<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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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

Mastery of Digital® is an international initiative launched to empower engineers, architects, and construction professionals with cutting-edge knowledge and tools for navigating the rapidly evolving world of digital construction. In a sector often resistant to change, Mastery of Digital® serves as a platform for innovation, bridging the gap between traditional workflows and emerging technologies. By providing high-quality educational resources, expert insights, and practical tools, we aim to help professionals not only adapt to the demands of the modern AEC industry but also become leaders in shaping its future.

What sets Mastery of Digital® apart is our commitment to turning technological complexity into accessible and actionable solutions. We curate the most relevant innovations, from Building Information Modeling (BIM) and AI-powered tools to automation and digital twin technologies, and translate them into hands-on, real-world knowledge. Our platform is built for those who want to future-proof their careers, deliver better projects, and stay competitive in a data-driven economy.

We provide a growing library of case studies, training programs, reports, and AI resources tailored specifically for the architecture, engineering, and construction (AEC) community. Whether you're a contractor looking to streamline operations, a civil engineer exploring automation, or a university lecturer designing a new curriculum, Mastery of Digital® gives you a space to learn, contribute, and grow.

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Our mission is to cultivate a dynamic global community focused on the discovery, application, and scaling of digital construction technologies. We aim to provide the resources, guidance, and support necessary for professionals to embrace digital transformation with confidence and to use it as a tool for innovation, sustainability, and competitive advantage.

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We hold ourselves to the highest standards in everything we produce. From training modules to technical white papers, we ensure our community always receives top-tier content grounded in practical application and academic rigor.

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Our goal is to empower individuals and organizations with the tools, skills, and confidence to navigate digital change. Whether you're starting your digital journey or leading it, we’re here to support your next step.

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ArchiMind &mdash; Compliance Is No Longer Manual
For decades, California architects have done the same thing: finish the drawings, then spend days hunting through CBC chapters, marking up PDFs, and hoping nothing falls through the cracks before plan check.
ArchiMind ends that.
ArchiMind is a native Revit plugin that turns California building code into a live, intelligent layer inside your model. The moment you run an analysis, every room, door, stair, corridor, and restroom is evaluated against CBC 2025 &mdash; automatically, instantly, and without leaving Revit. No exports. No spreadsheets. No guesswork.
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Built for the Code That Actually Governs California Projects
California doesn't run on IBC.
ArchiMind was built from the ground up for this stack:
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<li style="color: #000000;">CBC 2025 + 2022 &mdash; both editions, data-driven, always current</li>
<li style="color: #000000;">CPC 2025 Table 422.1 &mdash; exact stepped fixture counts by occupancy, not simplified ratios</li>
<li style="color: #000000;">Chapter 11B &mdash; the strictest accessibility standard in the country, fully implemented</li>
<li style="color: #000000;">DSA IR 10-2 + IR 11B-10 &mdash; K&ndash;12 and state-funded project requirements that other tools skip entirely</li>
</ul>
This isn't a multi-jurisdiction platform retrofitted for California. This is California, done right.
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12 Domains. One Click. Everything Checked.
ArchiMind evaluates your model across every major CBC review category in a single run:
Occupancy Load &middot; Egress &middot; Stairs &amp; Ramps &middot; Fire Resistance &middot; Height &amp; Area &middot; Light &amp; Ventilation &middot; Chapter 11B Accessibility &middot; Restroom Fixtures &middot; Plumbing Fixture Count &middot; EVCS Parking &middot; Exit Signs &middot; DSA Path of Travel
Every result is tied to a specific Revit element and cited to the exact code section. Not "something might be wrong." Exactly what, exactly where, exactly why.
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Not Just Flagging &mdash; Fixing
Most compliance tools stop at flagging. ArchiMind goes further.
Click GoTo on any failing check &mdash; Revit zooms directly to the element. Click Fix &mdash; ArchiMind corrects it automatically. Door too narrow? Fixed. Handrail too low? Fixed. Ramp too steep? Fixed. Stair too narrow? Fixed. Corridor too tight? Fixed. Every fix is fully undoable with Ctrl+Z. Re-run the analysis to confirm.
Click What's Checked in the dashboard to see every check ArchiMind runs, which ones have auto-fix available, and what's on the roadmap &mdash; full transparency, no black box.
For corridor and stair width violations, the Guide Lines tool draws red dimension annotations directly on your floor plan &mdash; showing exactly how far a wall needs to move. For restrooms, blue annotation boxes appear next to each room showing required vs. provided fixture counts from CPC Table 422.1, plus CBC 11B ADA clear floor area calculations.
This is the difference between a report that tells you what's wrong and a tool that helps you fix it.
<hr>
Compliance That Runs While You Design
ArchiMind's Live mode monitors your model continuously in the background. The moment a room change triggers an egress violation or a door swap creates a width issue, you know &mdash; while it's still a 30-second fix, not a 3-day redesign.
<hr>
For the Whole Team
<ul>
<li style="color: #000000;">Architects &amp; designers &mdash; catch issues during design, not at plan check</li>
<li style="color: #000000;">BIM managers &mdash; embed compliance into your standard Revit QA workflow</li>
<li style="color: #000000;">Project managers &mdash; track compliance status across every active project from the team dashboard, no BIM file required</li>
<li style="color: #000000;">Plan check consultants &mdash; pre-check before DSA or city submission and cut review cycles in half</li>
</ul>
<hr>
The Numbers
200+ compliance checks &middot; 12 CBC domains &middot; 4 California code layers &middot; Revit 2023&ndash;2026 &middot; CBC 2022 &amp; 2025 &middot; CPC 2025 &middot; Chapter 11B &middot; DSA IR 10-2 First CBC run free. No credit card required.

ArchiMind

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

Reer is&nbsp;an Intelligent Design Environment that brings AI directly into&nbsp;Rhino (&amp;Grasshopper), Revit, and more.
While most AI tools in AEC work on downstream point solutions like documentation, compliance, and visualization, Reer works upstream, directly at the source, the CAD and BIM model files where every project decision originates.
For decades, CAD and BIM platforms have been the backbone of AEC projects worldwide. They are powerful, deeply embedded, and not going anywhere. But the way professionals interact with them has barely changed. Most tasks remain manual, repetitive, and error-prone, requiring users to translate their intent into commands, scripts, and step-by-step operations rather than focusing on the work that actually requires their expertise.
Reer brings a true CAD-native AI collaborator directly into the tools we all rely on, fundamentally changing that interaction. The agent understands the context of your model, acts on your behalf, and handles the full range of workflows professionals deal with every day:
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<li class="whitespace-normal break-words pl-2">Batch edits and parametric modeling</li>
<li class="whitespace-normal break-words pl-2">Data extraction and analysis</li>
<li class="whitespace-normal break-words pl-2">Object, layer, and file organization</li>
<li class="whitespace-normal break-words pl-2">Cross-platform coordination</li>
<li class="whitespace-normal break-words pl-2">Visualization and rendering</li>
<li class="whitespace-normal break-words pl-2">Construction documentation</li>
<li class="whitespace-normal break-words pl-2" style="font-style: italic;">And more for you to explore...</li>
</ul>
Beyond individual productivity, teams can build custom skills and modules on top of Reer, turning everyday interactions into reusable design intelligence that compounds over time.
The long-term vision is one intelligent environment across the entire AEC project lifecycle, from schematic design in Rhino through coordination in Revit and beyond.
Reer is currently in Free Beta in Rhino - <a class="underline underline underline-offset-2 decoration-1 decoration-current/40 hover:decoration-current focus:decoration-current" href="https://www.food4rhino.com/en/app/reer-ai">Try it now</a>
Revit coming in June!

Reer

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

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Why choose Ideate BIMLink?
Linking Revit and Excel with Ideate BIMLink enables you to export data from Revit to Excel, streamlining data management tasks and dramatically reducing the time they take to perform. By creating a bidirectional connection between the model and a spreadsheet environment, teams can extract large volumes of element data, edit parameters in bulk, validate information, and apply changes using Excel&rsquo;s powerful sorting, filtering, and formula tools. This eliminates the need to manually open and edit individual elements in Revit, significantly reducing repetitive work and the risk of human error.
Importing data from Excel back into Revit means that your models are instantly updated with approved changes, helping ensure they remain clean, coordinated, and accurate. Ideate BIMLink provides access to nearly all instance and type parameters, including many that are difficult to manage efficiently using native Revit schedules alone. This expanded access gives teams greater control over project data, improves consistency across models, supports internal standards, simplifies model auditing, and strengthens overall data governance practices. It also enables collaboration with stakeholders who may not work directly in Revit but can safely contribute through structured Excel-based workflows.
Boost the Power of Ideate BIMLink with Ideate Automation
Add Ideate Automation to your subscription* to reduce the cost of repetitive BIM tasks by letting them run in the background while your staff focuses on higher-value work. This scripting solution can be run manually or scheduled to operate at convenient times, providing just-in-time insight into key Revit model metrics while automating essential workflows such as:
Producing document issue records Exporting PDFs Exporting health check data Preparing COBie data drops Generating quantity takeoff data
By automating these recurring processes, teams improve efficiency, strengthen data accuracy, increase consistency, reduce risk, and deliver more predictable project outcomes without interrupting daily production workflows across projects.
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Ideate BIMLink - Overview on Data Management

Ideate BIMLink

MeltBid by MeltPlan is an AI bid leveling software built for general contractors in preconstruction. The traditional bid leveling process takes estimators hours of manual work &mdash; pulling numbers from PDFs, building spreadsheets by hand, hunting for buried exclusions, and hoping nothing slips through before you award. MeltBid compresses all of that into minutes.
The workflow is straightforward: upload your subcontractor proposals and MeltBid automatically structures them into a leveled comparison inside your own Excel template or a provided one. It works both in the browser and natively inside Excel via MeltPlan's Microsoft marketplace add-in, so estimators don't have to change how they work.
The value, though, goes well beyond speed. MeltBid surfaces five core capabilities:
Bid leveling in minutes. Upload multiple proposals &mdash; PDFs from each sub &mdash; and get a fully populated line-by-line comparison in your template in under 5 minutes. Bring your own format or start from MeltBid's.
Scope gap detection. Automatically identifies line items that one sub priced and others excluded or missed entirely. These gaps are the silent killers in preconstruction &mdash; they show up as change orders in the field, well after you've already awarded the contract.
Exclusions and qualifications flagging. Highlights non-obvious exclusions and qualifications buried deep in bid packages &mdash; things like "fire alarm devices excluded," "roof penetrations not included," or "price valid 30 days only, subject to material escalation." The cheapest number on paper becomes the most expensive one post-award when these get missed.
Full traceability. Every number in the leveled output links directly back to the source line in the original proposal. No black box &mdash; estimators can click any cell and see exactly which clause in which sub's PDF drove that value, with a "Mark as Reviewed" workflow to systematically work through the output.
Natural-language AI Q&amp;A. Ask plain-English questions directly against your leveled bids: "Who is the lowest bidder and by how much?" "What did Summit exclude?" "Where's the biggest spread across bidders on mechanical?" MeltBid answers in context, scoped to the exact run you're reviewing.
It's built for the way estimators actually work &mdash; in the browser or right inside Excel &mdash; and is aimed squarely at GCs who are tired of the bid leveling process eating their preconstruction capacity.

Melt Bid

Ichi is an AI-powered code compliance and automation platform built for architecture firms.
The platform helps architects navigate complex building codes, automate time-consuming construction administration tasks, and strengthen QA/QC across the entire project lifecycle&mdash;from early design through construction.
Designed specifically for the realities of architectural practice, Ichi augments professional expertise by automating previously manual processes, improving accuracy, and minimizing costly downstream rework. The platform supports both project-level execution and firm-wide knowledge sharing, helping teams work more efficiently while maintaining accountability and control.
Key Features
<ul>
<li dir="ltr">AI Code Compliance Reviews</li>
</ul>
Ichi enables architects to quickly search, interpret, and validate applicable building codes and jurisdictional requirements. AI-assisted guidance is presented alongside authoritative source language, allowing teams to confidently apply code requirements during design, documentation, and review&mdash;without relying on generic or unverified AI outputs.
<ul>
<li dir="ltr">Automated Construction Administration</li>
</ul>
The platform helps automate and support manual construction administration workflows, including navigating construction documents, responding to RFIs, and reviewing submittals. By reducing the time spent on repetitive coordination tasks, Ichi helps keep projects moving while easing the administrative burden on architectural teams.
<ul>
<li dir="ltr">QA/QC at Scale</li>
</ul>
Ichi strengthens QA/QC by assisting with plan set reviews, cross-sheet consistency checks, and document validation across large and complex drawing packages. This allows architects to identify issues earlier, improve document quality, and reduce risk prior to submission or construction.
<ul>
<li dir="ltr" style="font-weight: bold;">Institutional Knowledge Management</li>
</ul>
Ichi acts as a living knowledge base for architecture firms. Project-specific decisions, jurisdictional nuances, and internal best practices can be captured, shared, and reused across teams and over time&mdash;helping firms preserve institutional knowledge as staff, projects, and regulations change.
Who Should Use Ichi?
Ichi is designed for:
<ul>
<li dir="ltr" style="font-size: 14px; font-family: helvetica, arial, sans-serif;" aria-level="1">
Architecture firms working across multiple jurisdictions
</li>
<li dir="ltr" style="font-size: 14px; font-family: helvetica, arial, sans-serif;" aria-level="1">
Design teams navigating complex or evolving code requirements
</li>
<li dir="ltr" style="font-size: 14px; font-family: helvetica, arial, sans-serif;" aria-level="1">
Firms seeking to reduce QA/QC risk and rework
</li>
<li dir="ltr" style="font-size: 14px; font-family: helvetica, arial, sans-serif;" aria-level="1">
Architects managing heavy construction administration workloads
</li>
<li dir="ltr" style="font-size: 14px; font-family: helvetica, arial, sans-serif;" aria-level="1">
Practices looking to capture and scale internal expertise
</li>
</ul>
While built with architects at the center, Ichi also supports collaboration with engineers and contractors by improving clarity, consistency, and access to critical project information.
Why Ichi?
Ichi is purpose-built for the architectural profession. Unlike general-purpose AI tools, it is designed to operate within the constraints of real-world practice&mdash;prioritizing accuracy, traceability, and professional responsibility.
By combining AI-driven code compliance, QA/QC automation, construction administration support, and firm-wide knowledge management in a single platform, Ichi helps architects spend less time on rote tasks and more time on high-value design and decision-making. The result is better documentation, smoother project delivery, and more resilient architectural practices.
Learn More
Ichi is helping architecture firms modernize how they manage code compliance, QA/QC, and knowledge across their projects, all on one platform.
👉 Visit https://www.ichiplan.com/ to schedule a demo or learn more about how Ichi is helping architects and other AECs build cities of the future.

Ichi

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:
<ul data-start="923" data-end="1834">
<li data-start="923" data-end="1042">
AI Vision measurement detecting 800+ building elements with counts, lengths, perimeters, areas, and volumes
</li>
<li data-start="1043" data-end="1129">
Automated scope recognition across architectural, structural, and MEP drawings
</li>
<li data-start="1130" data-end="1256">
Detailed allowances for scope not shown, added as itemized ROMs based on code requirements and real project benchmarks
</li>
<li data-start="1257" data-end="1351">
Drawing&ndash;spec conflict detection, with estimator-led resolution before it hits your bid
</li>
<li data-start="1352" data-end="1528">
Estimator-verified outputs, reviewed by onshore senior estimators with 20+ years of field experience, including delivery of multi-million to billion-dollar projects
</li>
<li data-start="1529" data-end="1691">
Flexible classification standards, supporting MasterFormat&reg;, UniFormat&reg;, or custom structures, so takeoffs drop directly into your estimating workflow
</li>
<li data-start="1692" data-end="1834">
Interactive takeoff output, with side-by-side plan review, traceable quantities, and clean Excel exports ready for pricing and bidding
</li>
</ul>
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

The Špansko Pool Complex in Zagreb is a significant public infrastructure initiative designed to elevate the recreational and athletic amenities in the western part of the city. With an investment of €34 million, the facility includes an Olympic-size swimming pool, a wellness center, dedicated fitness areas, and seating capacity for 300 spectators. This modern complex is part of the city’s broader strategy to promote community well-being and provide high-quality sports infrastructure.

As part of a real-world evaluation, a field test of InspectMIND AI, an advanced site inspection and documentation tool, was conducted during an active construction phase. The Job Site Observation module was used to assess concrete works and PPE (Personal Protective Equipment) compliance. The tool enabled real-time digital documentation of site activities, categorization of findings, and attachment of site photos—all through a streamlined and simple to use mobile interface.

Within less than an hour, a fully structured inspection report was generated, complete with AI-generated summaries for each observation area. The test demonstrated how InspectMIND AI reduces time spent on field reporting, increases clarity of safety documentation, and facilitates faster communication between stakeholders. The result was a detailed, data-backed report that highlights the value of digital workflows in the construction sector.

Špansko Swimming Pool Complex - AI Site Docs

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:
<ol data-start="2968" data-end="3342">
<li data-section-id="qsu294" data-start="2968" data-end="3069">
Upload drawings Users upload their architectural or engineering plans directly into InspectMind.
</li>
<li data-section-id="15wns3j" data-start="3071" data-end="3213">
AI plan analysis The AI analyzes the full drawing set, identifies potential issues, and compares plans against applicable building codes.
</li>
<li data-section-id="tm56oz" data-start="3215" data-end="3342">
Review results Users receive a detailed issue report showing potential risks, code conflicts, and recommended corrections.
</li>
</ol>
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

Using AI to Automate Building Inspections

Construction of the campus began in September 2019, and work was completed in late 2021. Public spaces have been raised to the fourth floor making the campus specific to conventional office buildings.
The building is shaped by stacking - vertical arranging of different functions on top of each other: a parking lot on the ground floor, three floors of office spaces, a public floor with a restaurant and a conference room, and three floors with accommodation units. 

An important feature of this project is the pioneering application of the VOLUM3 platform for the cooperation of all participants in design, construction and site supervision phases.  
Every day, 139 users joined the platform and by creating (and completing) more than 1700 individual tasks, they generated more than 200 meeting reports. More than 1200 drafts were made and their delivery, alongside complete documentation, was submitted through VOLUM3. 
Thanks to VOLUM3's modules, better overview of activities, faster and better decision-making process, the completion of tasks on time, and detailed reporting were provided. This increased the overall satisfaction of all project stakeholders and reduced the risk of failure.

Campus Infobip

VOLUM3 enables construction professionals to collaborate on projects from any Internet-connected device with access to all project documents, contracts, RFIs, submittals, schedules, drawings and more. We tested VOLUM3 with large architecture companies - they were amazed by the fact it allows them to very quickly ramp up their project and start using it for their project team(s). Most of their subcontractors were also interested in seeing VOLUM3 in action, finding it extremely easy to use and learn. Even if you are a small company VOLUM3 enables you to compete with much larger firms by showcasing the quality of produced work directly from within the VOLUM3 Collaboration Platform.

Result for “Croatia”

Case Studies "2 results"

Špansko Swimming Pool Complex - AI Site Docs

Campus Infobip