanners verify the precise placement of steel columns, precast panels, or glulam beams against BIM coordinates. It details the high-accuracy comparison, non-conformance reporting, and integration with fabrication tracking systems, demonstrating how it eliminates costly field adjustments, ensures structural integrity, and accelerates subsequent trade work.
This page explains a workflow that ingests hyperlocal weather forecasts, correlates them with the project's critical path, and simulates schedule impacts. It details the agentic logic for recommending crew reassignments or material delivery delays, and the integration with scheduling software like Primavera P6, showing how it reduces weather-related downtime and improves resource resilience.
This page details a computer vision workflow where drones capture rebar cages before concrete pour, verifying spacing, size, and lap lengths against structural drawings. It covers the automated generation of inspection reports and punch lists, demonstrating how it replaces slow manual checks, reduces the risk of structural failure, and provides assurance for engineers of record.
This page outlines a workflow where drones equipped with thermal and visual cameras autonomously inspect weld seams on steel structures. It explains the analysis of heat signatures for defects, the routing of flagged welds for UT testing, and the integration with quality management systems, showing how it improves inspection coverage, speeds up commissioning, and reduces liability.
This page describes a workflow where autonomous ground robots equipped with laser scanners or profilometers map finished floor surfaces. It details the FF/FL number calculation, comparison to spec, and automated report generation for the concrete subcontractor, demonstrating how it delivers objective quality data faster than manual methods, ensuring compliance and avoiding disputes.
This page covers a workflow deploying sensor-equipped drones and fixed stations to map particulate matter and noise levels across the site perimeter. It explains the real-time dashboard, automated alerting when thresholds are breached, and generation of environmental compliance reports, showing how it mitigates community complaints, avoids regulatory fines, and supports ESG reporting.
This page details a scheduled drone inspection workflow that checks scaffolding for missing components, base plates, and tie-ins. It covers the computer vision model training, condition scoring, and automatic work order generation in CMMS systems, demonstrating how it improves safety oversight, reduces manual inspection labor, and ensures regulatory compliance for high-risk temporary structures.
This page explains a workflow where LiDAR-equipped drones capture as-built conditions of in-slab or in-wall MEP rough-ins before closure. It details the automated clash detection against BIM models and the generation of RFIs for missed sleeves or conflicts, showing how it prevents extremely costly retrofit work, reduces coordination delays, and ensures systems are installable as designed.
This page outlines a workflow using drones and photogrammetry to verify the alignment, sealant application, and hardware installation of curtain wall and window systems. It covers the tolerance analysis, leak risk scoring, and integration with commissioning checklists, demonstrating how it ensures building envelope integrity, reduces water intrusion callbacks, and speeds up facade completion.
This page describes a workflow that uses computer vision at gatehouses and drones overhead to log personnel and vehicle ingress/egress, cross-referencing with badged access lists. It details the real-time headcount dashboard, alerting for unauthorized entry, and integration with payroll systems, showing how it enhances site security, simplifies labor hour verification, and supports emergency evacuation accounting.
This page details a workflow where drones with multispectral cameras inspect below-grade waterproofing membranes and roof installations for continuity and thickness. It explains the anomaly detection logic, the generation of repair maps, and the link to warranty documentation systems, demonstrating how it catches application flaws before backfill or topping, preventing major water damage and litigation.
This page covers a workflow that deploys a network of smart sensors and drones to monitor vibration from pile driving and noise levels, correlating them with community complaint logs. It details the real-time alerting to site supervisors, automated regulatory report generation, and predictive modeling to adjust work methods, showing how it manages community relations and avoids work stoppages.
This page outlines a workflow where drones equipped with ultrasonic or eddy current sensors measure dry film thickness on structural steel or tanks. It explains the automated comparison to spec, mapping of under/over-application areas, and generation of remediation work orders, demonstrating how it ensures corrosion protection, reduces material waste, and provides objective quality records for owners.
This page describes a workflow that uses drone video feeds and computer vision to model vehicle and pedestrian traffic, identifying bottlenecks and near-miss events. It details the dynamic routing suggestions for delivery trucks, alerting for congestion, and integration with digital twin simulations, showing how it improves material flow efficiency and reduces the risk of vehicle-on-person incidents.
This page explains a workflow where drones categorize and estimate volumes of waste in site dumpsters, tracking diversion rates for wood, metal, and concrete. It covers the automated reporting for LEED documentation, scheduling of haul-away services, and cost allocation to responsible trades, demonstrating how it reduces landfill fees, supports sustainability goals, and improves site housekeeping.
This page details a workflow using drones to visually inspect the placement of sprinkler heads, firestop penetrations, and standpipe connections against life safety drawings. It covers the automated punch list generation and integration with commissioning software like Autodesk BIM 360, showing how it accelerates the critical path to obtaining a certificate of occupancy and ensures regulatory compliance.
This page outlines a workflow where drones perform final topographic surveys, verifying finish grades, drainage slopes, and topsoil placement against civil plans. It explains the automated calculation of cut/fill balances for final payment and the generation of as-built documentation for the owner, demonstrating how it streamlines project closeout and reduces disputes over sitework completion.
This industry-specific page details a multi-agent workflow for coordinating tower cranes, material hoists, drone flights, and floor-by-floor inspections in a dense urban high-rise project. It covers the integration with logistics software, dynamic airspace management, and progress tracking against a 4D BIM, showing how it mitigates sequencing conflicts and accelerates vertical construction cycles.
This page explains a workflow for using drones and crawler robots to conduct visual and thermal inspections of vessels, piping, and structures during planned plant shutdowns. It details the integration with asset integrity management systems, automated thickness measurement, and work package prioritization, demonstrating how it reduces scaffolding costs, shortens turnaround duration, and improves worker safety in confined spaces.
This page covers a workflow for large linear projects, using swarms of drones and robotic total stations to monitor alignment, settlement, and falsework integrity. It explains the data fusion for digital twin updates, alerting for geotechnical risks, and integration with heavy civil design software, showing how it ensures structural safety and adherence to tight tolerances over long distances.
This page details a workflow for verifying the precise placement and interconnection of prefabricated data hall modules, electrical skids, and cooling units. It covers drone-based scanning for clash detection, automated validation against manufacturer specs, and integration with commissioning scripts, demonstrating how it ensures rapid, error-free deployment critical to hyperscale construction schedules.
This industry page explains a workflow where drones and robots capture the complex MEP overhead in hospital ceilings before drywall installation. It details the automated clash detection against coordinated BIM models and the generation of prefabrication drawings for ductwork and piping, showing how it prevents costly field rework and ensures critical medical gas and HVAC systems are installable.
This quality control page details a workflow where drones with high-resolution cameras and AI models autonomously patrol structures to identify and classify cracks, spalling, or honeycombing. It explains the severity scoring, routing of repair tickets to superintendents, and historical tracking, demonstrating how it enables proactive maintenance, improves quality assurance, and reduces liability from latent defects.
This page covers a workflow that automatically compares as-built point cloud data from robotic scanners against the BIM model's specified tolerances for every building element. It details the statistical reporting, identification of systemic trade errors, and integration with quality control systems, showing how it enforces construction precision and reduces fit-up issues for subsequent assemblies.
This page explains a workflow that goes beyond design-phase clash detection, using site-captured data to identify conflicts that emerge during construction (e.g., a duct installed where a beam was supposed to go). It details the automated RFI generation, assignment to responsible parties, and resolution tracking, demonstrating how it catches costly field conflicts early, before they require demolition.
This progress tracking page details a workflow where daily drone progress imagery is automatically analyzed, mapped to BIM elements, and used to update the project's 4D schedule. It explains the earned-value calculation, critical path impact analysis, and forecast updates, showing how it gives project managers real-time visibility into schedule performance and delay root causes.
This page outlines a workflow that automates the data collection for Earned Value Management by using robots to quantify installed quantities (e.g., cubic yards of concrete, linear feet of pipe). It details the integration with cost-loaded schedules and accounting systems, demonstrating how it provides accurate, auditable EVA metrics without manual quantity surveying, improving financial control and forecasting.
This page describes a workflow where specialized agents monitor drone and sensor data to confirm the completion of specific critical path activities (e.g., foundation pour cured, steel column erected). It details the automated notification to the project scheduler and the triggering of successor tasks, showing how it reduces schedule uncertainty and accelerates decision-making to maintain pace.
This safety page details a workflow using drones and fixed cameras to continuously scan for unguarded edges, open shafts, and improper scaffolding access. It explains the real-time alerting to nearby workers via wearables and to site safety officers, demonstrating how it creates a proactive safety net, reduces fall-related incidents, and strengthens the project's safety culture.
This page covers a workflow that uses drones and IoT sensors to monitor atmospheric conditions and personnel within permitted confined spaces. It details the integration with permit-to-work systems, automated alarm triggering for hazardous gas detection, and log maintenance, showing how it enhances worker protection, ensures regulatory compliance, and reduces the need for safety watch standers.
This page explains a workflow where thermal imaging drones patrol the site to detect unauthorized hot work (welding, grinding) and verify that permitted hot work zones have proper fire watches and extinguishers present. It details the violation alerting and integration with safety management software, demonstrating how it systematically reduces fire risk on congested job sites.
This logistics page details a workflow that ingests the project schedule, laydown yard capacity, and real-time traffic data to optimize delivery windows for suppliers. It explains the automated communication with truckers, dynamic gate management, and integration with ERP systems, showing how it reduces laydown yard congestion, minimizes material handling, and cuts demurrage costs.
This page outlines a workflow where daily drone maps analyze laydown yard utilization, identifying unused space and misplaced materials. It details the automated suggestions for reorganization, integration with material tracking databases, and alerting for safety violations like blocked fire lanes, demonstrating how it maximizes scarce site space and improves trade productivity.
This page covers a workflow for modular construction, using RFID and drones to track prefab modules from factory to site, verifying their condition upon arrival and guiding crane operators for precise placement via AR overlays. It details the integration with manufacturing and installation schedules, showing how it reduces errors, accelerates installation, and ensures modular efficiency gains are realized.
This data integration page details a workflow where agents synthesize data from drones, robots, weather feeds, and crew inputs to automatically produce narrative and visual daily reports. It explains the distribution to stakeholders, highlighting variances and risks, and integration with platforms like Procore, demonstrating how it saves superintendents hours daily and improves stakeholder communication.
This page explains a workflow where point clouds and imagery from site robotics are processed to automatically identify and flag changes from the design model, then generate proposed updates for the BIM manager's review. It details the change-log creation and integration with BIM authoring tools, showing how it keeps the digital twin accurate for downstream FM use without manual remodeling.
This advanced robotics page details a workflow for autonomously coordinating a swarm of drones to efficiently map mega-projects (e.g., airports, solar farms) by dividing airspace and processing data in parallel. It explains the fleet management logic, edge computing architecture, and seamless data stitching, showing how it reduces mapping time from days to hours, enabling near-real-time site intelligence.
This page outlines a workflow for deploying rugged UGVs to autonomously patrol site perimeters, inspect below-grade areas, and monitor conditions in unsafe or inaccessible zones overnight. It details the path planning, obstacle avoidance, and integration of sensor payloads (gas, thermal), demonstrating how it extends monitoring coverage, enhances security, and reduces risk to personnel.
This page covers a workflow for integrating collaborative robots (cobots) with human workers for tasks like material handling or precision positioning. It details the safety sensing, task allocation logic, and real-time communication interfaces, showing how it reduces worker fatigue, improves precision, and safely introduces automation into dynamic construction environments.
This page details a workflow where a bricklaying robot's progress is continuously verified by a companion drone, checking for alignment, mortar joint consistency, and pattern accuracy. It explains the closed-loop feedback to the robot controller and automated quality reporting, demonstrating how it ensures robotic masonry meets design intent and quality standards without constant human oversight.
This page explains a workflow where autonomous robotic total stations or GPS rovers perform layout and as-built surveying based on digital design files. It details the elimination of manual stakeout, the automated validation of placed elements, and the integration with project control systems, showing how it increases layout speed and accuracy while reducing labor-intensive survey crew hours.
How We Work
One-fit-all AI don't work for modern businesses. At Inferensys, we aim to understand your business & custom requirements; which we use to define most efficient agentic workflows, the data, and the tools for your business.
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We understand the task, the users, and where AI can actually help.
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We define what needs search, automation, or product integration.
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We implement the part that proves the value first.
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We add the checks and visibility needed to keep it useful.
Read moreThe first call is a practical review of your use case and the right next step.
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