<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;
<blockquote>
"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.
</blockquote>
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.
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To learn more about InspectMind AI, visit InspectMind AI's <a href="../../../tools/inspectmind-ai" target="_blank" rel="noopener">page on aec+tech </a>or explore the latest features and request access on the InspectMind AI's <a class="_ymio1r31 _ypr0glyw _zcxs1o36 _mizu194a _1ah3dkaa _ra3xnqa1 _128mdkaa _1cvmnqa1 _4davt94y _4bfu18uv _1hms8stv _ajmmnqa1 _vchhusvi _kqswh2mm _ect4ttxp _syaz13af _1a3b18uv _4fpr8stv _5goinqa1 _f8pj13af _9oik18uv _1bnxglyw _jf4cnqa1 _30l313af _1nrm18uv _c2waglyw _1iohnqa1 _9h8h12zz _10531ra0 _1ien1ra0 _n0fx1ra0 _1vhv17z1" title="https://www.inspectmind.ai/" href="https://www.inspectmind.ai/" target="_blank" rel="noopener" data-renderer-mark="true" data-is-router-link="false" data-testid="link-with-safety">website</a>.
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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.
<blockquote>
"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.
<blockquote>
"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.
<blockquote>
"I wish we could put Mark Davis into a notebook LLM."&nbsp;&mdash; Chinmayi Suri, on DGA principal Mark Davis
</blockquote>
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.
<blockquote>
"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
</blockquote>
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

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Champ Agents, powered by Wyatt is Unanet&rsquo;s agentic AI experience that turns your Unanet data and connected business systems into trusted answers, agentic workflows, and interactive apps. It is designed to help teams move faster, work smarter, and make better decisions by transforming complex business data into clear insight and action.
<div class="tk0j8o1 _1ibi0s31a _1ibi0s3en"><a id="base-ui-:rm2:" class="wdbi343 wdbi341 _1ibi0s3ec _1ibi0s376" tabindex="0" role="button" href="https://unaverse.atlassian.net/wiki/spaces/MKTG/pages/2728591474" target="_blank" rel="noopener noreferrer" aria-label="Citation 1" data-label="1" data-base-ui-click-trigger="" aria-haspopup="dialog" aria-expanded="false"></a></div>
Instead of relying on static reports, spreadsheets, or manual data pulls, Champ Agents, powered by Wyatt, gives users a more intuitive way to interact with the information they need most. Teams can ask business questions in plain English, automate recurring work, generate decision-ready outputs, and act across connected systems without needing specialized technical expertise.
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This makes the product especially valuable for organizations that want to reduce reporting bottlenecks, surface risk earlier, and give more users access to meaningful, governed insight.
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The core features of Champ Agents, powered by Wyatt include Chat, Roundups, Chores, Email, and Connections.
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With Chat, users can ask questions, refine requests, launch analyses, and work across connected systems through a conversational interface.
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Roundups serve as the agentic analysis and app-building layer, producing briefs, recommendations, structured tables, monitors, simulators, and interactive apps.
<div class="tk0j8o1 _1ibi0s31a _1ibi0s3en"><a id="base-ui-:rmk:" class="wdbi343 wdbi341 _1ibi0s3ec _1ibi0s376" tabindex="0" role="button" href="https://unaverse.atlassian.net/wiki/spaces/MKTG/pages/2728591474" target="_blank" rel="noopener noreferrer" aria-label="Citation 1" data-label="1" data-base-ui-click-trigger="" aria-haspopup="dialog" aria-expanded="false"></a><a id="base-ui-:rmn:" class="wdbi343 wdbi341 _1ibi0s3ec _1ibi0s376" tabindex="0" role="button" href="https://unaverse.atlassian.net/wiki/spaces/MKTG/pages/2729115690" target="_blank" rel="noopener noreferrer" aria-label="Citation 2" data-label="2" data-base-ui-click-trigger="" aria-haspopup="dialog" aria-expanded="false"></a></div>
Chores handle recurring workflows such as checks, reminders, digests, nudges, escalations, and refreshes, helping teams stay on top of important work without constant manual follow-up.
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Email supports summaries, drafts, approvals, and inbox-based workflows, while Connections federate structured and unstructured data from Unanet and approved systems to create a richer, AI-readable context layer.
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A major differentiator of Champ Agents, powered by Wyatt is that it is built to be governed, permission-aware, sourced, and human-in-the-loop where needed.
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Rather than acting like a generic chatbot, it is designed to work with trusted business context so outputs are more explainable, traceable, and useful in real business workflows.
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It can help teams ask questions, automate workflows, build apps, and act across connected systems, all within a secure and structured experience.
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In short, Champ Agents, powered by Wyatt helps organizations turn disconnected data and manual work into trusted answers, proactive workflows, and practical business action. For SEO purposes, it is best positioned as an agentic AI platform, AI business workflow solution, and connected data intelligence experience that helps project-based firms operate with more clarity, confidence, and control.
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Champ Agents, powered by Wyatt

CRDR BIM, short for "Clash Resolution Diagnosis Report in BIM," is a specialized tool designed to streamline clash resolution documentation within Autodesk Navisworks. It simplifies compliance reporting and enhances workflow efficiency for BIM managers, coordinators, and specialists by providing a structured approach to documenting resolved clashes. 1. Efficient Clash Resolution Reporting CRDR BIM enables users to generate detailed Clash Resolution Diagnosis Reports that document how clashes have been addressed between different BIM model versions. This feature helps teams maintain accurate records of resolution progress, improving project tracking and compliance. 2. Seamless Navisworks Plugin Integration Functioning as a dedicated Navisworks plugin, CRDR BIM integrates directly into existing workflows, allowing users to compare &lsquo;before&rsquo; and &lsquo;after&rsquo; BIM models effortlessly. This integration ensures that clash resolution documentation remains within the familiar Navisworks environment, eliminating the need for external tools. 3. Intuitive &amp; User-Friendly Interface Designed for ease of use, CRDR BIM features an intuitive interface that simplifies navigation and workflow execution. BIM professionals can efficiently document and categorize clashes without technical complexity, improving productivity and adoption rates. 4. Consistent Viewing Angles for Clear Comparisons CRDR BIM ensures consistent perspective when capturing images of clashes, maintaining uniformity between different model versions. This feature enhances clarity in reports, making it easier for teams to analyze and understand clash resolutions. 5. Customizable Clash Categorization for Precise Documentation Users can categorize clashes into resolved, unresolved, approved, or resolved but connected manually, ensuring a clear and organized approach to documentation. This categorization allows for better project tracking and compliance verification. 6. Flexible Reporting Formats for Easy Sharing CRDR BIM supports HTML and PDF export options, enabling teams to save and share reports efficiently. These reports include detailed images and summaries, making them valuable for project documentation and stakeholder communication. 7. Enhancing BIM Management with Streamlined Reporting By integrating directly with Navisworks, CRDR BIM empowers BIM managers and specialists to document clash resolutions accurately and efficiently. It ensures that compliance reporting is effortless while maintaining a structured and transparent workflow. With its powerful reporting features and seamless integration, CRDR BIM is an essential tool for professionals involved in clash resolution documentation within BIM models.

CRDR BIM

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 

Ark Design AI is an AI-powered platform that helps architects and developers create architectural designs for multi-family and mixed-use projects. Architects and real estate developers, create automated floor plans and feasibility reports for multi-family and mixed-use projects quickly and efficiently. Our platform is the first AI solution for architectural schematic design. Optimize profitability, density, and living standards while meeting US codes and ordinances instantly, allowing you to make better and faster decisions. How it works Ark Design AI's software analyzes architectural designs and generates optimized building designs that meet project requirements and local regulations. It can create infinite variations to optimize for density, profitability, and living standards. Benefits Ark Design AI can help architects make better and faster decisions, and cut design costs by up to 90%. It can also free up architects' time and energy for more creative endeavors. Features Ark Design AI can create automated floor plans and feasibility reports. It can also generate a unique mix of unit types for a floor layout. Trial Ark Design AI offers a 10-day free trial. Ark Design AI was built by a team of AI scientists, architects, and software developers. Stage 1 Properties Provide several basic details about your project, such as the number of Floors, the number of Dwelling units, building uses and the Zoning district; these details will help ARK suggest better options further in the process. Stage 2 Lot Type &amp; Lot Editor Please select the preferred lot type: Interior Lot (One side street lot), Corner Lot (Two side street lot), or 2 Corner Lot (Three side corner lot). Once selected, kindly click on &ldquo;Next&rdquo; to proceed. Create the property lines of the Lot and provide information on the surroundings (Streets, Adjacent buildings, and yards). Stage 3 Locate Main Floors Locate the schematic floor plate of the ground and Typical floor of the Project, use different Shapes and sizes to explore and select a floor scheme. Stage 4 Core Editor Design the Vertical Shafts focusing on the residential floors; Adjust the circulation and Determine Room size according to the market standards and building type (Rental/ Condo/ Affordable). Stage 5 Schematic Section Receive the schematic side view and 3D massing of your project. From this point forward plan the project according to your personal preferences. Control the number of floors and their types(Cellar, Ground, Mixed-use, Typical and setback), Edit the floor plans, View summarize area calculations and export it to PDF and Revit file for further design. Stage 6 Floor Plan Editor Edit the Floor plate, create recesses, courts and setbacks; plan the units layout based on your choice of units types distribution. Within minutes the Ark algorithm will generate potential floor plans that meets building code. Select and edit the best option for your project. Stage 7 Plan Unit Mix The floor layout design includes various unit types such as studios, one-bedroom, two-bedroom, and three-bedroom apartments, each with customizable settings like price per square foot and minimum area. Room distribution control ensures optimal use of rooms along window walls without exceeding the maximum available area. The process allows for creating a unique mix of unit types for the typical floor layout, balancing efficiency and market demands.

ArkDesign.AI

ArkDesign.AI Demo with Dity Ayalon (Founder & CEO)

Featuring ArkDesign.AI at the AEC Innovate Conference

Featuring ArkDesign.AI  at the AEC Innovate Conference

Tools Leading the Way in the AEC+Tech Community

Generative AI & Real Estate Innovation with ArkDesign.AI

Article post ( Multi-Family & Mixed-Use Design Through AI: Meet ArkDesign)

Multi-Family & Mixed-Use Design Through AI: Meet ArkDesign

Generative AI & Real Estate Innovation (Webinar)

Generative AI & Real Estate Innovation with ArkDesign.AI, Higharc & Laiout - AEC-Tech Talk 20

AI-Powered Generative Design & Feasibility for Multi-Family & Mixed-Use

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

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

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

The Biosphere, Treehotel – Designed by BIG, Bjarke Ingels Group

The Biosphere at Treehotel, designed by BIG (Bjarke Ingels Group), is a unique retreat located in northern Sweden that merges architecture, ecology, and immersive nature experiences. BIG, a global architecture and design practice with offices in Copenhagen, New York, Shenzhen, London, and Barcelona, created the Biosphere to extend the experience of Treehotel guests into a cohabitation with nature, including resident birds and bats. The project draws inspiration from the surrounding forest, incorporating local materials such as wood and stone to create an interior that harmonizes with its natural environment while promoting a positive ecological impact.

The design process was greatly enhanced through the use of D5 Render, enabling the team to produce high-quality, photorealistic visualizations in just a few hours. This contrasts sharply with traditional rendering tools like V-Ray, which would have required three to four days to achieve comparable fidelity. D5 Render’s real-time rendering and AI-powered features allowed BIG to iterate rapidly, explore design alternatives efficiently, and present highly detailed visuals to clients and stakeholders. By combining speed, accuracy, and visual quality, D5 has become an essential part of BIG’s workflow, demonstrating its transformative impact on architectural visualization for complex, nature-integrated projects like the Biosphere.

BIG & D5 Render

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

Why Every Designer Needs Collaborative Rendering (Webinar)

Why Every Designer Needs Collaborative Rendering

Sweden

BIM Energy Renovation is an easy-to-use, building energy analysis software helping you realise energy savings in existing buildings. You don’t need to be an Energy Expert to use BIM Energy Renovation. We have simplified the energy analysis process, empowering you to model, analyse, and understand your potential energy savings. The software helps you find energy savings in existing buildings, allowing you to consider multiple scenarios in energy upgrades, and giving you a full payback analysis of the energy savings against the potential investment. The energy calculation is based on physics and economy only. It is not reliant on specific building regulations which means you can analyse potential energy savings for any existing building, anywhere in the World. Why wait? Get an automatic Free Trial or book an instant Live Demo of BIM Energy Renovation and Get Started today! https://bimenergy.com/#trial-license Developed by StruSoft AB.

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BIG & D5 Render