<div><img src="https://aecplustech-files.s3.amazonaws.com/blog/5741b41d-2b98-40f9-9a96-00550e6d0a57.png" alt="InspectMind AI" width="1080"></div>
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<div>Most conversations about AI in construction start with speed.&nbsp;</div>
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<div>&ldquo;How many hours did we save?&rdquo;</div>
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<div>&ldquo;How many pages could we review in minutes?"&nbsp;</div>
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<div>But a recent aec+tech conversation on AI-driven plan review made a more useful point: the tools save time, but the real reason to adopt them is risk.</div>
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<a href="https://www.youtube.com/watch?v=r0nEGFxC7as">This conversation</a> brought together Colton Johnson, Principal Engineer at Flint Engineering Company; Aakash Prasad, Co-founder and CEO of InspectMind AI; and Simon G. Solorio, President of Solorio Inc. Much of the discussion centered on InspectMind AI's plan-checking tool, though the takeaways apply to any firm weighing this kind of software.&nbsp;
In this article you&rsquo;ll see where AI plan review helps, where it falls short, and why a firm with weak internal processes will not get much out of it no matter how good the software gets.
<h2 dir="ltr">Reading a Pictorial Language</h2>
Plan review is not just about reading text. Construction documents carry meaning through symbols, callouts, line weights, and schedules that change from one project type to the next. An industrial set does not look like a large residential set. Every new hire has to learn how to read that visual system before they can review anything in it.
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"Construction documents are a language, and on top of that they're a pictorial language. You're relying on graphic and visual cues to display the information, translating heavy analysis and code requirements into those pictures."
&mdash; Colton Johnson, Principal Engineer, Flint Engineering Company
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Solorio described the second half of the problem: volume.&nbsp;
Even with tools like Revit and virtual walk-throughs on Meta Quest headsets, engineers still check ever-changing building codes by hand and coordinate across disciplines. Holding hundreds of pages of drawings against dense architectural, structural, mechanical, and civil specifications is a cognitive load that does not scale. There is only so much one person can keep straight at once, and plan sets keep getting bigger.
That is the gap AI steps into. Not the design work or the engineering judgment, but the slow, repetitive cross-checking that wears people down and still lets errors slip through.
A Third-Party Reviewer That Works Through Lunch
<figure class="image"><img src="https://aecplustech-files.s3.amazonaws.com/blog/bb52d8a0-3272-45e5-b6a9-e96e1717194f.png" alt="Errors missed in review surface on site, where fixes cost most." width="1080">
<figcaption spellcheck="false" data-qb-tmp-id="lt-8150" data-gramm="false">Fig 2) Errors missed in review surface on site, where fixes cost most.&nbsp;</figcaption>
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AI is a third-party reviewer. It&rsquo;s not a replacement for the engineer or the plan checker, but an extra set of eyes that never gets tired and never skips a page.
Prasad said InspectMind AI can typically review a 100- to 200-page project in one to two hours. In that window it catches discrepancies between drawings and specs, flags cross-discipline coordination issues, and checks against selected/applicable codes and references. It surfaces a prioritized set of findings while the team continues other work.&nbsp;
InspectMind AI has flagged a water main running straight into a 12-inch concrete pipe. It has caught a storm sewer drawn with no slope, which means it would never drain. These are the errors that hide on page 140 of a set and get expensive once they reach the field.&nbsp;
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"This isn't replacing your own diligence or your own quality of work. It's a useful automated review tool that helps you reduce risk and liability and improve quality overall."
&mdash; Aakash Prasad, Co-founder and CEO, InspectMind AI&nbsp;
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The discussion was honest about the limits, too. AI still struggles to read the purely graphical parts of a drawing when there are no words or numbers to anchor it. That should improve over the next few years, but for now InspectMind AI is strongest where drawings, specs, and code references can be grounded and cited. Purely graphical meaning, where the intent lives only in the picture, is still an area where human review matters.
<h2 dir="ltr">From Months of Back-and-Forth to Days&nbsp;</h2>
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<figure class="image"><img src="https://aecplustech-files.s3.amazonaws.com/blog/c6175af5-f771-4066-a384-2d4991e7ce06.png" alt="InspectMind AI tags each finding by severity, discipline, and code." width="1080">
<figcaption>Fig 3) InspectMind AI tags each finding by severity, discipline, and code.&nbsp;</figcaption>
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A minor discrepancy on a drawing is not just a quality problem. It is a liability problem. Prasad pointed to ADA compliance as an example. A ramp drawn with a 2.2 percent slope instead of the required 2 percent maximum is a small number on a sheet and a frequent target for litigation. Catching it before construction starts is worth far more than the hour it saved.
The same logic runs downstream. Errors that survive review become Requests for Information (RFIs) and change orders once contractors are on site. Each one costs time and money and strains the client relationship. The financial case lives in catching those errors early.&nbsp;
A church project in California shows how that plays out. The design firm ran its full drawing set (architecture, civil, structural, and specs) through InspectMind AI before submitting to the city. The AI flagged around 300 issues. The team fixed them first. When the city's review came back, the comments were mostly minor, and the firm closed them out in days rather than the weeks or months a project like this can take.
That is the payoff that matters. Not the hours saved running the review, but the weeks of back-and-forth with the jurisdiction that never happened, and the rework that never reached the field.
Garbage In, Garbage Out Still Applies
<figure class="image"><img src="https://aecplustech-files.s3.amazonaws.com/blog/88271371-af97-4945-8f3f-0a5ee2295ce9.png" alt="AI only reflects the inputs it gets." width="1080">
<figcaption spellcheck="false" data-qb-tmp-id="lt-415377" data-gramm="false">Fig 4) AI only reflects the inputs it gets.</figcaption>
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The strongest warning has always been against leaning on the tools too hard. The old 'garbage in, garbage out' rule still stands.
The software is powerful, but it sits on top of a firm's inputs, its standards, and the judgment of the people running it.
Solorio made the case for foundational knowledge. Junior engineers still need to learn the basics through hand calculations and free-body diagrams so they understand how loads travel and what a structural member is actually doing. Without that, an engineer cannot tell when the AI is wrong.
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"If you put in garbage, you're going to get out garbage, and that's still true with these new programs. If you put in something wrong, it gives you something wrong. You have to know the basics to know when it's giving you wrong information."
&mdash; Simon G. Solorio, President, Solorio Inc.
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Johnson framed the same idea as a sequence. He sees a working AI workflow as a three-tier system.&nbsp;
A firm establishes good internal processes first. Then it introduces standard automations. Only then does it put AI at what he called the tip of the spear. Skip the first two steps and there is nothing solid for the AI to stand on. When a firm's drawing standards and inputs are inconsistent, there is no realistic way to scale an AI workflow on top of them.
<h2 dir="ltr">Own the Output</h2>
Accountability is the other side of this. When an engineer signs off on a design, they own it, whatever tools helped produce it.
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"The motto we live by with AI is own the output. Just because you have a cool tool doesn't mean you get to say that the tool is the reason something was wrong. You own the output."
&mdash; Colton Johnson, Principal Engineer, Flint Engineering Company
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That has a practical edge. Johnson raised data security, pointing out that client information needs to stay protected under NDAs when a firm runs it through a third-party AI platform. Owning the output means owning the data path, too. Before uploading project documents, firms should evaluate the data path and the platform's NDA and security requirements. That is a question to settle before the first project goes up, not after.&nbsp;
<h2 dir="ltr">Built by the People Who Use It</h2>
The panel closed on what actually makes these tools better, and the answer was feedback from the field rather than anything happening in a lab. Solorio and Johnson both pointed to the value of a simple thumbs up or thumbs down on each flagged issue. That input teaches the model the difference between a real problem and an irrelevant false positive, which is the difference between a tool engineers trust and one they learn to ignore.
Because AEC technology moves quickly, the tools that respond to this feedback can change almost daily. Johnson offered an example from a job site. He floated the idea of having the plan checker automatically generate a targeted field observation checklist based on the specific materials and structural elements it found in the drawings. Within hours, the development team had piloted a connection between the plan checker and a site inspection app.
That speed of iteration is the point underneath the whole conversation.
The AI plan review tools that earn a place in AEC workflows will be the ones built and shaped by the people doing the work, not the ones that promise to do the thinking for them. The engineers on this panel were not looking for software to replace their judgment. They were looking for a reviewer that helps them protect it. That is a smaller promise than replacing an engineer, and a far more useful one.
Editor's Note
<img src="https://aecplustech-files.s3.amazonaws.com/blog/c3806924-0521-483d-b74d-a01f09ed330e.png" alt="Niknaz Aftahi" width="160" height="160">
This article was independently reviewed and edited by Niknaz Aftahi, Founder of AEC+Tech, drawing on InspectMind AI's product documentation, direct conversation with the panel participants, and hands-on knowledge of plan review and QA/QC workflows. Figures drawn from the panel rather than published material were checked before publishing.
&mdash; Niknaz Aftahi, Founder, AEC+Tech
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AI plan review saves hours, but engineers say the real payoff is fewer lawsuits, cleaner permit reviews, and errors caught before they reach the field.

AI Plan Review Isn't About Speed. It's About Staying Out of Court.

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How Ideate Software Improves Revit Data Management and QA/QC

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="What Agentic Actually Means Here" 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="niknaz aftahi" 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

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

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

Featuring Solibri at the AEC Innovate Conference

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 

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 

<h1 data-start="260" data-end="340">Ideate StyleManager for Revit: Clean, Standardize, and Optimize BIM Styles</h1>
<h2 data-start="347" data-end="390">Why Revit Styles Become a Hidden Problem</h2>
Managing styles in Autodesk Revit &mdash; including object styles, line patterns, materials, text types, and view templates &mdash; is one of those tasks that quietly affects every project. When unmanaged, styles accumulate from imported content, linked models, and copied elements, leading to bloated files, inconsistent graphics, and coordination headaches.
Over time, even well-structured projects can drift away from office standards without teams realizing it.
<h2 data-start="853" data-end="885">What Ideate StyleManager Does</h2>
Ideate StyleManager is a Revit plugin built to give BIM managers and architects greater control over project styles. Instead of manually hunting through settings or risking accidental deletions, users can analyze, merge, rename, and remove unwanted styles safely.
The tool makes it easier to clean up project files and enforce consistent standards across teams.
<h2 data-start="1256" data-end="1300">Safe Cleanup with Built-In Style Analysis</h2>
One of the biggest risks in Revit is deleting a style that is still in use. That action can trigger unexpected graphic changes throughout the model.
Ideate StyleManager addresses this with detailed usage analysis. Before merging or deleting a style, users can see exactly where it is applied. This visibility allows teams to consolidate duplicate styles, remove clutter, and align projects with office templates without compromising model integrity.
<h2 data-start="1759" data-end="1815">Improving Model Performance and Documentation Quality</h2>
Style clutter does more than create visual inconsistencies. It can also impact file size and model performance.
By reducing redundant definitions and maintaining consistent graphics, teams improve drawing clarity, coordination workflows, and overall BIM health. For firms working across multiple offices, maintaining a unified visual language becomes much easier.
<h2 data-start="2188" data-end="2225">Supporting Long-Term BIM Standards</h2>
Ideate StyleManager is not just a cleanup tool. It supports long-term BIM governance.
As projects grow in complexity and content libraries expand, style management becomes increasingly important. The ability to proactively monitor and standardize styles helps firms protect documentation quality from early design through construction documentation.
For teams focused on Revit efficiency and model reliability, Ideate StyleManager offers a practical solution for maintaining cleaner, healthier BIM environments.

Ideate StyleManager

D5 Render is a leading real-time rendering solution that revolutionizes the architectural and design workflows. Powered by AI-driven and cutting-edge technologies, D5 empowers architects, designers, and visualization artists to work more efficiently and collaboratively, accelerating creation while ensuring consistently accurate and photorealistic results. -Best balance between speed and quality -Seamless integration with your favorite design workflow -User-friendly for designers, architects, visualizers, and archviz animation professionals alike -Free access and a cost-effective teams subscription option D5 Render is transforming the world of architectural visualization by bringing real-time rendering into everyday workflows&mdash;without sacrificing quality. Designed with architects, designers, and visual storytellers in mind, D5 combines high-speed GPU processing with an intuitive interface, allowing users to create photorealistic renderings, animations, and walkthroughs almost instantly. Unlike traditional rendering engines that require long wait times and technical know-how, D5 simplifies the process through real-time preview and rendering capabilities. Users can drag and drop assets, make lighting and material adjustments on the fly, and see results in seconds&mdash;all while working within a seamless, responsive environment. It also supports live sync with popular modeling tools like SketchUp, Rhino, Revit, and Archicad, streamlining the design-to-render workflow. What sets D5 apart is not just its speed, but also its ability to handle large, complex scenes with remarkable performance. Its AI-powered denoising engine and built-in library of assets&mdash;including vegetation, lighting, and materials&mdash;help elevate visualization without external plugins or post-production tools. Architects and design firms using D5 report significant time savings, improved client communication, and enhanced design decision-making. By eliminating the barrier between modeling and rendering, D5 allows teams to visualize concepts earlier and with greater clarity&mdash;ultimately leading to better outcomes. For firms working on tight deadlines or aiming to elevate their presentations, D5 offers a powerful solution that redefines what&rsquo;s possible in real-time rendering. It&rsquo;s not just a tool&mdash;it&rsquo;s becoming a core part of the modern architectural tech stack. Check out our latest article: https://www.aecplustech.com/blog/rendering-redefined-d5-real-time-revolution-architects-design-firms

D5 Render

Featuring D5 Render at the AEC Innovate Conference

Rendering Redefined: D5’s Real-Time Revolution

Tools Leading the Way in the AEC+Tech Community

Why Every Designer Needs Collaborative Rendering (Webinar)

Why Every Designer Needs Collaborative Rendering

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

KlashBIM is a next-generation clash management tool designed to enhance efficiency in BIM-based construction projects. Unlike conventional clash detection software, KlashBIM not only identifies conflicts but also streamlines the documentation and reporting process, ensuring a seamless workflow for MEP and structural coordination. 1. Automated Clash Detection for Accurate Model Validation KlashBIM precisely detects clashes within BIM models, ensuring that conflicts between MEP and structural components are identified before construction begins. By automating clash detection, KlashBIM helps teams create more reliable designs, minimizing errors and reducing costly rework on-site. Its accuracy ensures that potential issues are flagged early, allowing project teams to make informed decisions during the design phase. 2. Customizable Documentation &amp; Reporting KlashBIM enhances project documentation by generating detailed and structured reports tailored to specific project needs. These reports provide a clear overview of detected clashes, their locations, and project-wide coordination insights. With customizable reporting options, project teams can extract relevant data efficiently, ensuring all stakeholders have the necessary information for effective collaboration. 3. AI-Powered Integration with BAMROC for Enhanced Clash Management While KlashBIM specializes in identifying and documenting clashes, it integrates seamlessly with BAMROC to facilitate AI-powered clash resolution. This integration enables smarter processing of detected clashes, ensuring that conflicts can be analyzed and resolved efficiently when used alongside BAMROC. The synergy between KlashBIM and BAMROC creates a streamlined workflow for managing design conflicts in BIM projects. 4. Seamless BIM Workflow Integration KlashBIM is designed to work effortlessly within existing BIM environments. It is compatible with major BIM platforms, allowing construction teams to incorporate its clash detection and documentation features without disrupting their workflows. This seamless integration ensures smooth coordination between different project disciplines, improving communication and reducing project delays. 5. Enhancing Project Efficiency Through Advanced Clash Management By focusing on precise clash detection, structured documentation, and seamless BIM integration, KlashBIM optimizes construction workflows and reduces inefficiencies. It ensures that all detected clashes are well-documented, improving collaboration and preventing oversight in project execution. With its advanced capabilities, KlashBIM is an indispensable tool in modern construction management, helping teams maintain accurate designs, minimize errors, and improve overall project coordination.

KlashBIM

ARES is a modern, feature-rich CAD software that serves as a leading AutoCAD alternative for professionals working with 2D drafting and 3D modeling in the industry-standard DWG file format. Developed by Graebert, ARES combines power, flexibility, and affordability&mdash;making it an ideal solution for architects, engineers, designers, and construction professionals seeking a more cost-effective, high-performance CAD platform. With a growing global footprint, ARES is trusted by some of the world&rsquo;s most prominent companies. Among its users are three of Japan&rsquo;s top five construction groups, as well as globally recognized manufacturers such as LG, Hyundai, and Samsung. Its robust feature set supports the complex demands of design, engineering, and construction workflows, offering seamless collaboration and precision. ARES also serves as the underlying technology for other leading CAD solutions. It powers DraftSight by Dassault Syst&egrave;mes, a popular CAD software for 2D design, and Onshape Drawings, the drawing module within the cloud-native Onshape CAD platform. These partnerships highlight ARES&rsquo;s reliability, scalability, and integration capabilities across different design environments. As a truly cross-platform CAD solution, ARES is available on Windows, macOS, and Linux, with additional cloud and mobile access through ARES Kudo (web-based) and ARES Touch (mobile app). This ensures design professionals can work seamlessly across devices and locations, with real-time syncing and collaboration tools. With its DWG compatibility, modern interface, and flexible subscription-based licensing model, ARES offers a compelling alternative to traditional CAD tools&mdash;without sacrificing performance or productivity. Whether you're migrating from AutoCAD or starting fresh with a new CAD workflow, ARES delivers the tools you need to design faster, collaborate smarter, and lower your total cost of ownership. Discover why ARES is the next-generation DWG CAD software for today&rsquo;s professionals. DWG Compatibility: ARES Commander works directly with DWG files, making it a good option for users familiar with other CAD software that uses this format. Part of the ARES Trinity: ARES Commander is the desktop component of Graebert's Trinity, which also includes cloud-based ARES Kudo and mobile ARES Touch. This allows for seamless workflows across devices. Productivity Features: ARES Commander includes features like Power Trim, Dimension Palette, and Layer Tools, designed to enhance user productivity. Customization: Users can customize their workspaces and user interfaces to suit their preferences. Collaboration: The ARES Trinity enables collaboration features, allowing users to share, view, and edit drawings across different platforms and devices. Cloud Integration: ARES Commander integrates with popular cloud storage services like Box, Dropbox, Google Drive, and iCloud. BIM Features: ARES Commander also includes BIM (Building Information Modeling) features, allowing users to import and view Revit and IFC files, extract 2D drawings, and extract BIM information into tables. Licensing: ARES Commander offers perpetual, annual, or flex licenses, and a single license can be used across multiple computers with different operating systems.

Modern workflows leveraging cloud and mobile to synchronize the work across all your users and devices.

CAD Anywhere: Desktop CAD (Windows, Mac, Linux), Cloud CAD (runs in your Internet browser), and Mobile CAD (Android tablets/smartphones and iOS iPhone/iPad).

With the DWG for BIM features in ARES Commander, users can create smarter CAD drawings whose geometry can be extracted and updated from Revit and IFC BIM projects.

ARES Commander - Desktop CAD: All the 2D and 3D features needed to create and modify DWG drawings on Windows, macOS, and Linux.

ARES Kudo - Online CAD: Create and modify DWG drawings online, and enjoy new collaboration features to comment with voice, photos, text notes, and stamps.

ARES Touch - Mobile CAD: A full set of 2D features to create, modify, and annotate DWG drawings on your Android smartphone/tablet and iOS iPhone/iPad with unique collaboration features to markup with voice, photos, text notes, and stamps.

ARES Trinity by Graebert

The ARES Trinity of CAD software encompasses three integrated software products: ARES Commander, ARES Kudo, and ARES Touch.

Featuring Graebert ARES CAD software at the AEC Innovate Conference

8600 AutoCAD users migrated to ARES CAD Software at Takenaka in Japan

Featuring Graebert at the AEC Innovate Conference

A civil engineering and building construction firm, which also carries out research, survey, planning, designing, supervision, and other related activities. Projects include bridges, highway tunnels, stadiums, hospitals, and office buildings.

Tobishima Corporation

Civil engineering and building construction firm

Tobishima Corporation had to reorganize management resources to deal with rising IT costs due to BIM implementation. After evaluating various CAD products, considering cost, operability, installations by other companies in the same industry, and potential, Tobishima switched from its existing 2D CAD to a different, but highly compatible solution: ARES CAD software.

Mr. Aihara (left), Chief of Construction Concierge Office; Mr. Mizukoshi (center), Manager of Civil Engineering Division; and Mr. Ozawa (right), Manager of Information System Division.

Tobishima Corporation Migrates 1,600 Computers to ARES CAD Software

Graebert‘s product portfolio is anchored by the ARES Trinity of CAD software — a powerful suite of DWG-based CAD software for desktop (ARES Commander), mobile (ARES Touch) and cloud (ARES Kudo). 

Taisei Corporation

TAISEI Corporation is one of the “Big 5” construction groups in Japan, with 8,572 employees as of March 2021. Technical drawings are required on construction sites, and a company as big as TAISEI uses more than most. Such drawings are created at the office by the design teams, and keep evolving during the project. Construction workers depend on a constant flow of drawings because they need to keep up with frequent design updates. In addition, they need a way to share feedback from the field with the design teams — which is ideally done within the drawings themselves. Centralizing files in the cloud enables users to circulate drawing updates in real time. But in TAISEI’s case, the challenge is also to deploy a Mobile CAD solution to the thousands of iPads used in the field, without compromising on productivity and security.

TAISEI Corporation Deploys ARES Touch Mobile CAD On 4,000+ iPads

ARES Touch is a full-featured mobile CAD app that provides a complete set of 2D drafting and annotation commands. It's the perfect companion to quickly review a drawing shared in the cloud or received by email while on the go.

Team members at the jobsite can collaborate efficiently with colleagues back at the office

ARES Touch helps users work more effectively while on the jobsite or  in the field

ARES Touch works on Android and iOS tablets and smartphones

The ARES Trinity of CAD Software: Create and Modify DWG Files with Modern Collaboration Tools

ARES CAD Software: Top 10 Trinity Features Outperforming AutoCAD | Expert Review by Architosh

DWG Editing on Mobile: ARES Touch Makes it Quick & Easy!

ARES Touch: Mobile CAD for Android and iOS to View, Create and Annotate DWG

ARES Touch

Work productively on mobile devices with tools tailored to smaller screens

Result for “Japan”

Case Studies "2 results"

Tobishima Corporation Migrates 1,600 Computers to ARES CAD Software

TAISEI Corporation Deploys ARES Touch Mobile CAD On 4,000+ iPads