Learn how Ideate StyleManager, Ideate Explorer, and Ideate BIMLink help construction teams eliminate Revit inefficiencies — from unmanaged styles and hidden elements to manual data errors — with automated QA/QC workflows and bulk parameter editing that saves hours on every project.

How Ideate Software Is Helping Teams Improve Revit Data Management, QA/QC and Automation

In this article, Kestrel Labs explains how AI powered building code compliance can help architects check code directly inside the BIM model, reducing manual review, rework, and permitting delays. The conversation explores how computable building codes, Revit based workflows, and traceable compliance results can support faster design decisions, stronger code confidence, and a more efficient future for AEC permitting.

How Kestrel Labs Is Bringing Code Compliance Inside the BIM Model

<figure class="image"><img src="https://aecplustech-files.s3.amazonaws.com/blog/9198a462-3995-42b2-ad2b-bddb08f3649e.png" alt="Ichi helps architecture teams bring code research earlier into the design process, turning complex compliance questions into faster, cited answers." width="1080">
<figcaption spellcheck="false" data-qb-tmp-id="lt-987372" data-gramm="false">Fig. 1) Ichi helps architecture teams bring code research earlier into the design process, turning complex compliance questions into faster, cited answers.</figcaption>
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We sat down with Chinmayi Suri, Architectural Designer at DGA, and Brandon Levey, Founder and CEO of <a href="https://www.ichiplan.com/" target="_blank" rel="noopener">Ichi</a>, to learn how the two are working at the intersection of architecture and AI to tackle some of the profession's most time-consuming pain points: building code compliance, QA/QC, and the institutional knowledge that disappears when senior staff retire.&nbsp;
On a recent corporate laboratory project, the team at DGA, a Bay Area architecture firm specializing in life sciences and advanced manufacturing facilities, hit a familiar wall. Their mechanical scope carried a $250,000 line item for fire dampers, and the client wanted that cost justified before signing off. The architects suspected the dampers were not actually required by code for this specific occupancy and assembly configuration, but confirming it the usual way meant setting up a meeting, a senior architect pulling code books, cross-referencing amendments, and chasing answers across the firm. On a project already under deadline pressure, the budget question was at risk of being closed out without ever being properly answered.
Instead, they ran the question through Ichi, an AI platform built specifically for navigating building code complexity. Within seconds they had a cited answer from the code confirming the dampers were not required. The line item came out. The client saved $250,000.
While this is a relatively small sum for highly complex projects, this is just a single example and without that quick verification, the money would not have stayed in the project. It would have come out of finishes, or contingency, or somewhere else the team would rather not have touched. That is the part worth paying attention to. Code research used to be a slow, senior-architect-only task that often gets skipped or rushed under deadline pressure. When it stops being slow, it starts shaping budgets in real time.
<h3 dir="ltr">How Manual Code Work Is Costing You</h3>
Code compliance is one of those parts of architectural practice that almost no one celebrates. It is also one of the most expensive parts of the job, in hours if not always in dollars.
Chinmayi Suri, an architectural designer at DGA, works on cGMP pharmaceutical, life science, and manufacturing facilities that often run past 100,000 square feet and carry layered regulatory requirements. She describes the existing process bluntly. Navigating ADA, fire codes, and industry-specific regulations falls on the most senior architects, the ones who have memorized chunks of code over years of repetition. It is institutional knowledge stored in human heads, queried one Slack message or in-person conversation at a time.
That creates two problems. The first is throughput. Senior staff become the bottleneck on every code question, which pulls them away from the design work they were hired to do. The second is timing. Code checking traditionally happens after the design is mostly finished, so compliance issues surface late, when changes are expensive and the schedule is already tight.
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"This stemmed from frustration really. I was sitting on my computer saying this should not take so long, and that's kind of actually where this all started."&nbsp;&mdash; Chinmayi Suri, DGA
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Her original interest in AI tools came out of that frustration, not out of any particular enthusiasm for AI as a concept.
<h3 dir="ltr">Why Generic AI Tools Fall Short</h3>
The obvious question for any architect frustrated with code research is: Can&rsquo;t ChatGPT do the job? The answer, so far, has been no, and the reasons are useful to understand.
Brandon Levey, founder and CEO of Ichi, comes from a 20-year tech background that includes work at a Department of Energy national lab and running R&amp;D teams at Square. He grew up in his father's HVAC and sheet metal business, which is partly why he ended up building software for AEC. He breaks the gap down into three things.
Accuracy on compliance codes has to be very high. Architects stamp their drawings, and a model that produces plausible-sounding language without citing the exact code section is worse than no answer at all. The documents themselves are also unusually large. A spec package can run 6,000 pages. A jurisdiction comment letter can run 60 pages with 300 individual items. General-purpose tools are not built to parse and cross-reference at that scale with any reliability. And, the answers always depend on the context that lives inside the firm: what counts as a standard detail, which assemblies the client has used before, what the senior architect on this project type would flag.
Chinmayi put it more directly. Generic AI tools require the user to already have deep domain knowledge to know what to ask and to verify what comes back. That defeats the purpose. The point of an AI assistant for code work is to lower the barrier, not require an expert to operate it.
<figure class="image"><img src="https://aecplustech-files.s3.amazonaws.com/blog/d43e9371-5c56-45b3-b46b-0264708472ca.png" alt="Ichi&rsquo;s agent based workflow helps teams organize code review, QA/QC, and consultant coordination tasks in a structured review board." width="1080"><canvas class="qb-highlighter__canvas" style="display: none; top: 1px !important; left: 200px !important;" width="47" height="22"></canvas>
<figcaption spellcheck="false" data-qb-tmp-id="lt-193248" data-gramm="false">Fig. 2) Ichi&rsquo;s agent based workflow helps teams organize code review, QA/QC, and consultant coordination tasks in a structured review board.</figcaption>
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<h3 dir="ltr">What Agentic Actually Means Here</h3>
Ichi's product is built around what Brandon calls an agentic approach. The term gets used loosely, so it is worth being specific.
In Ichi's case it means that when users ask Ichi a question they aren&rsquo;t interacting with a single LLM, but rather many different agents that are specialized in specific tasks. This approach helps the platform guide users when they do not know exactly what to ask,check its own work, and complete longer, more complex tasks.
A simple example. An architect asks a question about an auditorium in California. A generic chatbot returns a generic answer. Ichi instead asks clarifying questions about occupancy load and square footage, because those variables fundamentally change which code sections apply. That guided flow matters in code work, where a wrong assumption upstream invalidates everything downstream.
The error-checking piece is more technical but arguably more important.
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"Any type of long-running or long horizon task in AI is going to have errors in it. The way that we evolve through that, a lot of the agentic approaches are by using multiple AI agents to check its work."&nbsp;&mdash; Brandon Levey, founder and CEO, Ichi
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In practice that means when Ichi pulls code references from a 60-page comment letter or runs a feasibility study, separate agents are reviewing the output for inconsistencies and gaps before the architect sees it. That internal cross-check is what makes the difference between a curiosity and something architects will actually put their name on.
Want to hear the full discussion?<a href="https://www.youtube.com/watch?v=rsm_sSv2urI" target="_blank" rel="noopener"> Watch the complete panel conversation with Chinmayi Suri and Brandon Levey on YouTube.&nbsp;</a>
<h3 dir="ltr">Reducing the Work Architects Enjoy Least&nbsp;</h3>
The most concrete value Ichi delivers shows up in the workflows least enjoyed by architects.
Comment letters are the clearest case. When a jurisdiction returns a plan check, the architect's first job is translation: turning a long PDF into a structured spreadsheet that maps each comment to the relevant code reference, assigns it to the right consultant, and drafts an initial response. Brandon described one user who spent five to eight hours turning a 60-page letter with 300 comments into a workable spreadsheet. With Ichi, that drops to around 20 minutes.
Feasibility and zoning studies follow a similar pattern. A study that previously took two to four hours of senior staff time becomes a 20-minute guided workflow, producing an exportable document with code references already in place.
On the documentation side, the savings come less from automation than from triage. Chinmayi pointed to 700-page document sets where the real value is having AI do the first pass, locating where specific notes or drawing discrepancies live. The architect still verifies and decides. They just no longer have to read every line to find the three that matter.
The work that consumes the most time in an architecture firm is rarely the work that draws people into the profession. Cutting hours out of that work is where most firms will see the first real return.
<div><a href="https://www.youtube.com/watch?v=gSKu4e2Qg3s" target="_blank" rel="noopener">Watch Ichi in action: See how the platform supports zoning, design, permitting, and inspections across the full project lifecycle.</a></div>
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<h3 dir="ltr"><iframe style="display: table; margin-left: auto; margin-right: auto; width: 709px; height: 400px;" src="https://www.youtube.com/embed/gSKu4e2Qg3s" width="709" height="400" allowfullscreen="allowfullscreen"></iframe></h3>
<h3 dir="ltr">Capturing the Knowledge Before It Walks Out the Door</h3>
One of the more interesting features in Ichi is what the platform calls Memory. It captures the institutional knowledge of senior staff, the kind of judgment and pattern recognition that takes decades to build and tends to leave the firm when those people retire.
Chinmayi described how the idea took shape at DGA.
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"I wish we could put Mark Davis into a notebook LLM."&nbsp;&mdash; Chinmayi Suri, on DGA principal Mark Davis
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The line is partly a joke about a specific colleague, but it captures something the AEC industry has struggled with for a long time. Knowledge transfer from senior to junior staff is mostly accidental, dependent on proximity, mentorship, and time.
Memory works by ingesting organizational standards, past project notes, and client specifications so the AI's answers reflect the firm's actual way of working, not a generic best practice. DGA has started using it as a training tool, exposing younger architects not just to answers but to the prompts and reasoning senior staff use to get there. The platform already syncs directly with OneDrive and soon will with Google Drive, removing another friction point for firms with messy document storage.
This is also where the security question gets practical. Ichi does not train its models on customer data. For firms handling sensitive client information, particularly in life sciences or government work, that distinction is the difference between adoption and a hard no from legal.
<h3 dir="ltr">The Barriers Are Real, but Shrinking</h3>
Adoption is rarely about whether a tool works. It is about whether a firm can afford the time to find out.
Chinmayi named three barriers: time, trust, and security. Time, because evaluating new software is itself a senior-staff task that competes with billable work. Trust, because architects are personally liable for what they sign. Security, because client data cannot be pasted into a public chatbot.
The trust question tends to dissolve through small wins. Firms that successfully adopt AI usually start with a single project, often a smaller one, and prove the tool against a known answer. Once the output matches what a senior architect would have produced, the conversation shifts from whether the tool is reliable to which workflow it should touch next.
The fear of replacement also fades quickly once people actually use the tools. The intelligent part of the work, as Chinmayi pointed out, still comes from the humans using it. AI takes the repetitive parts. It does not make decisions, and it does not stamp drawings.
<h3 dir="ltr">The New Baseline</h3>
The shift Brandon describes has happened fast. In 2024, the conversation in architecture was still about whether AI was real. By late 2025 it had moved to how to implement it. Firms are now being asked in client interviews and RFP responses what their AI strategy is. Within the next year, not having a clear answer will start to count against firms competing for work.
Chinmayi frames it in the only terms that really matter to working architects.
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"You have to start using it. We have to start thinking about using it in our workflow. It is going to become as prevalent to us at some point as Revit is."&nbsp;&mdash; Chinmayi Suri, DGA
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The shift from hand drafting to CAD took roughly a decade. CAD to BIM took another. Each one looked optional at the start and obvious by the end. The shift now underway, from manual code research to agentic compliance tools, is following the same curve. 
Firms picking up the tools now are buying themselves time to figure out how to use them well before that becomes a job requirement rather than a competitive edge.
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<img src="https://aecplustech-files.s3.amazonaws.com/blog/1de0bf31-afe3-430b-82bc-a14f24bea959.png" alt="" width="160" height="160">
Editor's Note
This article is based on an aec+tech Expert Talk, Navigating Code Complexity with AI, featuring Chinmayi Suri of DGA Architects and Brandon Levey of Ichi. The conversation explored why building code review remains one of the most manual and time consuming parts of architectural practice, and how AI can help project teams navigate code requirements, reduce repetitive review work, and make more informed design decisions.
Learn more on Ichi&rsquo;s product page: <a href="../../tools/ichi" target="_blank" rel="noopener">https://www.aecplustech.com/tools/ichi</a>
Watch the full conversation here: <a href="https://www.youtube.com/watch?v=rsm_sSv2urI&amp;t=168s" target="_blank" rel="noopener">https://www.youtube.com/watch?v=rsm_sSv2urI&amp;t=168s</a>
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In this article, we explore how DGA Architects is using Ichi’s agentic AI platform to rethink building code compliance, QA/QC, and time consuming review workflows. Through a recent project example, the article shows how faster, cited code research helped the team remove a $250K fire damper line item and make better decisions earlier. The piece also looks at how AI can capture institutional knowledge, support junior teams, and become a new baseline for architecture firms as client expectations evolve.

How DGA Used Ichi’s Agentic AI to Rethink Code Compliance

<img src="https://aecplustech-files.s3.amazonaws.com/blog/57eee85d-cd2f-4ffd-842d-2e8a109ebfa4.jpg" alt="" width="1080">The difference between a concept and a market leader lies in the ecosystem in which they operate. If your startup is building the next generation of automation, artificial intelligence, or spatial intelligence for the built environment, this is your final opportunity to apply for the 2026 Trimble 0&ndash;60 Challenge. Applications officially close on May 22, 2026.
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Tech startups in the Architecture, Engineering, Construction, and Owner-Operator (AECO) sector often struggle to gain traction, not because they lack innovative ideas, but because they lack real-world domain context and access to established workflows. The Trimble 0&ndash;60 Challenge is a rigorous, equity-free 12-week sprint designed to help early-stage founders bridge the gap between digital concepts and physical workflows, moving solutions from prototype to real-world deployment.
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Why This Accelerator Is Different
General-purpose accelerators can give you cloud infrastructure, but the Trimble 0&ndash;60 Challenge gives you the industry. Recently named the 2025 Accelerator Program of the Year by BuiltWorlds, this program provides what standard accelerators cannot: direct integration with the world's most widely deployed AECO platforms.
Startups selected for the 2026 cohort will receive:
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Deep Platform Integration: Build directly on Trimble Connect, SketchUp, Tekla, and ProjectSight, or integrate your computer vision and autonomy solutions with Trimble's GNSS receivers, total stations, and laser scanners.
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Expert Mentorship: Get weekly guidance from Trimble platform engineers, product teams, and Trimble Ventures advisors who have spent their careers inside construction workflows.
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Prizes and Market Distribution: Compete for non-dilutive cash prizes and gain a direct path to verified customers via a listing on the Trimble Marketplace.
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Demo Day at Dimensions: Pitch your fully functioning minimum viable product (MVP) to 5,000 construction professionals, buyers, and investors live at Trimble Dimensions in Las Vegas from November 9&ndash;11, 2026.
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Proven Commercial Outcomes
The 12-week roadmap focuses entirely on rapid development and commercial validation. As Simon Boyles, founder of ScaffPlan, noted after his cohort experience, "We finished the program with a brand new, fully functioning application ready to be commercialized". Similarly, the 2025 winner, Datagrid, successfully built an agentic AI tool that integrates directly with Trimble solutions and over 100 enterprise systems, which is now live on the Trimble Marketplace.
Who Should Apply
The program is actively seeking early-stage startups and developers building solutions in three core categories:
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Connected Data: Solutions that facilitate the seamless flow, management, and interoperability of data across the project lifecycle.
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Connected Design: Tools that enhance modeling, simulation, and design workflows to improve accuracy and efficiency.
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Connected Field or Jobsite: Technologies that optimize real-world execution through hardware-to-software synergy and precision technology.
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Key Dates for 2026:
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Applications Close: May 22, 2026
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Finalists Selected: June 12, 2026
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2-Day Kick-Off Event: June 16&ndash;17, 2026 (Westminster, CO)
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Dimensions Demo Day: November 9&ndash;11, 2026 (Las Vegas, NV)
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Do not watch the future of construction technology from the sidelines. If you are ready to scale your technology within the Trimble ecosystem, you must submit your application before the portal closes.
 Apply before midnight on May 22, 2026. <a href="https://www.trimble.com/en/our-company/events/dimensions/experiences/0-60-challenge?utm_source=influencer&amp;utm_medium=article&amp;utm_campaign=TRB-26Q1-GLOB-LE-Nov-09-Trimble-0-60&amp;utm_term=Aec+tech-campaign%20https://www.trimble.com/en/our-company/events/dimensions/experiences/0-60-challenge?utm_source=linkedin&amp;utm_medium=paid-ad&amp;utm_campaign=TRB-26Q1-GLOB-LE-Nov-09-Trimble-0-60&amp;utm_content=startups" target="_blank" rel="noopener">Learn more.</a></div>
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The 2026 Trimble 0–60 Challenge, an equity-free 12-week accelerator for early-stage AEC startups in automation, AI, and spatial intelligence, closes applications on May 22, 2026. Selected founders gain direct integration with Trimble platforms, expert mentorship, non-dilutive cash prizes, and a Demo Day slot at Trimble Dimensions in Las Vegas.

Last Call: Accelerate Your AEC Startup with the 2026 Trimble 0–60 Challenge

Graebert’s ARES Trinity 2027 helps AEC teams automate BIM-to-DWG documentation from Revit, IFC, and Autodesk Forma workflows. It reduces manual drawing updates, supports cloud automation, preserves CAD details, improves feedback loops, and introduces ARES Neo for Revit-based drawing validation.

BIM Has the Intelligence. Why Is Revit Documentation Still Manual?

Melt Takeoff is built for estimators who need takeoffs that are not just fast, but truly bid-ready and reliable. Unlike fully automated takeoff software that only measures what&rsquo;s drawn, Melt Takeoff is designed around a simple truth: winning bids requires accounting for what&rsquo;s missing from plans &mdash; the scope gaps, incomplete details, coordination conflicts, and implied work that experienced estimators catch through judgment.
Melt Takeoff combines AI vision with expert estimator validation to deliver takeoffs with both speed and accuracy. The workflow is simple: upload plans, the AI measures and identifies scope, flags drawing/spec conflicts, adds allowances, and then senior estimators validate everything before delivery. This hybrid model is what makes Melt Takeoff dependable &mdash; AI accelerates measurement, but real estimators ensure completeness and constructability.
Key capabilities include:
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AI Vision measurement detecting 800+ building elements with counts, lengths, perimeters, areas, and volumes
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Automated scope recognition across architectural, structural, and MEP drawings
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Detailed allowances for scope not shown, added as itemized ROMs based on code requirements and real project benchmarks
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Drawing&ndash;spec conflict detection, with estimator-led resolution before it hits your bid
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Estimator-verified outputs, reviewed by onshore senior estimators with 20+ years of field experience, including delivery of multi-million to billion-dollar projects
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Flexible classification standards, supporting MasterFormat&reg;, UniFormat&reg;, or custom structures, so takeoffs drop directly into your estimating workflow
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Interactive takeoff output, with side-by-side plan review, traceable quantities, and clean Excel exports ready for pricing and bidding
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Melt Takeoff is designed for speed without sacrificing trust. Quantities aren&rsquo;t just dumped into a spreadsheet &mdash; they&rsquo;re delivered in a structured, reviewable format so teams can validate quickly, collaborate internally, and confidently move into bid submission. The output stays fully in your control: review, adjust, and export clean files without messy reformatting.
Melt Takeoff delivers a takeoff process that is 40% more accurate, 20% faster, and 10% cheaper, with turnaround times ranging from 2&ndash;3 days for small projects to 7&ndash;8 days for extra-large projects. The result is fewer missed scope items, stronger bid coverage, reduced risk of underbidding, and significantly more confidence in your numbers &mdash; so estimators can bid faster and win more work.

Melt Takeoff

InspectMind AI helps engineers review hundreds of pages of drawings in minutes instead of hours.
InspectMind AI is an AI-powered plan review and code compliance platform built for the architecture, engineering, and construction (AEC) industry. The platform automatically analyzes construction drawings, engineering plans, and technical documents to identify code violations, design inconsistencies, and constructability issues before projects move into permitting or construction.
Traditional plan review and QA processes require engineers, architects, and reviewers to manually analyze hundreds or thousands of pages of drawings. This process is time-consuming, expensive, and prone to human error. InspectMind AI accelerates this process by using advanced artificial intelligence models trained on construction drawings, building codes, and engineering standards.
Users upload their project plans directly to InspectMind, and the AI system analyzes the drawings sheet-by-sheet. The platform detects issues such as missing details, dimension conflicts, structural inconsistencies, accessibility violations, and code compliance risks. InspectMind then generates a structured report highlighting the detected issues along with visual references to the affected drawings.
The platform is designed for engineers, architects, developers, contractors, plan reviewers, and government agencies who want to reduce risk and improve design quality. By catching issues early in the design process, InspectMind helps teams avoid expensive rework, delays during permitting, and construction change orders.
InspectMind AI supports a wide range of building codes and standards including IBC, ADA, NFPA, and local jurisdictional codes. The system can analyze architectural drawings, structural plans, engineering reports, and construction documentation.
Typical workflow:
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Upload drawings Users upload their architectural or engineering plans directly into InspectMind.
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AI plan analysis The AI analyzes the full drawing set, identifies potential issues, and compares plans against applicable building codes.
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Review results Users receive a detailed issue report showing potential risks, code conflicts, and recommended corrections.
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InspectMind AI dramatically reduces manual review time, improves plan quality, and helps project teams move faster from design to construction.

InspectMind AI

Catch construction mistakes before they get built

AI-Powered Site Documentation: An InspectMIND Case Study

Featuring InspectMind AI at the AEC Innovate Conference

Featuring InspectMIND at the AEC Innovate Conference

Tools Leading the Way in the AEC+Tech Community

Using AI to Automate Building Inspections

Revit professionals keep their models clean and accurate with the Ideate Explorer Revit add-on software. When using this model browser, as opposed to a project browser, users decrease the time they spend on model management, increase the accuracy of their models, and reduce file space by: Finding and deleting incorrect, obsolete, and hidden problem items, including DWGs Finding, refining, and revising elements, even those excluded from the project browser Getting a full view of any Revit model and understanding all elements in the model Easily complying with standards Auditing and removing Revit warnings The following components included with the Autodesk Revit add-in, Ideate Explorer, provide additional ways to ensure model accuracy: Warnings Manager - Easily review and manage Revit warnings Navigate - Navigate through view-based elements within Revit&mdash;legends, schedules, sheets, and views&mdash;and quickly customize the browsing experience Query - Search on parameters, such as elements assigned a certain size or modeled as in-place families, sheets based on Issuance numbers, and dimensions that have been overridden Boost the Power of Ideate Explorer with Ideate Automation Ideate AutomationFree your staff to do more meaningful work by adding Ideate Automation to your subscription*. This scripting solution runs time-intensive tasks in the background, so staff can continue using the computers to perform higher value tasks. Not only does task automation free staff for higher level engineering and design work, but automation also has the benefit of ensuring that your task outcomes are highly predictable and standardized. The scripts can be run manually or scheduled to run at specific times, including at night. Example: Exporting Model Health Data With Ideate Automation, staff no longer needs to stop what they are doing so their computer can export critical information about the Revit project. Instead, the script will run in the background and deliver actionable data that can be used to increase project health. *Ideate Automation is included in the cost of an Ideate Software Enterprise subscription. Others can purchase it as a separate subscription. View subscription options or contact your account manager for more information. Attend FREE online training courses on all of our products. Visit our Events Page for details. &ldquo;Switching from an office with [Ideate] Explorer to without is a jarring experience. This app puts the filter tool in Revit to absolute shame - it is simply THE tool for finding elements in Revit models.&rdquo; ~Gavin Crump, BIM Guru

Precisely Manage Revit Models | Introduction to Ideate Explorer 

Ideate Query and its Value to All Revit Users

Ideate Explorer

Ideate Explorer and IdeateApps: Safely Moving Revit Content to Another Level 

BAMROC: Revolutionizing MEP and MEP vs. Structure Clash Resolution in the AEC Industry
BAMROC is the first AI-powered software in the AEC industry purpose-built to automate MEP (mechanical, electrical, and plumbing) and MEP vs. Structure clash resolution inside real-world BIM coordination workflows. Instead of spending countless hours manually reviewing clashes, modifying routes, and iterating on design options, BIM teams can now rely on BAMROC to act as an intelligent copilot that does the heavy lifting with remarkable speed and consistency.
At its core, BAMROC combines advanced AI-powered decision-making to understand how MEP services interact with structural and architectural elements. It doesn&rsquo;t just highlight problems, it actively proposes and applies optimized resolutions that respect design intent, space constraints, and constructability. This allows BIM MEP coordinators, designers, and engineers to move from reactive firefighting to proactive, high-value decision-making.
By streamlining a traditionally time-consuming and repetitive coordination stage, BAMROC helps projects meet the highest standards of accuracy, safety, and efficiency. It reduces the back-and-forth between disciplines, minimizes rework, and cuts down coordination cycles, ultimately supporting faster project delivery and more predictable outcomes. BIM professionals can focus on design quality, system performance, and client requirements instead of being stuck in manual clash-by-clash troubleshooting.
As an advanced BIM clash resolver, BAMROC analyzes design conflicts, explores multiple routing scenarios, and intelligently adjusts MEP systems to create a clash-free environment. The resolutions are generated with precision, preserving clearances, avoiding critical zones, and maintaining the logic of each system. This level of automation and reliability redefines what &ldquo;productive&rdquo; means in BIM MEP design and coordination.
Whether your team is handling dense plant rooms, complex high-rise cores, hospitals with strict service zoning, or large infrastructure projects under aggressive timelines, BAMROC ensures that BIM professionals can achieve lightning-fast coordination with uncompromising quality. Adopting BAMROC is not just a software upgrade, it is a shift toward smarter, AI-assisted project delivery and a more efficient, future-ready AEC workflow.

BAMROC

We lead the construction industry in quality control. No more stumbling blocks. Constructing flawless buildings is now easier than ever. Solibri Office imports building models from all major BIM software using the standardized IFC interface. It identifies potential problems early, preventing issues during construction and beyond, ensuring smooth project execution and long-term building performance. Solibri offers the most advanced and comprehensive library of customizable rule templates to tackle the most demanding quality assurance tasks. Choose from over 70+ predefined rule templates or customize your own to ensure no stone is left unturned. Go beyond clash detection and take your BIM quality to the next level. Solibri offers a wider range of checking capabilities than any other tool in the market: geometry, clearance, accessibility, information &amp; property checking, building code compliance, model revision, fire safety and more. Solibri understands your role and offers you a set of rules, classifications, and reporting templates that you can customize to match your company, project, local, or national needs. Use organizational tools like classifications and filters to streamline model checking, to takeoff quantities and data, or to visualize the BIM data/components in the model. Information takeoff allows you to extract quantities based on different criteria more easily. Share the information using your own customized Excel templates. Information takeoff allows you to extract quantities based on different criteria more easily. Share the information using your own customized Excel templates. Share project information and collaborate seamlessly and reliably through Solibri&rsquo;s integrations, BCF Live Connector and API. Automate tasks, such as running checks, exporting the identified issues as BCF files and creating presentations, and run them at a convenient time with Solibri Autorun. Easily integrate with your workflows: Solibri is founded on openBIM standards to foster interoperability and enable transparent, open cooperation among all project participants. This allows model authors and collaborators to connect Solibri's quality assurance capabilities with the tools of their choice.

Solibri Office

Eight categories reveal how AEC technology evolved on aec+tech.

Building Better with BIM QA: 2025 Interview with Solibri

Checking Revit Models in the Cloud with Solibri

Featuring Solibri at the AEC Innovate Conference

Solibri CheckPoint is a browser-based BIM model checking platform that helps architecture, engineering, and construction teams improve model quality, coordination, and compliance&mdash;without heavy installs or steep training. Open a browser, review Revit and IFC models side by side, run standardized QA/QC checks (clash detection, data/property rules, clearances), and manage issues in one collaborative workspace that fits your existing CDE and project tools.
<h2 data-start="758" data-end="783">Why Solibri CheckPoint</h2>
<ul data-start="784" data-end="1370">
<li style="font-size: 15px;" data-start="784" data-end="888">Zero install, fast adoption: Web-based model checking reduces IT overhead and speeds onboarding.</li>
<li style="font-size: 15px;" data-start="889" data-end="985">Native Revit &amp; IFC support: Review the same source data&mdash;no exports or lossy conversions.</li>
<li style="font-size: 15px;" data-start="986" data-end="1090">Standardized QA/QC: Enforce modeling standards with reusable rule sets, filters, and data views.</li>
<li style="font-size: 15px;" data-start="1091" data-end="1208">Collaborative issue management: Assign, track, and resolve clashes/data issues with transparent audit trails.</li>
<li style="font-size: 15px;" data-start="1209" data-end="1370">CDE-friendly: Practical integrations with Autodesk Construction Cloud and Procore keep models and issues in sync via direct connections or BCF.</li>
</ul>
<h2 data-start="1372" data-end="1391">Key Capabilities</h2>
<ul data-start="1392" data-end="1804">
<li style="font-size: 15px;" data-start="1392" data-end="1460">Clash detection &amp; constructability checks across disciplines</li>
<li style="font-size: 15px;" data-start="1461" data-end="1541">Property/data validation to enforce BIM standards and naming conventions</li>
<li style="font-size: 15px;" data-start="1542" data-end="1611">Clearance/free-space rules for safety, access, and operations</li>
<li style="font-size: 15px;" data-start="1612" data-end="1702">Data Viewer to inspect quantities, attributes, and patterns alongside the 3D model</li>
<li style="font-size: 15px;" data-start="1703" data-end="1804">Project Library to publish shared, repeatable templates (rules, filters, views) across projects</li>
</ul>
<h2 data-start="1806" data-end="1832">Integrations &amp; Workflow</h2>
<ul data-start="1833" data-end="2138">
<li style="font-size: 15px;" data-start="1833" data-end="1939">Sync models and issues with Autodesk Construction Cloud and Procore (reduce duplicate uploads)</li>
<li style="font-size: 15px;" data-start="1940" data-end="2021">Export/import issues via BCF to keep coordination moving across platforms</li>
<li style="font-size: 15px;" data-start="2022" data-end="2138">Fit lightweight checks into more milestones&mdash;from schematic coordination to preconstruction and punch-list closeout</li>
</ul>
<h2 data-start="2140" data-end="2155">Who It&rsquo;s For</h2>
<ul data-start="2156" data-end="2394">
<li style="font-size: 15px;" data-start="2156" data-end="2237">Architects &amp; BIM Managers standardizing QA/QC and modeling best practices</li>
<li style="font-size: 15px;" data-start="2238" data-end="2319">General Contractors &amp; VDC Teams driving constructability and coordination</li>
<li style="font-size: 15px;" data-start="2320" data-end="2394">Owners/Consultants requiring transparent, auditable BIM deliverables</li>
</ul>
<h2 data-start="2396" data-end="2407">Benefits</h2>
<ul data-start="2408" data-end="2682">
<li style="font-size: 15px;" data-start="2408" data-end="2474">Higher BIM quality with consistent templates and rule sets</li>
<li style="font-size: 15px;" data-start="2475" data-end="2544">Fewer rework loops thanks to faster clash and data validation</li>
<li style="font-size: 15px;" data-start="2545" data-end="2608">Lower overhead via web-based access and simpler rollout</li>
<li style="font-size: 15px;" data-start="2609" data-end="2682">Better traceability with centralized issue records and auditability</li>
</ul>
Learn more: <a href="https://www.solibri.com/checkpoint?utm_campaign=3706145-Solibri%20CheckPoint&amp;utm_source=AEC%20Tech">www.solibri.com</a>

Model view IFC and Revit - Solibri CheckPoint

Solibri CheckPoint

Checking Revit models in the cloud with Solibri

Solibri CheckPoint & the Future of AEC Model-Checking (2)

Acquisition of Xinaps and the rebranding of Verifi3D as Solibri Checkpoint

Solibri CheckPoint & the Future of AEC Model-Checking 

The Context
Despite decades of investment in BIM, architectural documentation remains heavily manual. Teams continue to spend significant time on repetitive production tasks, manual QA/QC, and enforcing standards across drawings, views, and schedules.
At scale, this creates inefficiency, late-stage rework, and coordination risk, while contributing to burnout across documentation teams. Existing automation is often fragmented or difficult to apply consistently within live project models, limiting its effectiveness in everyday production work.
SketchPro is being actively deployed at top architecture firms to address this gap by embedding AI directly into architectural documentation workflows.
The Question
How can AI support architectural documentation&ndash; QA/QC, standards alignment, and repetitive production work&ndash; while keeping architects in control, preserving authorship, and respecting firm-specific standards and workflows?
The Approach
SketchPro is an AI documentation copilot embedded directly within Revit, designed to operate on live project models, views, schedules, and sheets. The system is developed around three core principles:
<ul>
<li dir="ltr" aria-level="1">
No workflow disruption Al actions occur inside the same Revit environment already used by project teams for documentation. No exporting, syncing, or parallel tools required.
</li>
<li dir="ltr" aria-level="1">
Architect-in-the-loop control All actions are previewed, reviewable, and reversible, ensuring professional judgment remains central to decision-making.
</li>
<li dir="ltr" aria-level="1">
Firm-specific intelligence Rather than applying generic rules, the system adapts to project standards, naming conventions, and documentation logic defined by each practice with an &ldquo;AI Knowledge Base&rdquo; set up by admins from each firm.
</li>
</ul>
The Collaboration Model
SketchPro is being deployed in collaboration with architecture firms working across a range of scales, typologies, and geographic locations, including educational, multifamily, and commercial projects. Ongoing collaboration ensures the platform addresses real documentation pain points such as QA/QC consistency, annotation cleanup, and standards alignment within live projects.
The Results
Across partner firms, teams report:
<ul>
<li dir="ltr" aria-level="1">
Standardized preferences across projects with AI
</li>
<li dir="ltr" aria-level="1">
30% reduction in documentation time across major tasks like sheet creation, dimensioning, schedules, and QA/QC
</li>
<li dir="ltr" aria-level="1">
20% more capacity to do more projects with their current team
</li>
</ul>
AI is experienced as a support system rather than a replacement. Junior staff report increased confidence and productivity, while senior staff retain full control over technical decisions.
Key Takeaways
Successful AI adoption in architecture depends less on automation alone and more on customization, transparency, and trust in the technology.
By embedding AI directly into documentation workflows and aligning it with the realities of architectural practice, SketchPro illustrates how AI can strengthen existing processes rather than disrupt them, supporting efficiency, quality, and professional judgment.

SketchPro allows you to go from a hand-drawn sketch to a fully rendered design

Firm-specific intelligence based on a custom, internal knowledge base

Mentor mode provides model-specific guidance

QAQC Audit using your firm's standards and checklists

SketchPro.ai

MeltPlan is the AI-native planning engine for the built environment, built to help architects, engineers, and contractors plan projects better &mdash; earlier, faster, and with far more confidence. Construction planning today is still fragmented across disconnected tools, spreadsheets, PDFs, and tribal knowledge. Teams are forced to manually connect decisions across code compliance, estimating, scheduling, and value engineering, which leads to slow iterations, missed scope, costly rework, and avoidable project risk.
MeltPlan solves this by creating a unified planning platform that connects the four planning pillars that shape every project: code compliance, cost, schedule, and value engineering. Instead of operating like a black-box AI assistant, MeltPlan is built for accuracy and transparency. It understands real AEC context &mdash; including codes and standards, building assemblies and elements, quantities and scopes, and project-specific conditions. Every output is designed to be defensible, with clear assumptions, step-by-step reasoning, and verifiable sources.
At the core of MeltPlan is the belief that planning isn&rsquo;t just producing a schedule or an estimate &mdash; it&rsquo;s making the right decisions early, before mistakes become expensive field problems. MeltPlan helps teams move from &ldquo;searching and guessing&rdquo; to &ldquo;knowing and deciding.&rdquo;
Today, MeltPlan offers two core products:
<h3 data-start="1400" data-end="1417">Melt Code</h3>
Melt Code is the first building code research AI built to make compliance transparent and decision-ready. Instead of giving a generic answer, Melt Code provides project-specific compliance requirements along with a step-by-step reasoning trail and direct citations to every relevant code section. It also maps out Compliance Paths, guiding architects and engineers through multiple valid routes to compliance based on project variables like occupancy, construction type, sprinkler conditions, height/area thresholds, and fire ratings. Melt Code understands exceptions, cross-references, tables, diagrams, and code math &mdash; and can generate project-specific compliance illustrations to make requirements easier to interpret and communicate. It also includes workflow tools like Projects and Checklists, helping firms capture institutional knowledge and standardize compliance across teams.
<h3 data-start="2326" data-end="2346">Melt Takeoff</h3>
Melt Takeoff is an AI-assisted takeoff service designed for contractors and estimators who need speed without sacrificing trust. It combines AI vision for fast quantity extraction with onshore, field-experienced senior estimators who verify outputs and add detailed allowances for scope not shown on drawings. Melt Takeoff detects hundreds of building elements, flags drawing/spec conflicts, structures takeoffs in MasterFormat&reg;, UniFormat&reg;, or custom standards, and delivers interactive outputs with sheet-linked traceability and clean Excel exports. It enables faster bids, fewer missed scope items, and more confident pricing.
Together, Melt Code and Melt Takeoff form the foundation of the MeltPlan platform &mdash; a connected planning engine that brings clarity, speed, and trust to preconstruction decision-making.

MeltPlan

Finding the perfect office:
Mavenir’s Global Facility Manager had to find just the right office space for
the global technology software corporation in Bucharest, Romania - quickly. With time
ticking away, he needed to find the perfect office space to set up Mavenir’s operations. 

“We are now building qbiq’s AI generated architectural plans.
This platform is a true gamechanger. I have saved a huge
amount of time and leg work” - Yehuda Adar, Mavenir

Mavenir: Finding the perfect office

Qbiq is reshaping how commercial spaces are planned and visualized by leveraging advanced artificial intelligence. This innovative platform enables landlords, brokers, architects, and developers to generate test fits, floor plans, 3D walkthroughs, and material estimates in just minutes. By automating what typically takes weeks, Qbiq significantly reduces time to lease and design while increasing accuracy and scalability. Designed to work with common file formats like PDFs, CAD, and DWG, Qbiq allows users to upload floor plans, set programming requirements, and instantly receive multiple layout options. Each layout comes with full documentation&mdash;editable CAD files, BIM-ready Revit models, and rendered 3D tours&mdash;making the platform a powerful tool from concept to delivery. One of Qbiq&rsquo;s biggest advantages is its ability to support layout customization based on user-defined rules, including department sizes, adjacency preferences, and branding standards. The AI also provides quantity takeoffs, such as furniture counts and material data, helping teams better understand cost implications from the start. By streamlining both visualization and documentation, Qbiq empowers real estate professionals to move from plan to action faster and with greater confidence. The company has already made a global impact. With over 650 million square feet of AI-generated designs and clients in more than 60 countries, Qbiq has proven its value across markets. Real estate firms using Qbiq report 40% faster leasing cycles and significantly higher efficiency. For example, Colliers Japan reduced its turnaround time for test fits to under 24 hours, dramatically improving client service and deal velocity. Qbiq integrates seamlessly into existing workflows and exports fully editable plans, enabling architects and designers to build on AI-generated layouts rather than start from scratch. It also features enterprise tools like admin dashboards, user management, and custom planning engines tailored to specific corporate or project requirements. Backed by top-tier investors including Insight Partners and JLL Spark, Qbiq recently closed a $16 million Series A funding round, following a $10 million seed round in 2023. The platform is rapidly gaining traction among leading architecture firms, design studios, and property management companies looking to scale operations, improve client engagement, and accelerate space planning. Qbiq&rsquo;s combination of AI automation, customizable design logic, and real-time visualization offers a transformative approach to commercial space planning. Whether used for office fit-outs, coworking layouts, or landlord test fits, the platform delivers fast, data-driven results that align with real-world needs. Its ability to combine speed, accuracy, and design flexibility makes it a standout solution for the modern built environment. As the architecture and real estate industries continue to embrace digital transformation, Qbiq is positioned as a leader in AI-powered design technology. By reducing friction in the planning process and enabling better decision-making, it is helping reshape how commercial spaces are imagined and delivered.

qbiq

Featuring qbiq AI at the AEC Innovate Conference

qbiq’s Custom AI Transforms Design for AEC Professionals

Featuring Qbiq at the AEC Innovate Conference

Generative AI & Real Estate Innovation (Webinar)

Congratulatory post for the $16M Series A funding round

Result for “Romania”

Case Studies "1 result"

Mavenir: Finding the perfect office