GPT4All

New:AI in the Built World — March 2026New:AI in the Built World — March 2026AI in the Built World — March 2026Our productsNomic transforms decades of unstructured data into organized, AI-ready knowledge. Empower your teams with domain-specific agentic workflows that accelerate every project, workflow, and decision across your firm's institutional knowledge.Nomic PlatformOur turnkey platform that accelerates your engineers and designers with domain-specific agentic workflows across your firm's knowledge.Developer APIAccess our domain-specific models for search, document understanding and drawing parsing to acclerate your firm's internal AI solutions.Use CasesSee how Nomic automates code compliance, drawing review, submittal review, and firm-wide search. Ground AI in your drawings, specs, and project data to catch issues before construction and accelerate every workflow.#/Your Private and Local AI ChatbotDownload for macOSDownload for macOSDownload for macOSDownload for WindowsDownload for WindowsDownload for WindowsDownload for Windows ARMDownload for Windows ARMDownload for Windows ARMDownload for UbuntuDownload for UbuntuDownload for UbuntuGPT4All delivers private, high-performance AI right on your device—no cloud required, no data leaves your machine.About GPT4AllDesigned for developers, teams, and AI power-users, GPT4All runs open-source language models on Windows, macOS, and Linux—with full customization, local document chat (LocalDocs), and support for thousands of models—empowering you to build assistants and workflows with maximum control, security, and speed.Explore Nomic PlatformAvoid rework and accelerate asset creation by giving your teams the AI capabilities to get reliable project insights instantly, and run workflows like RFIs and RFP generation across your entire knowledge base at a fraction of the usual task time.Explore Nomic PlatformExplore Nomic PlatformExplore Nomic PlatformExplore MoreNomic PlatformDomain-specific AI agents that accelerate AEC workflows across drawings, specs, and project dataDeveloper APIAEC-trained models for document parsing, embeddings, and intelligent searchUse CasesAI workflows for code compliance, drawing review, submittal review, and project research --- New:AI in the Built World — March 2026New:AI in the Built World — March 2026AI in the Built World — March 2026Our productsNomic transforms decades of unstructured data into organized, AI-ready knowledge. Empower your teams with domain-specific agentic workflows that accelerate every project, workflow, and decision across your firm's institutional knowledge.Nomic PlatformOur turnkey platform that accelerates your engineers and designers with domain-specific agentic workflows across your firm's knowledge.Developer APIAccess our domain-specific models for search, document understanding and drawing parsing to acclerate your firm's internal AI solutions.Use CasesSee how Nomic automates code compliance, drawing review, submittal review, and firm-wide search. Ground AI in your drawings, specs, and project data to catch issues before construction and accelerate every workflow.Simple, transparent pricingPricing built for clarity, control, and scaleDeploy secure, accurate AI across your firm with measurable ROI. Clear pricing that scales with your needs. No surprises, no hidden fees.Most PopularBusiness$40*/ user / month*Minimum 25 seat commitment requiredEverything your team needs to automate drawing reviews and project tasks with AI.Automate drawing reviews & project tasks$20 included AI usage per seatCentralized team billingUsage analytics & reportingOrg-wide privacy controlsSAML/OIDC SSOGuided bulk data indexingGet StartedEnterpriseCustomAdvanced security, compliance, and support for large organizations with complex requirements.Everything in Business, plus:Custom AI usage commitsSCIM, audit logs, granular controlsCustom deployment options (VPC, on-prem)Priority support & dedicated CSMSelf-guided bulk data indexing at scaleCustom workflows & implementation supportContact Sales*Prices shown are list. Annual commitment required. Additional seats: $40/user/month. Deployment options and add-ons may affect final pricing.How pricing worksSeats are the primary way teams scale both access and included AI usage capacity.1Minimum platform commitment$1,000/month, includes 25 seats, annual commitment.2Seats scale access and included AI usageEach $40/seat/month includes $20 of included AI usage.3AI usage is pooled at the org levelApplies across Platform, Assistant, Workflows, and Developer API.4Bulk data indexing is guided by defaultLarge dataset onboarding is scoped separately. Enterprise unlocks self-guided bulk data indexing.5Overages are visible with admin controlsOnly occur after included AI usage is consumed. Set alerts and limits in your admin dashboard.Designed to pay for itself.Nomic cuts the time your teams spend searching, reviewing, and cross-referencing project documents. Hours of manual work drop to minutes of review.WorkflowManual ProcessCurrent CostWith NomicCostSavingsComplex RFI ResponseResearch, drafting & verification8 HoursCoordinate, review & respond to complex RFIs$1,60030 MinAgent retrieves details, cites sources, drafts response$10016x FasterZero context-switchingCode Compliance CheckPermit set verification16 HoursManual plan check against local codes (30+ pages)$3,2004 HoursAgent pre-flags code issues, occupancy loads, conflicts$80075% SavedFaster permit cyclesSubmittal / Spec ReviewConflict detection & compliance4 HoursManual review of complex packages (70+ pages)$80020 MinAgent scans docs, extracts requirements, generates matrix$6712x FasterReduces human errorTechnical Report WritingAggregating notes & writing25 HoursWriting technical content from scattered notes$5,0005 HoursAgent aggregates artifacts and writes first draft$1,00080% SavedFocus on designDrawing Set ReviewCross-sheet coordination10 HoursManual "red line" check of 20+ sheets for errors$2,0002 HoursAgent diffs sheets, flags missing tags automatically$4005x FasterHigher QA confidence* "With Nomic" times assume human review and final approval is still required.Based on $200/hr fully loaded billable rate."Nomic empowers us to build and deploy domain-specific AI to our whole global design team."Dave MackenzieManaging Principal DigitalRead More+30%Productivity Increase+20%Engineering Capacity IncreaseGet in touchReady to transform how your team works?Connect your institutional knowledge to every project, workflow, and decision. Get a personalized demo to see measurable ROI for your firm.Book a DemoExplore PlatformAnswers to your pricing questions.Everything you need to know about our pricing model and what's included.What is AI usage and how does it work?AI usage refers to the consumption of AI models when you use Nomic's Platform. Each seat includes $20 of included AI usage per month that can be applied across the Platform, including Assistant, Workflows, document ingestion, and Developer API tools built on top of Nomic.Included AI usage covers:Drawing reviews and code compliance checksProject data search and research queriesDocument ingestion and indexingDeveloper API usageCustom tools and Workflows built on the Developer APIAI usage is priced transparently and aligned with underlying model costs. Unused included AI usage rolls forward within your annual commitment period.What can I do with $20 of AI usage?With $20 of AI usage, you can typically:Fully review approximately 30 drawings per month (learn more)Ask 100 queries across your project data (learn more)Perform hundreds of code compliance checks (learn more)Generate dozens of technical reportsBuild and run custom Developer API Workflows (learn more)This allocation is based on real usage metrics across our deployments, ensuring your team has sufficient capacity for common Workflows.How is data indexing charged?You are only charged when you explicitly index or use AI, not for metadata visibility. When you index documents, the cost is deducted from your included AI usage balance. Once indexed, your data remains accessible for the duration of your license without additional charges.Small, ongoing indexing is self-serve and draws from your included AI usage balance.Bulk data indexing (large historical archives, multi-project backfills, high-volume onboarding) is a scoped and guided process by default to keep onboarding predictable. Enterprise unlocks self-guided bulk data indexing for large datasets and higher-throughput ingestion.What if I need more AI usage?Teams can add seats to increase both Platform access and included AI usage capacity. Each additional seat adds $20 of included AI usage to your org-level pool.You can also purchase additional packages of included AI usage as needed. We'll help you right-size your usage based on your team's needs and can provide flexible add-on packages to scale your AI capabilities as you grow.I have terabytes of project data. How does that work?You are only charged when you explicitly index or use AI. While you'll see metadata from your Sharepoint, Egnyte, ACC, or Bentley project files, you won't be charged unless you explicitly index that data.Small, ongoing indexing is self-serve and draws from your included AI usage balance. Start with one project or a specific file type across all projects, then expand as you realize value.Bulk data indexing for large historical archives or multi-project backfills is a scoped and guided process by default to keep onboarding predictable. Enterprise unlocks self-guided bulk data indexing for large datasets and higher-throughput ingestion.What deployment options are available?We offer flexible deployment options to meet your security and compliance needs:Cloud: Fully managed SaaS deploymentVirtual Private Cloud: Dedicated cloud instanceOn-premise: Self-hosted in your infrastructureEnterprise plans include custom deployment options with guided implementation and dedicated support. Contact sales for details.Learn more about our security practices →Still have questions? We're here to help.Talk to SalesCompare plans side by sideSee exactly what's included in each plan to find the right fit for your team.FeaturesBusinessEnterpriseIncluded AI usage ($20/seat/mo)Developer API accessSAML/OIDC SSOUsage analytics & reportingGuided bulk data indexingCustom AI usage commits—Invoice/PO billing—SCIM provisioning—Audit logs & compliance reporting—Custom deployment (VPC, on-prem)—Self-guided bulk data indexing at scale—Priority support & dedicated CSM—Custom workflows & implementation— --- New:AI in the Built World — March 2026New:AI in the Built World — March 2026AI in the Built World — March 2026Our productsNomic transforms decades of unstructured data into organized, AI-ready knowledge. Empower your teams with domain-specific agentic workflows that accelerate every project, workflow, and decision across your firm's institutional knowledge.Nomic PlatformOur turnkey platform that accelerates your engineers and designers with domain-specific agentic workflows across your firm's knowledge.Developer APIAccess our domain-specific models for search, document understanding and drawing parsing to acclerate your firm's internal AI solutions.Use CasesSee how Nomic automates code compliance, drawing review, submittal review, and firm-wide search. Ground AI in your drawings, specs, and project data to catch issues before construction and accelerate every workflow.AI in the Built WorldA monthly pulse on AI across design and build — curated by the Nomic agent and CEO Andriy.SubscribeGet our monthly AI in the Built World issue delivered to your inbox.SubscribeBy subscribing, you agree to our Privacy PolicyPast IssuesLinkedInX / TwitterHi, Andriy from Nomic here. Here's what happened in the world of AI and the built environment from Feb 6, 2026 – Mar 18, 2026. The gap between conference decks and deployed systems is closing fast: SMEs are shipping full project platforms built with AI, robots are tying rebar on live highway jobs, and digital twins are wiring directly into data-center and building controls—what's still missing is a product layer that can digest the chaos of drawings, RFIs, sensors, and emails into agents owners actually trust.Construction AI launches UK SME project platform built entirely via AI collaborationConstruction AI brought into production what it calls the first "AI‑native" project management platform aimed at the 98% of UK construction firms that have been priced out of enterprise software. Chartered builder Steve McKenna, a 30‑year industry veteran with no software background, used AI tools to help generate more than 700,000 lines of code and 186 database tables across 22 integrated modules covering drawings, tenders, cost control, programme, and commercial workflows, according to the company's launch announcement. The SaaS platform includes OCR-based drawing intake, sector‑specific assistants for contract queries, automated risk surfacing, and portfolio‑level reporting designed around UK practice, rather than generic CRM abstractions. Construction AI positions the product as infrastructure for a £170bn industry technology gap, signalling that domain experts, not traditional dev teams, may increasingly drive the next wave of built-world software.Construction AI · GlobeNewswire · Read more →Top Trends in Feb 6, 2026 – Mar 18, 2026Agentic project control moves from pilots to portfolio-scale rolloutsProcore introduces Agentic APIs: Procore announced "Agentic APIs" designed for deep search and action across construction data, highlighting Track3D as a partner that turns site video into structured progress metrics without bespoke data plumbing.Hensel Phelps standardises AI progress tracking: Contractor Hensel Phelps will deploy Track3D's machine‑learning platform on more than 200 active projects after a San Francisco airport connector job reported saving about $342,000 and nearly 3,000 labour hours in documentation and progress coordination.AI project control becomes a compliance issue in the UK: A UK analysis argues that under the Building Safety Act 2022, AI-backed project information systems are shifting from competitive advantage to a de‑facto requirement to evidence the "Golden Thread" for higher‑risk buildings.Developers build scheduling and planning agents on top of legacy tools: Indian developer Estairra launched a web platform that auto‑recalculates project timelines and manpower when tasks slip, while US‑based LeanCon raised $6m to cut preconstruction planning from months to minutes by assembling AI "pre‑construction engineering" teams in software.Predictive safety systems stack from CCTV to portfolio risk modelsOracle ships portfolio‑level safety forecasting: Oracle Construction and Engineering Advisor for Safety went live with a pre‑trained model built on more than 10,000 project‑years of data, with early users reporting up to 50% lower incident rates and 75% reductions in workers' compensation costs on high‑risk projects.Singapore trials AI CCTV on 14 construction sites: The SafeSite Video Analytics system, trialled on 14 public‑sector worksites, uses cameras to detect missing PPE, entries into restricted zones and proximity to machinery, sending Telegram alerts within seconds and contributing to a reduction in unsafe practices after 36 workplace deaths in 2025.Edge AI reduces latency in camera-based safety monitoring: Hardware supplier InHand Networks reported using its EC5000 AI edge computer to run site‑safety video analytics locally, avoiding cloud round‑trips and enabling faster alerts for hazards like workers entering exclusion zones.LLMs show moderate accuracy on visual hazard recognition: A study on SafetyInsights.org found GPT‑4o could identify visual safety hazards in construction imagery with around 69% accuracy, suggesting current models are useful as a second set of eyes but still require professional oversight.Construction robotics moves from service experiments to owned fleetsRebar-tying robots become capital assets, not just a service: Advanced Construction Robotics now sells its TyBOT rebar‑tying robot for Q1 2025 delivery, after deployments tying up to 1,100 joints per hour on bridges like the NASA Causeway and I‑26/I‑95 interchange and hitting 2 million cumulative ties across projects.Sitegeist raises €4m for concrete-renovation robots: Munich‑based Sitegeist closed a €4m pre‑seed round to deploy modular robots that work directly on aging concrete structures without needing clean 3D models or standardised site conditions, targeting Europe's multi‑hundred‑billion‑euro infrastructure repair backlog.Investors fund "physical AI" for heavy infrastructure work: Silicon Valley startup RoboForce raised $52m for TITAN robots built for solar construction, logistics and mining, while Austin‑based Apptronik extended its Series A past $935m to scale Apollo humanoids tested on palletising and trailer unloading.Major contractors integrate robots into everyday site logistics: South Korea's Samsung C&T detailed deployments of autonomous forklifts, material‑handling robots and water‑spraying drones across projects, tying them into AI‑based safety and training systems that support 40‑language safety briefings.Digital twins become operational control rooms for buildings and AI data centresJacobs targets gigawatt-scale AI data centres with lifecycle twins: Dallas‑based Jacobs released a digital twin solution built on NVIDIA Omniverse DSX that lets owners simulate compute, power, cooling and airflow for gigawatt‑scale AI data centres, with secure integration from on‑prem power to indoor airflow models.Vertiv and NVIDIA publish AI factory blueprints with digital twins at the core: Vertiv introduced its OneCore digital‑twin‑based modular data‑centre platform, claiming up to 50% faster deployment and 25% lower total cost of ownership, while NVIDIA's Vera Rubin DSX reference design and Omniverse DSX Blueprint provide a common architecture for partners like Siemens, Schneider and Jacobs.Industrial and building twins tie into Omniverse stacks: Tata Consulting Engineers launched a cognitive digital‑twin platform for manufacturing, energy and infrastructure using NVIDIA Omniverse, and Delta reported Omniverse‑based building simulations that can improve energy savings potential by up to 20%.Commercial real estate twins focus on energy and compliance: Jacobs' data‑centre work sits alongside building‑level offerings such as Prevu3D's mesh‑based retrofit twins, RevitGods' owner‑ready handover twins, and R‑Zero's Prospector AI, which now screens 32,000 US buildings with an estimated $3.8bn annual savings potential from energy optimisation and penalty avoidance.BIM tools absorb AI while practitioners debate data, ethics and skillsSnaptrude targets the conceptual phase with a graph-based model of buildings: In an interview with AEC Magazine, Snaptrude described its "Universal Graph Representation"—a data‑first model of spaces and relationships that underpins AI agents for zoning and space planning, claiming 60–70% time reductions in early‑stage design.Nemetschek's Allplan positions AI as part of a design‑to‑build stack: Allplan outlined AI‑enabled workflows spanning structural detailing through fabrication, and in a separate release highlighted adoption of its platform on complex dams, tunnels and high‑rise projects.Documentation and QA emerge as prime AI targets: An ArchDaily analysis of Avoice argued that generative tools are starting to restructure specifications, code research and documentation far more than glossy renderings, while AEC Magazine's Qonic article described BIM platforms evolving into continuous, AI‑assisted data validators.Practitioners see both opportunity and friction: On r/BIM, users like u/revitgods argued that data management skills are becoming central as owners raise data requirements "because of AI", while civil engineers on r/civilengineering and landscape architects on r/LandscapeArchitecture expressed concern over spammy AI queries, water‑hungry data centres, and efficiency gains accruing to shareholders rather than staff.Research tools for materials, damage detection and urban flow edge toward deploymentLow-cost 3D aggregate morphology for QA/QC: A photogrammetry study on marker‑based 3D reconstruction of aggregates showed that simple camera rigs plus markers can generate accurate 3D models of aggregate particles, enabling routine 3D shape inspection on quarries and sites without CT scanners.Unified crack and defect dataset for diverse surfaces: The StructDamage dataset aggregates about 78,000 images of cracks and surface defects across nine material types (including road, pavement, deck and concrete) and reports up to 98.62% accuracy with DenseNet201, offering a strong training baseline for inspection tools.Open CFD dataset for wind and comfort studies around buildings: UrbanFlow‑3K provides 3,000 lattice‑Boltzmann simulations of wind flow around random 2D building layouts at multiple Reynolds numbers, giving architects and engineers a reusable benchmark for data‑driven urban microclimate and pollutant‑dispersion models.Metamodels and diffusion models for seismic and structural dynamics: New work on deep‑learning metamodels for nonlinear structural response under seismic loading and the SWAN seismic waveform dataset plus diffusion model for missing‑trace reconstruction points toward faster, data‑driven workflows for regional hazard analysis and performance‑based design.Twitter Recap"How OpenSpace is building the data platform for a $13 trillion industry's agentic future." — Jeevan KalanithiJeevan Kalanithi OpenSpace CEO Jeevan Kalanithi argues that in an era where AI agents can replicate most text-based workflows, the durable advantage for construction platforms will come from proprietary visual jobsite data—positioning OpenSpace's photo archives as a strategic asset rather than just a documentation tool.→"Snaptrude has been pouring its AI investment into the conceptual and schematic design phases where architects spend the most time and legacy tools offer the least help." — Martyn DayMartyn Day In AEC Magazine, Martyn Day details how Snaptrude's Universal Graph Representation lets AI agents reason over rooms, adjacencies and programmes rather than just geometry, reflecting a broader shift toward data-first design platforms in early phases.→"In AEC the pace of change is accelerating. It took millennia to move from physical to digital drawings, then two decades to fully embrace BIM." — Martyn DayMartyn Day Day's separate piece on "the agentic future of BIM" frames current work by at least six BIM startups as a race to build an operating system where solver-style agents can continuously coordinate multidisciplinary models, rather than today's file-based exchanges.→"The BIM platform is evolving into an intelligent system that continuously validates models, reducing errors and saving time." — Erik de KeyserErik de Keyser describes Qonic's vision of BIM software that runs ongoing classification, parameter checks and standards compliance in the background, signalling that much of AI's near-term impact will be in invisible data hygiene rather than flashy design generation.→"AI-Enabled Digital Twins in the Built Environment: A Bibliometric Review of Applications, Trends, and Future Directions." — Fangyu GuoFangyu Guo A review led by Fangyu Guo analysed 316 papers on AI-enabled digital twins for the built environment from 2015–2025, finding rapid growth but persistent fragmentation across tools and data models—evidence that standards and integration remain major barriers to portfolio-scale adoption.→"Digital construction leaders are redefining infrastructure delivery through AI-driven digital twins and connected data ecosystems." — BiD AdminBiD Admin Build in Digital's overview of AI plus digital twin strategies in infrastructure notes that owners are starting to treat twin platforms as resilience tools for anticipating disruption and optimising asset performance, not just as visual dashboards.→"Global AI Secures Enterprise Deployment of Agentic AI Products to Automate Regulatory Compliance in Building Design and Construction" — Global AI, Inc.Global AI, Inc. Global AI announced that a European architecture firm is using its agentic system to check designs against complex safety standards before submission, signalling that code compliance is becoming an early proving ground for autonomous agents in AEC.→"A new robotics startup focused on industrial labor automation has raised $52 million to accelerate deployment of physical AI systems designed for some of the most demanding jobs in modern infrastructure." — Rachel WhitmanRachel Whitman Reporting in RobotsBeat, Rachel Whitman shows how RoboForce's TITAN platform targets solar construction, logistics and mining, underlining that investor attention is shifting from warehouse automation toward robots that can survive harsh, outdoor infrastructure work.→Reddit Recapr/civilengineering RecapBe awareby u/Vinca1is (Activity: 100 comments)Civil engineer u/Vinca1is warned peers about a surge of low‑effort questions and AI‑generated prompts asking for process descriptions, arguing that firms are already asking consultants to "confirm what AI said" in public town halls and project work. Many respondents reported clients sending AI‑drafted memos or specifications that turned out to be technically wrong, forcing engineers to charge extra or re‑write work product from scratch.u/Vinca1is — They described having to answer town‑hall questions about AI outputs, underscoring that engineers—not AI—still carry legal responsibility for design advice.u/ORD_Underdog — A consultant commented that any client asking to "confirm AI" would be billed a supplement, highlighting the extra liability engineers see in validating chatbot results.r/BIM RecapWhat skills will be most valuable for BIM professionals in the next 5 years?by u/qpacademy (Activity: 21 comments)In a discussion on future BIM skills, u/qpacademy asked whether automation and digital twins make scripting essential; replies suggested the biggest shift will be toward data management, communication, and platform interoperability rather than just more Revit tricks. Commenters expect owners to tighten information requirements as they see AI potential, forcing BIM teams to clean schemas, enforce naming, and be able to explain their data models to both humans and machines.u/revitgods — They argued that "data management" is becoming core because owners increasingly understand why clean data matters for automation and analytics.u/Dawn_Piano — One user noted that limitations in Autodesk's API—like not being able to customise linked view settings—still protect some manual coordination work from automation.Digital twins for buildings: hype or reality?by u/Far-Cash-51 (Activity: 21 comments)A thread by u/Far-Cash-51 asked whether full building digital twins are feasible today given fragmented BMS, BIM and CAFM systems, and whether schemas like Brick or IFC really cut integration effort; practitioners described limited real‑estate use and heavy manual mapping between assets and sensor IDs as ongoing bottlenecks. The overall sentiment was that point solutions—energy dashboards, fault detection, or CAFM integrations—are working better than fully unified, continuously updated twins at portfolio scale.University students built a full-lifecycle BIM data platform (ISO 19650 + Dynamo + ML + Digital Twin) — seeking feedbackby u/Only-You4424 (Activity: 19 comments)A Korean student team led by u/Only-You4424 presented an "Integrated Infrastructure Data Platform" that wires ISO 19650‑compliant BIM data from Dynamo into construction dashboards, ML‑based risk analytics, and a simple digital‑twin view, then asked r/BIM for professional feedback. Commenters praised the ambition but cautioned about data‑entry burden and integration with real‑world contractor workflows, echoing industry concerns that process change, not algorithms, is the hardest part of lifecycle BIM.r/ArchiCAD RecapI built an AI coding agent for Revit. Would ArchiCAD users want one too?by u/Archia_H (Activity: 30 comments)South Korean BIM engineer u/Archia_H described building a free AI assistant that reads the Revit/Dynamo API, diagnoses graph errors, and writes code in‑app, then polled ArchiCAD users on whether a similar tool—likely using the tAPIr add‑on—would be useful. Responses showed strong interest from ArchiCAD users who currently rely on scattered scripts, reinforcing that in‑context assistants for coding and automation are a concrete demand rather than a novelty.I built a quick MVP of the ArchiCAD AI Assistant (Demo inside) + Need your helpby u/Archia_H (Activity: 1 comments)Two days later u/Archia_H returned with an MVP ArchiCAD assistant built on tAPIr and Claude Opus 4.5 that can create slabs, columns, objects and zones and edit their properties via natural‑language prompts, but only in single steps and with API limits on more complex operations. The post invited testers on macOS and Windows, signalling how fast individual practitioners can now turn forum interest into working AI prototypes that sit directly inside design tools.r/LandscapeArchitecture RecapTo the LA's justifying Ai useby u/[deleted] (Activity: 111 comments)An anonymous practitioner on r/LandscapeArchitecture argued that data‑centre water consumption and energy use make current AI tools incompatible with a profession tasked with sustainable design, calling AI "not regulated properly" and accusing promoters of shifting value from labour to the "1%." The thread sparked debate over whether avoiding AI on ethical grounds is realistic, or whether the priority should be lobbying for greener infrastructure and fair workplace policies around automation.u/carlyfries33 — In a related thread, they criticised the "AI brings efficiencies" argument, pointing out that productivity gains often flow to shareholders while staff simply face higher expectations and unchanged pay.r/estimators Recapfuture of estimating?by u/quelowque (Activity: 87 comments)A junior estimator on r/estimators asked whether AI would take over their job; most respondents felt quantity takeoff automation will grow, but real estimating still depends on reading poor drawings, judging site constraints, and pricing ambiguous risks. Several users reported trying current tools on storm profiles or PDFs and finding that errors and edge cases still forced full manual review, suggesting that AI is entering workflows as a helper rather than a replacement.u/TheNamesMacGyver — One estimator quipped that "until AI can interpret the shit drawings that engineers are putting out, our jobs are safe," capturing common frustration with upstream documentation quality.u/icecreamtruck88 — They suggested that AI will first generate clearer drawings, which would then enable more reliable automated takeoffs downstream.News RecapHVAC‑focused startup Rebar raised $14m in Series A funding to expand its computer‑vision platform that reads blueprints, identifies HVAC equipment, and generates bills of materials and quotes 60–70% faster than manual workflows, with users reporting higher bid volume and win rates. (Ventureburn)→New Haven startup LeanCon secured $6m to build AI tools that condense months‑long preconstruction planning for large projects into roughly seven minutes by automatically generating schedules, manpower allocations and cost scenarios. (Hartford Business Journal)→A roundup in Construction Dive reported that six contech firms—including AI estimation platform XBuild and safety monitoring vendor Sensera—have raised a combined $126m so far in 2026, with investors concentrating on tools for AI‑based estimating, jobsite reality capture, and safety analytics. (Construction Dive)→Australian‑founded Scopey Onsite raised about €523,000 pre‑seed after relocating to Ireland, offering an AI platform that turns WhatsApp messages and voice notes from sites into structured, searchable records to reduce disputes and strengthen evidentiary documentation. (Startup Daily)→ICON launched its Titan programme, selling multi‑storey capable robotic 3D concrete printers to builders and claiming it can deliver wall systems at roughly $20 per square foot—about 40% below US averages for conventional wall construction. (ICON)→UK startup AUAR was profiled for its use of AI‑assisted robotic micro‑factories that locally prefabricate modular timber components for housing, reporting up to 75% labour reduction and 40% cost cuts relative to traditional construction while allowing site‑specific customisation. (AI CRE Tools)→US firm FrameTec detailed a robotic plant in Arizona that produces pre‑cut, pre‑marked framing systems for detached homes, using Swedish robots to cut, frame, sheath and insulate panels with near‑zero wood waste and capacity for roughly 3,500 homes per year. (HousingWire)→Birmingham‑based City Detect raised $13m in Series A funding to scale its truck‑mounted vision system, which scans streets for graffiti, illegal dumping and building defects so US cities can systematically track blight and code issues without manual surveys. (TechCrunch)→New ResearchStructDamage: A Large Scale Unified Crack and Surface Defect Dataset for Robust Structural Damage Detection— Ijaz et al. [cs.CV]Ijaz et al. compiled StructDamage, a dataset of about 78,000 images of cracks and surface defects across nine materials (walls, roads, decks, concrete and more), and showed that modern CNNs can classify defect types with up to 98.6% accuracy—providing a strong benchmark for training inspection tools for bridges, pavements and facades.Marker-Based 3D Reconstruction of Aggregates with a Comparative Analysis of 2D and 3D Morphologies— Huang et al. [cs.CV, cs.AI, eess.IV]Huang et al. proposed a low‑cost photogrammetry workflow that uses simple cameras and markers to reconstruct 3D aggregate particles and quantify shape, finding large differences between 2D and 3D morphology metrics and enabling more accurate QA/QC for concrete and pavement materials without CT scanners.UrbanFlow-3K: A Dataset of 3,000 Lattice-Boltzmann Simulations of Random Building Layouts— Lee et al. [physics.flu-dyn]Lee et al. released UrbanFlow‑3K, a set of 3,000 2D CFD simulations of wind flow around random building layouts at several Reynolds numbers, giving a reusable benchmark for training and validating ML models for urban wind comfort, pollution and natural ventilation studies.Deep Learning-Based Metamodeling of Nonlinear Stochastic Dynamic Systems under Parametric and Predictive Uncertainty— Atila & Spence [cs.LG]Atila and Spence presented metamodels that combine MLPs, message‑passing neural networks and LSTMs to approximate the seismic response of systems up to a 37‑storey nonlinear steel moment frame under uncertain ground motions and parameters, offering faster surrogates for performance‑based design with quantified prediction uncertainty.Training a generalizable diffusion model for seismic data processing using a large-scale open-source waveform dataset— Gong et al. [physics.geo-ph]Gong et al. introduced the SWAN seismic waveform dataset and trained a diffusion model for missing‑trace reconstruction that outperforms existing deep‑learning and physics‑based baselines, pointing toward more robust, data‑driven processing pipelines for exploration and earthquake imaging.GreenPhase: A Green Learning Approach for Earthquake Phase Picking— Wu et al. [physics.geo-ph, cs.AI, cs.LG]Wu et al. proposed GreenPhase, a feed‑forward, multi‑resolution model for detecting earthquakes and picking P/S arrivals that achieves F1 scores of 0.98–1.0 on the STEAD dataset while cutting inference FLOPs by about 83% versus prior deep models, making large‑scale, energy‑efficient seismic monitoring more practical for infrastructure networks. --- New:AI in the Built World — March 2026New:AI in the Built World — March 2026AI in the Built World — March 2026Our productsNomic transforms decades of unstructured data into organized, AI-ready knowledge. Empower your teams with domain-specific agentic workflows that accelerate every project, workflow, and decision across your firm's institutional knowledge.Nomic PlatformOur turnkey platform that accelerates your engineers and designers with domain-specific agentic workflows across your firm's knowledge.Developer APIAccess our domain-specific models for search, document understanding and drawing parsing to acclerate your firm's internal AI solutions.Use CasesSee how Nomic automates code compliance, drawing review, submittal review, and firm-wide search. Ground AI in your drawings, specs, and project data to catch issues before construction and accelerate every workflow.Security & ComplianceSecurity is fundamental to everything we build at NomicLast updated: September 30, 2025For security-related questions or to report vulnerabilities: security@nomic.aiCertifications and Third-Party AssessmentsNomic is committed to maintaining the highest security standards and undergoes regular third-party assessments to validate our security posture.SOC 2 Type II Certified: Nomic is SOC 2 Type II certified. Visit our Security Center to request a copy of our compliance report and other security documentation.Penetration Testing: We commit to conducting at least annual penetration testing by reputable third parties. External penetration test reports and other security assessments are available through our Security Center.Vendor Reviews: All security documentation, compliance reports, and vendor assessment materials can be accessed at security.nomic.ai. You will be asked to sign an NDA before being granted access.Infrastructure SecurityNomic is committed to maintaining the highest security standards and undergoes regular third-party assessments to validate our security posture.Cloud InfrastructurePrimary hosting on AWS with high availabilityData centers in US, Europe, and AsiaEncrypted data in transit and at restRegular security patches and updatesAccess ControlsMulti-factor authentication requiredLeast-privilege access principlesEncrypted data in transit and at restRegular security patches and updatesSubprocessorsWe rely on the following subprocessors to deliver our services. Data handling varies by service; please refer to our trust center for a complete overview.AWSSEES AND STORES YOUR FILESWe use AWS for our primary cloud hosting platform. All instances of Nomic are managed on AWS.AnthropicSEES YOUR FILESWe use Anthropic for AI responses. We have a zero data retention agreement with Anthropic.Google Cloud Vertex APISEES YOUR FILESWe rely on some Gemini models offered over Google Cloud's Vertex API to give AI responses. We have a zero data retention agreement with Vertex.Modal LabsSEES YOUR FILESWe use Modal for serverless infrastructure and compute to infer our custom models.SentrySEES NO FILESWe use Sentry to monitor errors and performance in our app. File data is never explicitly sent, but may show up in reported errors. Data from BYOC deployments never reaches Sentry.DatadogSEES NO FILESWe use Datadog to monitor errors and performance in our app. File data is never explicitly sent, but may show up in reported errors. Data from BYOC deployments never reaches Datadog.Google AnalyticsSEES NO FILESProvides analytics for web presence.StripeSEES NO FILESWe use Stripe for billing.WorkOSSEES NO FILESWe use WorkOS for enterprise authentication and single sign-on (SSO).MixpanelSEES NO FILESWe use Mixpanel for product analytics and user behavior tracking.LoopsSEES NO FILESWe use Loops for email communications and notifications.Geographic note: None of our infrastructure is located in China, and we do not directly use any Chinese companies as subprocessors.AI RequestsWhen you use Nomic's AI features, we take great care to protect your data throughout the AI processing pipeline.Data ProcessingAll AI requests are processed through our secure infrastructureData is encrypted in transit to AI model providersWe maintain zero data retention agreements with AI providersContext data is minimized to what's necessary for processingAI requests may include context from your files, conversation history, and relevant file snippets. This data is sent to our infrastructure and then to appropriate AI model providers (OpenAI, Anthropic, etc.) under strict data protection agreements.Data and File IndexingAll files stored with Nomic are indexed using Nomic Platform infrastructure. When your data isn't being processed, it is stored only in your Nomic instance and is encrypted at rest.Indexing works by sending files or folders of files to the Nomic Platform embedding and parsing endpoints, where they are processed to generate searchable embeddings. Files are encrypted during transit and are only temporarily accessed for processing.Deployment Options and DataNomic offers flexible deployment options to meet different security and compliance requirements. Each option provides different levels of data control and processing locations.Nomic-Managed (Cloud)Our standard cloud offering where Nomic manages all infrastructure and operations.All data is stored in Nomic managed AWS infrastructureData is processed through the Nomic PlatformProcessing involves our sub-processors as listed aboveFastest deployment with minimal setup requiredBring-Your-Own-Cloud (BYOC)Deploy Nomic within your own cloud environment while leveraging our platform services.All data is stored in your cloud environmentData is processed by Nomic Platform AWS infrastructureProcessing involves our sub-processors for AI operationsEnhanced data residency controlOn-Premise (Custom)Fully isolated deployment within your own infrastructure with custom agreements.Complete data isolation within your environmentCustom processing and sub-processor agreementsTailored security and compliance controlsContact our team for custom deployment optionsNeed a Custom Deployment?For enterprise customers requiring specific compliance, data residency, or security controls, we offer custom deployment options. Contact our team at sales@nomic.ai to discuss your requirements.Account DeletionYou have full control over your data and can delete your account and associated data at any time.Data Deletion ProcessAccount deletion can be initiated from your settings dashboardAll personal data, stored and indexed files are immediately deletedComplete data removal guaranteed within 30 daysBackups are automatically purged within retention periodNote: If your data was used in model training (opt-in), existing trained models will not be immediately retrained, but future model training will not include your deleted data.Vulnerability DisclosuresWe take security vulnerabilities seriously and encourage responsible disclosure from the security community.Report a VulnerabilityIf you discover a security vulnerability, please report it to: security@nomic.aiResponse TimelineAcknowledgment within 5 business daysComplete data removal guaranteed within 30 daysRegular updates throughout investigationPublic disclosure after fix deploymentResponsible Disclosure GuidelinesProvide detailed vulnerability informationAllow reasonable time for fix developmentAvoid accessing or modifying user dataDo not perform testing that degrades service