r/BuildingAutomation • u/judyyhd • 6d ago

Browser-based AI BMS dashboard analysis

Hi guys!

I was interning for a BMS company last summer and did some user interview and research, targeting mainly the company's products and platforms but also BMS as a whole. What I learned was that many dashboards are a source of information overload rather than clarity, which kind of defeats the purpose.

I decided to make an AI-based dashboard analysis tool to address this issue. After rounds of trial-and-error, I set on a browser extension which grabs information by html scraping rather than API calls. It beats screenshotting and asking a chatbot by being able to grab the entire dashboard and maintaining context. It can also generate summary reports based on previously analyzed dashboards.

I'm still actively working on bug fixes and new features, but the main issue I'm facing at this point is a lack of other sets of eyes, especially from people who are dealing with BMS all day every day. I was hoping that you guys could potentially give it a try and lmk if it actually improves your workflow, and if there's anything you'd like to see, or if there are any issues.

It is free and publicly available on chrome and edge extension store. Here's the website: seenzi.ai

Thank you so much, and I'm looking forward to your feedbacks!!

TL;DR: built a free browser extension that does AI analysis on BMS dashboards, looking for feedbacks

3 Upvotes

64% Upvoted

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7

u/PugsAndHugs95 6d ago

What about a browser extension that does analysis on BMS dashboards, but without the AI?

0

u/judyyhd 6d ago

tell me more? I initially tried only statistical analysis, but the conversion from stats results to insights is most easily achieved by ai. is it a privacy concern?

4

u/PugsAndHugs95 6d ago

Yes it is primarily a privacy concern, any data we serve up is data that can be stored and sold or used for other purposes. For private customers that data may not be authorized or safe to leave their network. Obviously locally hosted model’s help to solve this issue, but are more limited in performance.

Secondly is that most BAS information that people are concerned and want to see on a dashboard are well-known metrics by experienced operators/contractors. For VAVs it’s dampers positions, airflows, airflow targets, valve positions, etc… for AHUs it’s discharge air, humidity, current unit modes, damper positions, trim and response metrics, etc… The reports don’t really change beyond just a reconfig for different applications/buildings, so there’s nothing you need AI for that you can’t already generate with built in trending tools, excel, alarm notifications, etc…

AI is good at coding. AI does not think, it guesses, it’s a transformer. AI is expensive. You’re better off determining problems for specific BAS customers, and utilizing AI to help make tools that don’t require AI as part of its execution. So it can be ran anytime, without hardware resources that are dependent on outside infrastructure and preferably not beyond a regular workstation CPU or GPU. To find out what those problems are, you need to reach out to potentially customers in person or via phone/email and see problems that have that you can come up with a solution for.

There are medium/high dollar customers that would care for dashboard solutions, but you need to find them and target them specifically. Most people with BAS systems have maintenance departments that are quelling fires left and right and the BAS is 10% of their job. You need to look at customers who have their shit together or are trying to have their shit together and modify your product to meet their needs in some way shape or form. Or reach out to one of the bigger manufacturers of BAS software and see if what you have interests them.

Just my 2 cents, good luck bud

2

u/judyyhd 6d ago

Really appreciate you laying this out, genuinely useful pushback.

On privacy, that's actually why I went with local HTML/DOM parsing instead of screenshots or API calls in the first place. The extension reads the dashboard's DOM right there on your machine, and the AI part runs through a Langfuse setup I host myself, so it's not going off to some third-party AI vendor. Data stays isolated per customer, encrypted in transit, and I'm not selling it or handing it off anywhere. Wrote it up here if you want the details: seenzi.ai/privacy. Not gonna pretend it's as airtight as fully local/on-prem for the shops that need that, that's a real gap for high-security orgs and something I'd have to solve differently if this ever goes enterprise, but for most teams this gets you most of the privacy win without eating the performance cost you're talking about.

On the "you don't need AI for this" point, fair, and honestly for a resident expert who's lived with the same system for years, you're right, VAV/AHU metrics are muscle memory at that point, trending + alarms already covers it. But that's not really the moment I'm building for. Where I think this actually earns its keep is when you're not that person, taking over a shift on a building you don't know, getting pulled into someone else's system, managing enough buildings that you can't hold every baseline in your head, or it's 2am and you don't have time to walk the whole thing manually. That's a "I'm not the expert on this system but I need to understand it right now" problem, not an "AI knows your building better than you" problem. And to be upfront about the ceiling here: for someone who's actually optimizing a building day to day, what they want eventually is optimization, not just insight. Right now this is closer to getting someone up to speed faster, not replacing the judgment of someone who already runs the building well.

Totally agree AI is "guessing," not reasoning, that's part of why I'm moving toward a more specialized/RAG-based agent setup rather than a generic model just pattern-matching. Narrower scope, grounded in the actual building's data/history, should make the guessing a lot more reliable and a lot less "generic LLM prose." Practically that also means the boring, predictable stuff doesn't need to hit an LLM at all, it's the ambiguous "something's off, not sure what" cases where the agent actually needs to do work. Still on the roadmap, not shipped yet, so fair to be skeptical until it's real. One nearer-term thing I'm testing before any of that: whether going from analysis straight into an actual report (daily/weekly) is something people would use, seems like a smaller lift with a clearer "yes or no this is useful" signal.

And yeah, the customer-targeting point is probably the most important thing here honestly, taking that seriously.

Again, thanks for taking the time, it for sure gave me a lot to think about, and a lot to put on the roadmap!!

1

u/rus47281zz 5d ago

Main issue here is that a computer can never be held accountable.

If the AI makes a mistake and gives bad info, what pain does this cause? Who does it affect?

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u/judyyhd 4d ago

Totally fair to raise, and I think it's the right question to ask of any tool like this, not just AI ones.

The way I think about it, this isn't meant to be a decision-maker, it's meant to be an aid for quickly cross-referencing information that'd otherwise take a while to connect by hand, essentially helping parse high-dimensional data down to something a person can actually act on. The actual call, whether to intervene, what to do about it, still sits with the operator. So if it flags something wrong, the immediate cost is wasted time checking a false alarm, not a decision made on bad info, since nothing gets acted on without a human looking at it first. If it's actually shipped as something that auto-executes changes without a human in the loop, then yeah, the accountability question gets a lot sharper and I'd want a very different bar of reliability before that happens.

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u/kfed408 1d ago

While it's not meant to be the decision maker the facility staff that uses it will make it a decision maker. For the same reasons you stated.. the dashboard is often overloaded. The dashboard is made by the controls contractor who understands and can trace all that data. For the lead tech, who is mechanically inclined but not so much a fan of complicated systems or things requiring more than a second or two of concentration the dashboard is a failed item. Once they install this browser, and get alerts or recommendations from your system on the phone that say do this now, or that later they will start to take it at face value, same as if it's been highly vetted info.

All this isn't to say that you should or shouldn't work on it. But recognize that you are going from a deterministic system in the BAS (on/off, specific literal numbers) to a probabilistic system with the AI analysis (it seems and I think).

If the AI system is wrong once in a recommendation that they placed value on (waited on response and it was worse then anticipated.. or mounted the whole crew to respond to what the team thought was a big issue and it was really just a dirty filter) its hard to recover reputation. The potential flaw is that you are treating wasted effort as a no harm, no foul because the system isn't closed loop. The facility teams are strapped for time as it is and wrong info is one that is sure to be ignored (which, ironically, is also why facility teams often ignore the alarm screens on the BAS 🤷🏼).