<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.
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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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</div>

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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">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

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

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

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

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

Mentor mode provides model-specific guidance

QAQC Audit using your firm's standards and checklists

SketchPro.ai

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

Ideate StyleManager

ARES Kudo: The Cloud-Based CAD Solution Empowered by AI In the rapidly evolving world of computer-aided design (CAD), ARES Kudo has emerged as a game-changing cloud-based solution. Developed by Berlin-based software company Graebert, ARES Kudo is the first online CAD software to reach feature parity with AutoCAD LT, making it a strong alternative for professionals looking for flexible and collaborative drafting tools. With its modern ribbon user interface and more than 300 CAD features, ARES Kudo enables seamless transitions from traditional CAD environments while introducing innovative cloud collaboration capabilities. One of the most significant recent advancements in ARES Kudo is the integration of artificial intelligence (AI). Graebert has introduced A3, an AI-powered assistant designed to enhance user experience, optimize workflows, and provide real-time guidance. Unlike generic AI chatbots, A3 is specifically trained in CAD applications, offering expert support tailored to users&rsquo; needs. A3: The AI-Powered CAD Assistant Artificial intelligence has become an integral part of daily life, from navigation apps to automated customer support. In the CAD industry, AI's potential is now being realized through tools like A3. Initially launched within ARES Commander, A3 is now fully integrated into ARES Kudo, bringing AI-driven assistance directly to users working in the cloud. A3&rsquo;s capabilities go beyond simple query responses. The AI assistant helps users locate commands within ARES Kudo&rsquo;s interface, provides step-by-step guidance on using features, and offers best practices for CAD design. For professionals transitioning from other platforms like AutoCAD, A3 simplifies the learning curve by instantly pointing out feature locations. For example, users can type &ldquo;Where is the PDF Import?&rdquo; and A3 will highlight the relevant tool within the interface. A3 also functions as an advanced knowledge source for CAD professionals, offering technical advice, design recommendations, and even industry-specific best practices. Whether users are looking for ways to streamline workflows or need assistance with a particular command, A3 serves as an on-demand expert. Beyond guidance, A3 can perform time-saving tasks such as unit conversions, text translations, and quick calculations. Its multilingual capabilities make it even more accessible, allowing users to communicate with the AI assistant in languages such as English, Spanish, French, German, and Japanese. Unlike traditional customer support, which may take hours or days to provide answers, A3 delivers instant responses, significantly improving productivity. A Cloud-Based Alternative to AutoCAD LT ARES Kudo&rsquo;s introduction of A3 is part of a broader effort to position itself as a leading online CAD platform. With a feature-rich environment and a familiar ribbon UI, ARES Kudo now matches AutoCAD LT&rsquo;s functionality while offering additional benefits unique to cloud-based solutions. One notable enhancement is its support for dynamic blocks, which ensures compatibility with AutoCAD files and facilitates seamless collaboration between different software environments. Additionally, the integration of the Trinity Block Library provides access to over 450 pre-built dynamic blocks, further boosting efficiency. By combining cloud accessibility with AI-driven support, ARES Kudo is redefining the CAD experience. Users can now enjoy a powerful, intelligent, and collaborative design environment, making ARES Kudo a compelling choice for professionals seeking innovation and efficiency in their CAD workflows. Learn more about the powerful solution here: https://graebert.pxf.io/JKJgyv

ARES Kudo now features an AI assistant with extensive CAD knowledge.

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

 Online CAD with Dynamic Blocks in ARES Kudo 

ARES Kudo: Full-featured 2D Online CAD for your DWG Drawings

ARES Kudo Online CAD for DWG Viewing, Creation and Modification in your Internet Browser

ARES Kudo

Cloud-Only Plan (includes ARES Kudo only)

Mobility Pack (includes ARES Kudo and ARES Touch)

ARES Trinity Plan (includes ARES Kudo, ARES Touch, and ARES Commander)

Because ARES Kudo runs in a browser, it can be accessed on any computer.

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

Featuring Graebert at the AEC Innovate Conference

Featuring ArkDesign.AI at the AEC Innovate Conference

ARES Kudo: DWG Drawings Automation for Developers

ARES Kudo by Graebert Introduces AI to Assist Its Users

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

Solibri Office

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

Building Better with BIM QA: 2025 Interview with Solibri

Checking Revit Models in the Cloud with Solibri

Featuring Solibri at the AEC Innovate Conference

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

One Click LCA is the world&rsquo;s leading end‑to‑end sustainability platform, purpose-built for the construction and manufacturing sectors. Founded in Helsinki in 2001, the company has grown to a team of over 200 professionals across all continents and now serves users in more than 170 countries  At its core, this cloud‑based platform offers streamlined, automated Life Cycle Assessment (LCA) and Environmental Product Declarations (EPDs), enabling users to scientifically measure and reduce environmental impacts of structures, infrastructure, renovations, and manufactured goods. Beyond LCA and EPDs, it also supports assessments of circularity, life‑cycle cost, and biodiversity. A standout feature is its extensive global database with over 250,000&ndash;300,000 datasets&mdash;covering supplier-specific and generic material data&mdash;fully verified and regularly updated. This empowers users to make informed, data‑driven decisions. The platform supports over 80 compliance standards, including LEED, BREEAM, GRESB, CALGreen, and various country‑specific regulations. One Click LCA seamlessly integrates with more than 20 leading BIM and design tools, such as Autodesk Revit, Tekla Structures, Bentley iTwin, Rhino, and Grasshopper. This allows for real‑time feedback and early‑phase embodied carbon optimization&mdash;notably through its Carbon Designer 3D tool, which enables quick, BIM‑integrated carbon hotspot analysis and comparison of design scenarios. In some cases, Carbon Designer 3D helps identify carbon footprint variations up to 50% during conceptual design. Leading global players&mdash;including Skanska, WSP, Foster+Partners, Turner, ArcelorMittal, Arcadis, ARUP, Geberit, and Saint‑Gobain, among others&mdash;rely on One Click LCA to drive their decarbonization efforts. AECOM, for instance, equipped its ScopeX&trade; platform with One Click LCA to target a 50% carbon reduction across its projects. Its visibility among industry analysts reflects its market leadership: One Click LCA was featured in Verdantix&rsquo;s 2025 Buyer&rsquo;s Guide for LCA tools and is the first platform validated by RICS under its Whole Life Carbon Assessment (WLCA) programme. One Click LCA also makes parts of its platform freely accessible via its Planetary embodied&ndash;carbon calculator&mdash;a tool for benchmarking cradle‑to‑gate carbon impacts across building designs and supporting EPD usage worldwide. In summary, One Click LCA stands out with its: - Massive, high-quality global dataset (250&ndash;300k records) - Broad compliance across 80+ standards - Deep BIM integrations for early and ongoing carbon management - Recognition by major firms and standards bodies It remains the go-to platform for turning sustainability goals into measurable, actionable outcomes across construction and manufacturing value chains.

One Click LCA

Featuring One Click LCA at the AEC Innovate Conference

Featuring One Click LCA  at the AEC Innovate Conference

AI, Sustainability & Latest Tools for AEC

Sustainability Tools to Watch Out For in 2025!

Pirros is the AI project hub for architects and engineers. Our mission is simple: make managing projects easier for firms. We reduce the burden of producing hundreds of details and families per project by giving designers instant access to everything their firm has developed in the past. Rather than spending hours digging through cluttered folders on a server or rebuilding content that already exists somewhere in the archive, Pirros centralizes every detail and family a firm has produced and makes them accessible with a simple search.
Pirros was started by two structural engineers turned entrepreneurs. Ari and Peter spent time at a handful of different design firms and kept experiencing the same problems when it came to retrieving and managing Revit content. The technology industry has overlooked the AE design space and the strategic importance of details and families to a firm's success. Pirros is on a mission to change that.
The four pillars of Pirros:
<ol class="[li_&amp;]:mb-0 [li_&amp;]:mt-1 [li_&amp;]:gap-1 [&amp;:not(:last-child)_ul]:pb-1 [&amp;:not(:last-child)_ol]:pb-1 list-decimal flex flex-col gap-1 pl-8 mb-3">
<li class="font-claude-response-body whitespace-normal break-words pl-2">No upfront work. Upload and instantly search across projects without manual tagging or naming conventions. Powered by a direct ACC integration, firms get tens of thousands of details and families onto Pirros within 24 hours.</li>
<li class="font-claude-response-body whitespace-normal break-words pl-2">Search by composition, not tag. Find details and families based on what the element actually is, not what its tags say. Powered by a proprietary blend of AI, computer vision, and OCR.</li>
<li class="font-claude-response-body whitespace-normal break-words pl-2">Share universally. Share Revit details and families as universal PDFs with colleagues and partners outside of Revit, accelerating collaboration, content approvals, and new hire onboarding.</li>
<li class="font-claude-response-body whitespace-normal break-words pl-2">Built-in QA workflows. Review and approve standard content automatically surfaced based on analytics across projects. Approval workflows live inside the platform instead of in committee meetings and email chains.</li>
</ol>
Pirros is backed by Y Combinator, SOC 2 certified, and trusted by leading AE firms across North America, Australia, and the UK.
&nbsp;

Pirros

Solibri, a Nemetschek Company, is the global leader in model quality, BIM validation, and digital quality assurance for the construction industry. Solibri helps architects, engineers, contractors, owners, infrastructure teams, and public-sector organizations ensure that digital building models are accurate, reliable, and ready for confident decision-making before construction begins.

Digital construction depends on trusted model data. As buildings and infrastructure projects become more complex, teams need more than visual model review. They need structured, repeatable quality assurance that verifies geometry, information, classification, coordination, and compliance across every project stage. Solibri embeds rule-based BIM validation into digital construction workflows, helping teams identify and resolve issues early, before they create costly rework, delays, or downstream risk.

With Solibri, project teams can check multidisciplinary BIM models systematically, detect clashes, validate data, review classifications, manage issues, and enforce project or organizational standards. This supports a more reliable model-based process where decisions are made using verified information rather than assumptions. By improving model quality before construction, Solibri helps teams strengthen collaboration, reduce financial exposure, protect schedules, and improve the quality of building information across the entire building lifecycle.

Solibri supports BIM quality assurance from design through long-term operations. 

- In design and architecture, teams can validate models early to prevent inconsistencies before coordination begins. 
- In BIM coordination, Solibri helps multidisciplinary teams detect clashes, verify standards, and align around trusted model data. 
- During construction and handover, validated BIM information supports contractors in planning, delivery, documentation, and project handover. 
- In operation and maintenance, accurate model data helps owners and facility managers maintain buildings and infrastructure more efficiently over time. 


The Solibri platform is available through a structured product portfolio designed for different levels of BIM maturity, team size, and validation needs. 

- Solibri Starter gives individual professionals and small teams reliable IFC model review, federation, visual inspection, and model-based communication. 
- Solibri Essential adds structured clash detection, predefined rule-based model checking, baseline information validation, BCF issue management, and CDE integrations for project teams formalizing coordination workflows. 
- Solibri Advanced enables organizations to enforce digital quality standards across projects with custom rules, clash detection matrix control, IDS workflows, Quantity and Information Takeoff, repeatable validation practices, and automation-ready workflows. 
- Solibri Premium supports portfolio-level governance, compliance assurance, scalable automation, point cloud checking, centralized validation frameworks, AI-powered assistance, and extended IDS management. 

For secure and regulated environments, Solibri Security+ provides fully offline, air-gapped validation for government, defense, and sovereign infrastructure use cases. 


Built on openBIM, interoperability, and decades of BIM quality assurance expertise, Solibri integrates into connected digital construction ecosystems and supports collaboration across disciplines, organizations, and project phases. Active in over 100 countries, Solibri serves construction developers, infrastructure projects, design and engineering teams, contractors, owners, and public authorities working with digital permitting and model-based project delivery.

By raising the standard for model quality and information reliability in BIM, Solibri helps the construction industry move from reactive issue resolution to proactive quality assurance. Solibri enables teams to reduce rework, improve collaboration, ensure compliance, and deliver better buildings and infrastructure through trusted digital models. 

Solibri

Solibri is a Nemetschek Company and the global leader in BIM model validation and digital construction quality assurance, helping project teams reduce rework, ensure compliance, and deliver better buildings through trusted digital models.


Pellikaan is delivering one of the largest wellness complexes in the Netherlands in Zoetermeer, combining a 9,500 m² main building with sixteen pavilions across a 2.5-hectare site. The project includes pools, saunas, relaxation spaces, smaller structures, varied ground conditions, and a high level of architectural and technical complexity. To maintain control across this scope, Pellikaan structured the project around digital quality assurance, model-based coordination, and practical BIM workflows.

The project team divided the main building into ten zones and segmented the wider development into sixteen subprojects, each with its own team, schedule, site access, models, and coordination process. This reduced complexity and allowed the project to progress in parallel without changes in one area disrupting the entire delivery.

Solibri was used to support model checking, clash detection, Information Takeoff, and change detection between model versions. Weekly model updates created a consistent review rhythm, allowing design and construction teams to identify deviations early, validate quantities, and make decisions based on current project information. Instead of relying on assumptions or manual comparison, the team could see changes such as added steel, reduced concrete, or modified installations directly in the model.

The result was a more controlled delivery process, with faster decisions, fewer late-stage corrections, and greater confidence in project information. For Pellikaan, the Zoetermeer wellness complex has become a blueprint for applying digitalization in a practical, quality-driven way across future projects in the Netherlands and internationally.

BIM quality control at Pellikaan: Zoetermeer wellness complex

Netherlands

Spatiomatics aims to understand, create and manage cities and urban processes through a data-driven approach. We focus on spatial informatics, computational design and software products for different industries. We provide various services: digital workflow consulting, computational design scripts and product development. 

SIMO

OMRT was founded in Amsterdam in 2018 and has been accelerating ever since. Our vision is to create an all-knowing computational brain that facilitates the full design process towards the most optimal built environment on all technical levels. We have developed two major technologies: Olysis and Ostate. Optimizing the built environment by digital solutions. OMRT stands for shorter design times, optimal building performances and a sustainable environment. We created major generative design and parametric technologies to accelerate building teams.

A solar study displayed within OMRT's Design Scout

The OMRT Design Scout product detailing construction elements & unit size

Result for “Netherlands”

Case Studies "1 result"

BIM quality control at Pellikaan: Zoetermeer wellness complex