<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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How Ideate Software Improves Revit Data Management and QA/QC

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

How Kestrel Labs Is Bringing Code Compliance Inside the BIM Model

<figure class="image"><img src="https://aecplustech-files.s3.amazonaws.com/blog/9198a462-3995-42b2-ad2b-bddb08f3649e.png" alt="Ichi helps architecture teams bring code research earlier into the design process, turning complex compliance questions into faster, cited answers." width="1080">
<figcaption spellcheck="false" data-qb-tmp-id="lt-987372" data-gramm="false">Fig. 1) Ichi helps architecture teams bring code research earlier into the design process, turning complex compliance questions into faster, cited answers.</figcaption>
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We sat down with Chinmayi Suri, Architectural Designer at DGA, and Brandon Levey, Founder and CEO of <a href="https://www.ichiplan.com/" target="_blank" rel="noopener">Ichi</a>, to learn how the two are working at the intersection of architecture and AI to tackle some of the profession's most time-consuming pain points: building code compliance, QA/QC, and the institutional knowledge that disappears when senior staff retire.&nbsp;
On a recent corporate laboratory project, the team at DGA, a Bay Area architecture firm specializing in life sciences and advanced manufacturing facilities, hit a familiar wall. Their mechanical scope carried a $250,000 line item for fire dampers, and the client wanted that cost justified before signing off. The architects suspected the dampers were not actually required by code for this specific occupancy and assembly configuration, but confirming it the usual way meant setting up a meeting, a senior architect pulling code books, cross-referencing amendments, and chasing answers across the firm. On a project already under deadline pressure, the budget question was at risk of being closed out without ever being properly answered.
Instead, they ran the question through Ichi, an AI platform built specifically for navigating building code complexity. Within seconds they had a cited answer from the code confirming the dampers were not required. The line item came out. The client saved $250,000.
While this is a relatively small sum for highly complex projects, this is just a single example and without that quick verification, the money would not have stayed in the project. It would have come out of finishes, or contingency, or somewhere else the team would rather not have touched. That is the part worth paying attention to. Code research used to be a slow, senior-architect-only task that often gets skipped or rushed under deadline pressure. When it stops being slow, it starts shaping budgets in real time.
<h3 dir="ltr">How Manual Code Work Is Costing You</h3>
Code compliance is one of those parts of architectural practice that almost no one celebrates. It is also one of the most expensive parts of the job, in hours if not always in dollars.
Chinmayi Suri, an architectural designer at DGA, works on cGMP pharmaceutical, life science, and manufacturing facilities that often run past 100,000 square feet and carry layered regulatory requirements. She describes the existing process bluntly. Navigating ADA, fire codes, and industry-specific regulations falls on the most senior architects, the ones who have memorized chunks of code over years of repetition. It is institutional knowledge stored in human heads, queried one Slack message or in-person conversation at a time.
That creates two problems. The first is throughput. Senior staff become the bottleneck on every code question, which pulls them away from the design work they were hired to do. The second is timing. Code checking traditionally happens after the design is mostly finished, so compliance issues surface late, when changes are expensive and the schedule is already tight.
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"This stemmed from frustration really. I was sitting on my computer saying this should not take so long, and that's kind of actually where this all started."&nbsp;&mdash; Chinmayi Suri, DGA
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Her original interest in AI tools came out of that frustration, not out of any particular enthusiasm for AI as a concept.
<h3 dir="ltr">Why Generic AI Tools Fall Short</h3>
The obvious question for any architect frustrated with code research is: Can&rsquo;t ChatGPT do the job? The answer, so far, has been no, and the reasons are useful to understand.
Brandon Levey, founder and CEO of Ichi, comes from a 20-year tech background that includes work at a Department of Energy national lab and running R&amp;D teams at Square. He grew up in his father's HVAC and sheet metal business, which is partly why he ended up building software for AEC. He breaks the gap down into three things.
Accuracy on compliance codes has to be very high. Architects stamp their drawings, and a model that produces plausible-sounding language without citing the exact code section is worse than no answer at all. The documents themselves are also unusually large. A spec package can run 6,000 pages. A jurisdiction comment letter can run 60 pages with 300 individual items. General-purpose tools are not built to parse and cross-reference at that scale with any reliability. And, the answers always depend on the context that lives inside the firm: what counts as a standard detail, which assemblies the client has used before, what the senior architect on this project type would flag.
Chinmayi put it more directly. Generic AI tools require the user to already have deep domain knowledge to know what to ask and to verify what comes back. That defeats the purpose. The point of an AI assistant for code work is to lower the barrier, not require an expert to operate it.
<figure class="image"><img src="https://aecplustech-files.s3.amazonaws.com/blog/d43e9371-5c56-45b3-b46b-0264708472ca.png" alt="What Agentic Actually Means Here" width="1080"><canvas class="qb-highlighter__canvas" style="display: none; top: 1px !important; left: 200px !important;" width="47" height="22"></canvas>
<figcaption spellcheck="false" data-qb-tmp-id="lt-193248" data-gramm="false">Fig. 2) Ichi&rsquo;s agent based workflow helps teams organize code review, QA/QC, and consultant coordination tasks in a structured review board.</figcaption>
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<h3 dir="ltr">What Agentic Actually Means Here</h3>
Ichi's product is built around what Brandon calls an agentic approach. The term gets used loosely, so it is worth being specific.
In Ichi's case it means that when users ask Ichi a question they aren&rsquo;t interacting with a single LLM, but rather many different agents that are specialized in specific tasks. This approach helps the platform guide users when they do not know exactly what to ask,check its own work, and complete longer, more complex tasks.
A simple example. An architect asks a question about an auditorium in California. A generic chatbot returns a generic answer. Ichi instead asks clarifying questions about occupancy load and square footage, because those variables fundamentally change which code sections apply. That guided flow matters in code work, where a wrong assumption upstream invalidates everything downstream.
The error-checking piece is more technical but arguably more important.
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"Any type of long-running or long horizon task in AI is going to have errors in it. The way that we evolve through that, a lot of the agentic approaches are by using multiple AI agents to check its work."&nbsp;&mdash; Brandon Levey, founder and CEO, Ichi
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In practice that means when Ichi pulls code references from a 60-page comment letter or runs a feasibility study, separate agents are reviewing the output for inconsistencies and gaps before the architect sees it. That internal cross-check is what makes the difference between a curiosity and something architects will actually put their name on.
Want to hear the full discussion?<a href="https://www.youtube.com/watch?v=rsm_sSv2urI" target="_blank" rel="noopener"> Watch the complete panel conversation with Chinmayi Suri and Brandon Levey on YouTube.&nbsp;</a>
<h3 dir="ltr">Reducing the Work Architects Enjoy Least&nbsp;</h3>
The most concrete value Ichi delivers shows up in the workflows least enjoyed by architects.
Comment letters are the clearest case. When a jurisdiction returns a plan check, the architect's first job is translation: turning a long PDF into a structured spreadsheet that maps each comment to the relevant code reference, assigns it to the right consultant, and drafts an initial response. Brandon described one user who spent five to eight hours turning a 60-page letter with 300 comments into a workable spreadsheet. With Ichi, that drops to around 20 minutes.
Feasibility and zoning studies follow a similar pattern. A study that previously took two to four hours of senior staff time becomes a 20-minute guided workflow, producing an exportable document with code references already in place.
On the documentation side, the savings come less from automation than from triage. Chinmayi pointed to 700-page document sets where the real value is having AI do the first pass, locating where specific notes or drawing discrepancies live. The architect still verifies and decides. They just no longer have to read every line to find the three that matter.
The work that consumes the most time in an architecture firm is rarely the work that draws people into the profession. Cutting hours out of that work is where most firms will see the first real return.
<div><a href="https://www.youtube.com/watch?v=gSKu4e2Qg3s" target="_blank" rel="noopener">Watch Ichi in action: See how the platform supports zoning, design, permitting, and inspections across the full project lifecycle.</a></div>
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<h3 dir="ltr">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

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

ArchiMind

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.
</li>
</ul>
The Collaboration Model
SketchPro is being deployed in collaboration with architecture firms working across a range of scales, typologies, and geographic locations, including educational, multifamily, and commercial projects. Ongoing collaboration ensures the platform addresses real documentation pain points such as QA/QC consistency, annotation cleanup, and standards alignment within live projects.
The Results
Across partner firms, teams report:
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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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</ul>
AI is experienced as a support system rather than a replacement. Junior staff report increased confidence and productivity, while senior staff retain full control over technical decisions.
Key Takeaways
Successful AI adoption in architecture depends less on automation alone and more on customization, transparency, and trust in the technology.
By embedding AI directly into documentation workflows and aligning it with the realities of architectural practice, SketchPro illustrates how AI can strengthen existing processes rather than disrupt them, supporting efficiency, quality, and professional judgment.

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

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

Mentor mode provides model-specific guidance

QAQC Audit using your firm's standards and checklists

SketchPro.ai

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

ArchiBoost AI is an AI-powered co-pilot built specifically for the Architecture, Engineering, and Construction (AEC) industry. Developed by architects and engineers rather than generic AI developers, the platform helps project teams navigate complex building regulations, activate institutional knowledge, and automatically review project documentation to identify coordination risks and compliance issues before they become costly problems.
ArchiBoost AI supports architects, engineers, developers, and general contractors working across the full project lifecycle&mdash;from early design through construction documentation and project delivery.
The platform combines three core capabilities into a unified intelligence system: Code Compliance, Knowledge Hub, and AI-powered QA/QC review.
1. Code Compliance &mdash; Project-Aware Regulatory Intelligence
ArchiBoost provides project-specific code compliance guidance, not just code search.
The system evaluates project conditions&mdash;including occupancy classification, construction type, fire separation distance, and jurisdiction&mdash;to determine the exact requirements that apply to a project. It automatically resolves the hierarchy between international, state, and municipal codes, ensuring that teams see the governing regulations rather than conflicting sources.
Each response includes clear explanations and direct citations to the applicable code sections, helping teams confidently apply requirements during design, documentation, and plan review.
By identifying compliance paths early, ArchiBoost helps teams avoid costly redesigns, permit delays, and late-stage documentation changes.
2.1. Knowledge Hub &mdash; Institutional Intelligence for AEC Firms
Architecture firms often possess vast amounts of knowledge scattered across project folders, standards manuals, specifications, and internal documentation. ArchiBoost transforms this information into a structured and searchable intelligence layer.
The Knowledge Hub organizes two key knowledge domains:
Firm Intelligence &ndash; reusable internal standards such as BIM execution plans, QA/QC checklists, documentation standards, master specifications, and lessons learned from previous projects.
Project Intelligence &ndash; project-specific information such as owner standards, design guidelines, specifications, schedules, meeting minutes, and consultant documentation.
This structure allows teams to quickly access relevant information without manually searching through large document repositories.
2.2 Detail Library &mdash; A Living Library of Proven Solutions
Within the Knowledge Hub, ArchiBoost includes a Detail Library that transforms past project details into a living, searchable resource.
The platform extracts metadata from drawing sets and organizes details based on parameters such as: - project type - construction type - occupancy classification - applicable codes - jurisdiction - client standards
Teams can quickly locate vetted details from previous projects and reuse them with confidence. The system integrates directly with Revit, allowing teams to insert editable details into their models without interrupting their design workflow.
This helps firms standardize documentation, preserve institutional knowledge, and reduce time spent recreating common solutions.
3. AI-Powered QA/QC Review
ArchiBoost automatically analyzes complete drawing sets&mdash;including architectural and consultant documentation&mdash;to identify coordination gaps, inconsistencies, scope omissions, and potential compliance risks.
Issues are surfaced through a structured ticketing system that links directly to the relevant sheet location. Teams can review findings, assign ownership, track resolution, and export issue reports for consultant coordination.
By identifying problems earlier in the documentation process, ArchiBoost helps reduce RFIs, change orders, and costly rework during construction.
Who Uses ArchiBoost AI
ArchiBoost AI is designed for: - Architecture firms working across multiple jurisdictions - Design teams producing large, multi-disciplinary drawing sets - Developers seeking early compliance validation - General contractors reviewing documentation prior to construction - Firms looking to activate and scale institutional knowledge across projects and offices
Platform Overview
ArchiBoost AI is delivered as a cloud-based SaaS platform that integrates with existing AEC workflows and documentation processes.
By combining regulatory intelligence, institutional knowledge, and automated QA/QC review in one platform, ArchiBoost enables teams to identify risks earlier, make better design decisions, and deliver higher-quality projects with fewer surprises.

ArchiBoost AI

Melt Code is the first building code research AI built for accuracy, transparency, and real compliance decision-making. Unlike generic AI tools that behave like black boxes, Melt Code doesn&rsquo;t just provide an answer &mdash; it shows the full research process so architects and engineers can verify every step and confidently defend their decisions.
Users can ask code questions in plain English and receive reliable, project-specific requirements with:
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Step-by-step reasoning with clear assumptions
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Direct citations to exact code sections for full traceability
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Compliance Paths, mapping multiple valid routes to compliance (based on occupancy, construction type, sprinkler conditions, height/area limits, ratings, etc.) so teams can choose the best design approach
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Exception and cross-reference handling, including circular references across multiple chapters
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Table and diagram understanding, ensuring accurate interpretation of code tables and figures
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Built-in calculations, such as occupant loads, egress sizing, travel distances, and fixture counts
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Personalized compliance illustrations, automatically generated to visually explain requirements for a specific project scenario
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</ul>
Beyond research, Melt Code includes workflow tools that turn compliance into an organizational asset:
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Projects, where teams store project context (location, occupancy, construction type, sprinkler status, etc.) so every question becomes instantly project-aware
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Checklists, allowing firms to build, store, share, and export reusable compliance checklists across teams and project types
</li>
</ul>
The result is dramatically faster research, fewer senior escalations, reduced consultant dependence &mdash; and most importantly, confidence and peace of mind in every code decision.

Melt Code

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

InspectMind AI

Catch construction mistakes before they get built

AI-Powered Site Documentation: An InspectMIND Case Study

Featuring InspectMind AI at the AEC Innovate Conference

Featuring InspectMIND at the AEC Innovate Conference

Tools Leading the Way in the AEC+Tech Community

Using AI to Automate Building Inspections

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

KlashBIM

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.
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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.
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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

Norway

Flowcase is a centralized system of record for managing employee experience and project history in AEC and professional services firms.
Most firms store resumes, project sheets, and case studies across SharePoint folders, Word documents, Excel files, and email threads. Over time, data becomes inconsistent, outdated, and difficult to trust. Skills are duplicated. Certifications lapse. Project descriptions vary by format. Before every bid, teams scramble to piece together the right information.
Flowcase replaces this fragmented setup with a structured, searchable platform built specifically to manage CV and project data at scale.
As a system of record, Flowcase:
&bull; Maintains one authoritative source for employee profiles, certifications, and project experience &bull; Standardizes skills and master data to eliminate duplicates and inconsistencies &bull; Enables employees to update their own information through controlled templates &bull; Tracks certifications and education to ensure compliance and accuracy &bull; Stores project references in a consistent, reusable format &bull; Supports confidentiality levels across different clients and markets &bull; Preserves master CV data while allowing tailored versions for specific bids
Because the data is structured, it becomes instantly usable across the business, especially as more and more content is generated with AI. As every business knows, when it comes to AI - bad data in, bad content out. Flowcase ensure good data, great content out. Bid teams can quickly assemble accurate CVs and project references. HR can maintain up-to-date employee records. IT gains visibility, control, and integration flexibility through an open API.
Flowcase integrates with CRM, HR, and ERP systems to ensure experience data connects with commercial and resource workflows. It scales across offices and acquired companies, creating one uniform data layer for the entire organization.
The result is not just faster proposals. It&rsquo;s trusted, centralized expertise data that supports bidding, staffing, compliance, and strategic decision-making.
Flowcase becomes the source of truth for the most important asset your firm has: your past experience. Your Proof.

Flowcase

Import 3D for Autodesk Revit is a robust extension engineered to translate external 3D models into native Revit geometry and material definitions. It accelerates development of custom components and detailed entourage by reusing models produced in other 3D applications or sourced from online asset libraries. This approach streamlines family creation and project setup by eliminating redundant modeling tasks and maintaining design consistency across disciplines Autodesk App Store With a single-click operation users can import complete mesh models including embedded materials directly into project environments or family templates. Import 3D automatically detects and groups imported geometry into existing Revit materials based on color and transparency properties. Intelligent material sorting ensures that furniture entourage mechanical parts and site elements retain their intended appearance without additional manual mapping Performance and configurability are central to the Import 3D design philosophy. The plugin employs highly optimized code to process thousands of mesh faces in seconds making it practical to load complex assets into large building information models. Import parameters allow adjustment of units scale and insertion coordinates for each operation. Imported objects can be classified under the Revit category of choice to maintain project organization and support downstream scheduling workflows The extension supports all industry-standard exchange formats including glTF Wavefront OBJ and Collada DAE. This broad format compatibility lets teams leverage 3D assets created in SketchUp Blender Rhino and other modeling tools without file conversion overhead. Import 3D functions seamlessly in both project and family editing modes to accommodate architects mechanical engineers landscape designers and visualization specialists Licensing and deployment are tailored for both individual practitioners and enterprise teams. Import 3D is available as a desktop license or multi-user floating license through the archi web store with secure checkout provided by Stripe. Each license includes a fully functional seven-day trial and ships with a demonstration Wavefront file that can be loaded using the integrated Load Demo button to evaluate performance on custom workflows. The installer is digitally signed and uses a custom setup routine independent of the standard Autodesk installer to ensure full compatibility with virtual desktop environments. Version twenty-four point four adds robust vertex referencing support to handle a wider variety of OBJ file specifications and extends stability in large model scenarios. The plugin is compatible with Autodesk Revit versions two thousand twenty through two thousand twenty-five running on Windows x sixty-four with a download size of approximately two point eight megabytes. Comprehensive user documentation and a dedicated online forum are available for self-paced learning. Archi technical support can be reached via email and community channels to assist with deployment and customization within existing BIM standards

Import 3D

  Example of 3D model with materials, colour and transparency imported as a site.

Archi Reports is a comprehensive schedule-publishing solution for Autodesk Revit that transforms raw schedule data into polished, multi-page documents in seconds. Built to eliminate manual export workflows, it automates the entire process—from data extraction to pagination and file generation—so teams can focus on design rather than document production. At its core, Archi Reports features an intelligent pagination engine that analyzes schedule rows and automatically fits them into formatted tables across as many pages as needed. This algorithm handles varied row heights, column widths and nested groupings, ensuring pixel-perfect layouts without manual tweaking. Whether you’re publishing equipment lists, room schedules or material takeoffs, you’ll get clean, consistent output every time. Performance is a key advantage. Archi Reports runs natively inside Revit and processes large datasets—thousands of rows—in a fraction of the time required by exporting to external tools. Batch publishing allows multiple schedules to be generated in a single operation, turning what used to take hours into a task that completes in under a minute on typical hardware. Output flexibility is unparalleled: generate high-resolution PDF documents, native Excel spreadsheets or Word documents, and send reports directly to any Windows-accessible printer. Each report can include customizable headers, footers, page numbers and your company logo to maintain consistent branding. Document metadata—such as project name, author and date—can be embedded automatically for better traceability in document management systems. Archi Reports integrates seamlessly with the rest of the archi plugin ecosystem. When paired with Docuflow and iSync, it becomes part of an end-to-end BIM documentation workflow: parameter synchronization, sheet publication and schedule generation all occur with a single click. This tight integration eliminates handoffs between different teams and software, reducing errors and accelerating project delivery. Deployment and licensing are designed for both individual practitioners and large firms. The plugin installs via a digitally signed custom installer—bypassing standard App Store limitations—to support virtual desktop infrastructure environments. Licenses are available as perpetual or subscription, with desktop and floating options, and include a fully functional seven-day trial so you can evaluate features on real projects. Payment is processed securely through Stripe or PayPal on the archi web store. Archi Reports supports all Autodesk Revit versions from 2019 onward, including the latest 2025 release, running on Windows x64 platforms. Version 23.4, released December 26, 2023, adds streamlined file and folder dialogs for faster setup and improved compatibility with large project structures. The installer footprint is approximately 2.7 MB. Extensive user documentation, a dedicated forum and 24/7 email support ensure your team can deploy, configure and customize reporting workflows to match existing BIM standards. By automating schedule publication with high-quality output, Archi Reports helps AEC teams save countless hours on documentation, maintain brand consistency and deliver actionable data to stakeholders—online or offline—without leaving the Revit environment.

Reports

Docuflow is a powerful Revit plugin designed to simplify and accelerate the publication of construction documentation. With a single operation, users can export sheets to high-quality PDF, DWG, DWF, DWFx, IFC and drawing lists, eliminating manual selection, sheet matching and multiple print jobs. Intuitive, robust and lightning-fast, Docuflow transforms BIM workflows by automating repetitive tasks and enforcing office standards enterprise-wide. Featuring intelligent sheet detection, Docuflow automatically recognizes sheet sizes and orientations, matching them seamlessly to outputs without user intervention. Its advanced search and sorting capabilities let you locate sheets by any text or revision grouping, making project selection quick and precise—even on large models. Whether handling renovation projects or multi-phase developments, you’ll always publish the right drawing in the right order. Speed is at the core of Docuflow’s design. Built from the ground up with print-queue optimization, it delivers exports up to 65 % faster than native Revit or competing solutions, reducing batch publishing from minutes to seconds. High-performance publishing engines handle thousands of sheets concurrently, while optional background tasks let you run custom scripts, FTP uploads or document-control integrations before or after each job. Tailor output filenames to your office protocols with unlimited advanced filename rules. Combine project, sheet and custom parameters, dates and strings into preset naming conventions for PDF, DWG, DWF and IFC files. Rich BIM metadata, including model location and sheet attributes, is embedded as PDF metadata—ensuring document traceability and seamless integration with document management systems on desktop or VDI environments. Docuflow also supports physical printing workflows. Simultaneously publish digital outputs and route sheets to office printers of any size, simplifying review and archive processes. Integration with archi Reports (sold separately) dynamically generates drawing lists in PDF format. Docuflow supports both local installations and VDI environments, making it flexible for desktop and enterprise deployments. Available as a fully functional 7-day trial and licensed as desktop perpetual, subscription or floating licenses, Docuflow is backed by 24/7 support and regular updates for Revit 2026 compatibility. Visit the archi website to start your free trial today.

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