Automations
This pillar focuses on AEC workflows that evaluate structural performance, cost, constructability, and carbon tradeoffs across design options before physical work begins. Content should show how a custom BIM optimization workflow can reduce redesign cycles, improve decision quality, and connect simulation, planning, and design tooling into one operating model.
This foundational page details a custom multi-agent workflow that orchestrates structural, cost, and sustainability simulations to evaluate design tradeoffs before construction. It explains how integrating simulation engines, cost databases, and design tooling reduces redesign cycles by 30-50% and improves decision confidence. The architecture focuses on LangGraph for orchestration, API integration with Revit and Tekla, and human-in-the-loop approval gates for high-stakes changes.
This page outlines a custom workflow where specialized agents analyze spatial conflicts, flow requirements, and maintenance access across mechanical, electrical, and plumbing systems. It delivers faster coordination, fewer field clashes, and lower rework costs by automating clash detection, routing optimization, and coordination drawing updates. Implementation involves browser agents for legacy design tools, a central conflict resolution engine, and integration with Navisworks or BIM 360.
This page describes a custom automation workflow that cross-references BIM models against dynamic municipal and international building codes. It reduces permit submission delays and costly redesigns by flagging violations early. The architecture combines retrieval-augmented generation (RAG) for local code interpretation, geometric rule checking, and a review dashboard for exceptions, integrating with platforms like Solibri or custom rule engines.
This page explains a custom agentic workflow that generates and scores thousands of architectural layout options against programmatic, operational, and well-being criteria. It accelerates schematic design, improves space utilization, and supports data-driven client presentations. The build involves generative algorithms, multi-objective optimization agents, and integration with Rhino/Grasshopper or Autodesk Dynamo for iterative model generation.
This page details a workflow where AI agents extract quantities, apply regionalized material and labor rates, and forecast cost volatility to generate dynamic estimates. It improves bid accuracy and speed, directly linking design changes to budget impact. Implementation requires connecting BIM APIs to cost databases like RSMeans, using LLMs for specification parsing, and embedding approval workflows for estimate releases.
This page covers a custom automation layer that continuously extracts quantities from evolving BIM models, forecasts material price trends, and updates project budgets. It eliminates manual takeoff drudgery and provides real-time financial visibility. The architecture uses computer vision for drawing interpretation, agents for data validation against historical trends, and feeds into ERP systems like Oracle Primavera or Procore.
This page explains a workflow that instantly calculates the budgetary impact of any design alteration, from a material swap to a structural revision. It prevents cost overruns by enabling informed change orders before commitment. The solution involves event-driven agents monitoring the CDE, querying cost databases, and generating comparative reports, with integration into change management platforms like Aconex.
This page outlines a custom workflow that automatically generates and simulates construction schedules by linking BIM components to construction methods and resource constraints. It visualizes build sequences, identifies logistical conflicts, and optimizes for time. Implementation uses agents to decompose models into constructible tasks, integrate with scheduling software (e.g., Synchro, Microsoft Project), and update sequences based on site feedback.
This page describes an automation system that creates detailed, resource-loaded schedules by analyzing BIM geometry, material delivery lead times, and crew availability. It improves labor forecasting and reduces idle time. The architecture involves agents that assign labor and equipment to tasks, model dependencies, and output schedules compatible with P6 or Asta Powerproject, with human review for complex constraints.
This page details a workflow where AI agents simulate thousands of schedule scenarios based on weather, supply chain, and productivity risks to identify critical paths and mitigation strategies. It provides proactive delay management. The build requires Monte Carlo simulation engines, integration of external risk data feeds, and a dashboard for highlighting high-probability delays to project managers.
This page explains a custom workflow that automatically calculates the embodied carbon of a building design by mapping BIM materials to environmental product declaration (EPD) databases. It enables rapid carbon trade-off analysis and supports sustainability certification. Implementation involves agents for material tagging, API connections to databases like EC3, and reporting integration with tools like Tally or One Click LCA.
This page covers a comprehensive automation workflow that models operational energy use, maintenance cycles, and end-of-life disposal to calculate a building's total carbon footprint over its lifespan. It informs net-zero strategy and investment decisions. The architecture combines energy simulation agents, lifecycle assessment models, and generates audit-ready reports for ESG disclosure frameworks.
This page describes a decision-support workflow where AI agents instantly compare the carbon impact of different structural systems, cladding materials, and HVAC strategies. It allows designers to optimize for sustainability without manual calculations. The solution uses multi-objective optimization, real-time EPD data lookup, and visual dashboards embedded within design software like Revit.
This page outlines a workflow where agents analyze BIM components for prefabrication suitability, checking for modularity, tolerances, and connection details. It reduces factory errors and accelerates off-site construction. Implementation involves rule-based checking agents, integration with detailing software (e.g., Tekla), and generating fabrication-ready outputs and bills of materials.
This page details an automation system that takes conceptual BIM models and automatically generates detailed, shop-ready drawings for prefabricated elements like wall panels or MEP racks. It slashes detailing time and improves manufacturing accuracy. The architecture uses specialized agents for connection design, reinforcement detailing, and nesting optimization, outputting to CNC machinery.
This page explains a workflow where drone-captured site imagery is compared to the 4D BIM model by computer vision agents to automatically calculate percentage completion and identify delays. It replaces manual walkthroughs with continuous, objective tracking. The build involves processing drone data on edge, a central comparison engine, and updating project dashboards in Procore or Autodesk Build.
This page covers a custom workflow that uses lidar scans and photogrammetry to create a digital twin of the constructed asset, then employs AI agents to detect deviations from the design intent. It streamlines quality assurance and supports accurate as-built documentation. Implementation requires point cloud processing, geometric deviation analysis, and automatic generation of discrepancy reports for field teams.
This page describes an automation system where computer vision agents analyze live camera feeds from construction sites to identify unsafe conditions like missing PPE, fall hazards, or unauthorized access zones. It enables proactive intervention to reduce incident rates. The architecture involves edge-based vision models, real-time alerting to site supervisors, and integration with safety management software.
This page details a dynamic workflow where AI agents analyze daily work plans, material deliveries, and crane movements to optimize material staging locations and vehicle routes on a congested site. It reduces double-handling and improves trade coordination. The solution uses digital twin simulation, real-time GPS tracking of equipment, and sends optimized layout plans to field tablets.
This page outlines a workflow where agents analyze BIM models of MEP systems to automatically generate comprehensive commissioning plans, including test procedures, schedules, and documentation requirements. It accelerates handover and ensures system performance. Implementation involves parsing system hierarchies from BIM, retrieving standard test protocols, and populating commissioning software like Facility Grid.
This page explains an automation layer that connects to building management systems (BMS) during commissioning, comparing live sensor data against design performance benchmarks. It automatically flags systems failing to meet specifications. The architecture involves IoT data ingestion agents, performance simulation models, and generating punch lists for contractors to rectify issues.
This page describes a workflow that automatically aggregates the as-built BIM model, equipment submittals, warranty data, and sensor points into a living digital twin for the owner. It creates a single source of truth for operations. The build requires agents to federate data from disparate sources, structure it in a graph database, and provide an interface integrated with CAFM systems like IBM Tririga.
This page details a workflow for building operations where AI agents use the BIM geometry and real-time IoT sensor data to predict occupancy and optimize HVAC setpoints for energy savings without compromising comfort. It reduces operational costs by 15-25%. Implementation involves integrating with BMS APIs, using reinforcement learning for control strategies, and providing explainable dashboards for facility managers.
This page covers a workflow that analyzes Wi-Fi, badge, and sensor data against the BIM-based floor plan to model how spaces are actually used. It identifies underutilized areas and recommends reconfigurations or consolidation strategies. The architecture uses occupancy analytics agents, space planning algorithms, and feeds recommendations into IWMS platforms like SpaceIQ or Serraview.
This page explains a workflow that automates the tedious assembly of permit submission packages by extracting required drawings, calculations, and forms from the BIM model and project documentation. It cuts submission preparation from weeks to days. The solution uses document understanding agents to parse municipal requirements, auto-populate forms, and compile PDF packages ready for digital submission portals.
This page describes a custom workflow where agents analyze a site's BIM context against geospatial zoning maps, setback rules, height restrictions, and FAR calculations. It provides early-stage feasibility studies and identifies non-compliant design elements. Implementation involves integrating GIS data, parsing municipal zoning codes with RAG, and visualizing constraints directly within the design environment.
This page outlines a critical coordination workflow where AI agents automatically collect, version-check, and federate discipline-specific models (architectural, structural, MEP) into a unified view, checking for consistency and completeness. It ensures team alignment and model integrity. The architecture involves agents monitoring a Common Data Environment (CDE), running validation scripts, and notifying model authors of conflicts.
This page details a workflow that automates the governance of a project's CDE, including file naming enforcement, access control, audit logging, and archival. It reduces data chaos and ensures compliance with ISO 19650. The solution uses agents to monitor file uploads, apply business rules, and integrate with platforms like Autodesk BIM 360 or ProjectWise to enforce standardized workflows.
This page explains a workflow where AI agents classify issues logged in the BIM coordination platform (e.g., clashes, RFIs) and automatically route them to the responsible party based on discipline, contract, and urgency. It accelerates issue resolution cycles. Implementation involves natural language processing for issue description, integration with project directory APIs, and smart notification systems.
This specialized page details a workflow for healthcare projects where AI agents simulate patient and staff flows, equipment logistics, and infection control protocols within the BIM model to optimize departmental layouts. It improves operational efficiency before construction. The build integrates healthcare-specific simulation engines, regulatory guidelines, and provides evidence-based design recommendations.
This page covers a workflow for mission-critical facilities where agents model computational fluid dynamics (CFD) within the BIM to optimize hot/cold aisle containment, CRAC unit placement, and airflow. It minimizes PUE (Power Usage Effectiveness) and reduces cooling capex. Implementation involves coupling BIM with CFD simulation APIs, using optimization algorithms, and generating detailed mechanical schematics.
This page describes a workflow for aviation projects where AI agents simulate thousands of passenger itineraries through the terminal BIM to identify bottlenecks at security, check-in, and boarding gates. It informs design decisions to improve passenger experience and retail revenue. The solution uses agent-based simulation models, integrates flight schedule data, and provides visual congestion heatmaps.
This page outlines a supply chain workflow where AI agents analyze the BIM's bill of materials, cross-reference it with real-time supplier lead times and port congestion data, and flag critical path materials. It enables proactive procurement to prevent schedule delays. Implementation involves web scraping agents for supplier portals, integration with procurement software, and alerting the project scheduler.
This page details a logistics workflow where agents coordinate delivery schedules based on the 4D construction sequence, site storage capacity, and real-time progress tracking. It minimizes on-site inventory and congestion. The architecture uses digital twin of the site, communicates with supplier logistics systems, and sends dynamic delivery windows to truck drivers via mobile app.
This page explains an advanced analytics workflow where AI agents perform integrated cost and schedule risk analysis (IPSRA) by modeling correlations between delays and cost overruns. It provides a probabilistic view of project outcomes for better contingency planning. The build requires Monte Carlo simulation, historical project data training, and executive dashboards showing confidence intervals for finish dates and budgets.
This page covers a site investigation workflow where agents analyze BIM-integrated subsurface data (from boreholes and surveys) to model soil stability, excavation challenges, and foundation risks. It informs foundation design and construction methodology. Implementation involves processing geospatial data, running finite element analysis models, and generating risk maps layered onto the site BIM.
This page describes a workflow where AI agents ingest hyper-local weather forecasts, map them to weather-sensitive tasks in the 4D schedule, and recommend proactive mitigations like resequencing work or pre-staging materials. It reduces productivity losses. The solution integrates weather API feeds, understands task weather sensitivity from historical data, and pushes adaptive schedule updates to field teams.
This specialized page details a workflow for tall buildings where AI agents iteratively simulate and optimize the design of shear walls, braced frames, or outriggers to meet stiffness and drift requirements with minimal material. It reduces structural tonnage and cost. Implementation involves parametric modeling in Rhino/Grasshopper, cloud-based FEA solvers, and multi-objective optimization agents trading off steel, concrete, and floor space.
This page outlines a workflow for civil infrastructure where agents automate the generation and analysis of bridge design alternatives under various live and dead load combinations. It accelerates preliminary design and ensures code compliance. The architecture uses agents to parameterize geometry, call AASHTO-compliant analysis engines, and compile comparative reports on material use and constructability.
This page explains a workflow for underground construction where agents analyze geotechnical BIM data to optimize the alignment and advance rate of a TBM, minimizing risk of settlement or encountering obstructions. It improves tunneling safety and efficiency. The solution integrates geological models, TBM telemetry, and uses reinforcement learning to recommend optimal operational parameters.
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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