Every Building Is a Balance Sheet: Spatial Memory for Real-World Assets — Jordi Spranger, Spranic

Video authored by the channel shown, not by IANIA; all rights belong to the creator. Transcript and summary generated automatically; the original-language content is preserved.

Original language: English

Original video: Every Building Is a Balance Sheet: Spatial Memory for Real-World Assets — Jordi Spranger, Spranic · Channel: AI Engineer · 10:20 · Watch on YouTube

Topics: Enterprise AI applications, Data, databases and embeddings, Multimodal models

Summary

Jordi Spranger argues that real estate records lag behind financial records, leaving building conditions poorly documented between inspections and creating risks such as underinsurance. He proposes using off-the-shelf sensors to scan properties, identify and store assets in a graph database, and align repeated scans over time. This creates a queryable, AI-ready record that can answer questions about a space and show what has changed, with evidence linked to observations. The resulting building data could support maintenance, compliance, insurance, and other downstream uses, including more tailored insurance policies.

Key points

The talk describes real estate as a roughly $400 trillion global asset class, with commercial property as one focus.
Financial books are updated frequently, while the underlying building records may remain unchanged for years.
The speaker cites research on 1,500 properties in which 70% of buildings were found to be underinsured.
A walk-through with an off-the-shelf sensor can create a scan that is stored as a semantically meaningful graph.
Repeated scans are aligned over time so users can compare spaces and identify changes.
Each observation can retain its source, helping users inspect evidence for detected changes or issues.
Users can ask questions about a mapped property, such as locating a radiator or kitchenware.
Building records could support maintenance, compliance checks, insurance, interior design, and robotics.
The speaker presents insurance as a use case for verifying building conditions and tailoring premiums to property upkeep.

Chapters

Start	Chapter	Summary
00:01	The real estate record gap	Spranger introduces the scale of real estate and the mismatch between frequently updated financial books and infrequently updated building records.
03:07	Turning walk-throughs into spatial memory	He explains how sensor scans can capture a property, store recognized assets in a graph, and align repeated walks over time.
04:38	Querying spaces and detecting changes	The talk describes ledger-like questions about what changed, when it changed, and who should know, alongside examples of AI queries and change detection.
07:14	Applications for building data and insurance	Spranger connects AI-ready building records to maintenance and other tasks, then explains how verified property information could inform more tailored insurance.
09:48	Conclusion	He closes by calling for more transparent, AI-enabled real estate records and shares where viewers can learn more.

Quotes

what would it take to make a building as queryable as a ledger? — 02:35

every walk becomes a memory. — 03:07

These are not vision questions, they are ledger questions. — 04:38

you have now a building that is an AI-ready data asset. — 07:14

Concepts and entities

Jordi Spranger, Spranic, Neo4j, Kroll, memória espacial, banco de dados de grafos, ativo de dados pronto para IA, detecção de mudanças, seguro imobiliário personalizado

Full transcript (original language)

00:01 Hello. I’m very excited to give this talk today at AI Engineer NYC 2026. Um this is a collaboration between Sprana and Neo4j. Sprana, I am the founder of. It was founded in June 2026. Neo4j is a graph database in based in Sweden, Malmö. And um a special thanks here at Zaeem, who is the collaborator on Neo4j’s side. This is about every building is a balance sheet and the spatial memory for

00:31 real-world assets. The largest asset class on Earth is real estate and it’s about 400 trillion globally in size. And um this is more than global equities, debt, and gold combined. If we look on the right, you see a lot of squares. Each square is about 1 trillion uh in worth and you can see the residential part is the biggest. It’s around 300 trillion. Then you can see the commercial is around 60 trillion and then agriculture around 50 trillion.

01:02 And uh today we will focus more on the commercial side, but all of this um real estate is impacted by essentially a gap. And the gap is that our financial books uh where we have lending and insurances and um mortgages, all of this is book kept um and this is updated every quarter. But the actual underlying assets, which is the real estate that they are based on, um they’re not updated every

01:33 quarter. In fact, they’re only updated maybe every few years, maybe just before the financial instrument for the for the real estate is being created. And also maybe again once it needs a modification or a new financial instrument on top of it, maybe in in year five is a very re- realistic time frame. But between year zero and year five, you really don’t have an update on the

02:04 building state or the real estate state. So that means that while the book closes so many times, there’s really a black box or maybe something that is based on some pictures and some PDFs about the building. And this is a real gap. Um and you can see it. So according to Kroll, um they did a research with 1,500 properties. Essentially, 70% of the buildings are underinsured. And why exactly that is, um

02:35 I don’t exactly know. It didn’t conclude. But my read is that the the record is quite old and it’s maybe also not as detailed. So the insurance is not very highly specified and really fulfilling the needs of the real estate. And [snorts] this impacts around 4 trillion US commercial mortgages. And um yeah, every one of them rests on the building. Uh so the question, what would it take to make a building as queryable as a ledger? And a ledger here can be give

03:07 you an answer in milliseconds. But a building today is giving you only an answer after a site visit. And even then, maybe in like some notes, PDFs, very dispersed and intransparent, not unified. Um so this is where the idea comes in, where every walk becomes a memory. And how this works is you just take a off-the-shelf sensor, you walk the space, and you map it. And you can see it up there actually being created. That was me uh going through a conference

03:37 hall. And the whole walk or the whole scan here took literally just walking through it. And a minute later, you had the scan. Um so you walk through the property, whoever that is, property management, etc., can be equipped with it. They map it. And [clears throat] as they map it, you get all the assets in the building being recognized and stored in a database, which is building below. This is the graph database. And so, this is then has a semantic meaning, which means we can really

04:07 understand the space. And as we walk it again, both walks are kept. So, walk on Monday and a Monday next year or the next quarter. And these are now aligned on a time axis. So, we have a space, we know what’s in the space, and we know the evolution of the space according to every every time we walk it and we measure it. And this is all kept as a graph. Graph have a bunch of technical benefits, but one of them is they’re extremely flexible in what information you want to

04:38 gather and how you want to connect it. And so, this can be hyper personal personalized to each asset to each to each property. Um yeah. And so, now what is it that a record has to answer? So, these are not vision questions, they are ledger questions. You really want to know in a property or in the real estate, what has changed? So, two walks of one space compare object by object and and surrounding by surrounding. You

05:09 also want to know when it has changed. So, every observation carries and captures where it came from. And you also want to know who needs to know information or deltas that have happened that we have now observed. So, when you find something with evidence, for example, structural damages or maybe for fire insurance that fire extinguishers are off off Yeah, they have expired, then you need to know that and you need to be informed and counteract

05:41 on it. Or maybe the insurance can know and they can increase their premiums by the performance of the upkeep of the property. So, hyper-individualized insurance. Um and here’s really how it looks. So, this is just me walking through a kitchen captured twice, once on the left and once on the right. And you can see you just walk the space, you directly get the map, and um and as you walk the same space, they’re being recognized as the same space, and

06:12 you can align them between um the same space in the time period A and the time period B, and you have a timeline. And once you have a map, you can now use it as an AI-ready asset, which means that you can chat with it, you can talk with it, you can ask questions against it. For example, here I ask, “Where can I heat myself?” And in the map, the answer is being given to the radiator. Or, “Is there a glass or mug to drink from in a kitchen?” It It points you to

06:42 the glasses and mugs in the kitchen. Um so, this is very powerful because it makes the map from pixels into something we can work with. Um and now also the change detection. So, this were two passes we made of this kitchen or of this space, and we can really now connect these two and ask, “What is the delta between them? What has changed in this environment?” And in this case, um these three chairs in the middle have been displaced, and you can see when you chat with the with the asset, with your

07:14 data, you can actually identify the delta and get the ground source reference to the actual capture, so you have proof of it changing. Um so, what does this all mean? It means that you have now a building that is an AI-ready data asset. So, there’s one record of what it what is there, and you can connect it to any downstream task. So, on the left we have this conference scan that I made, and then you can now check over time any repair that needs to

07:45 be happening or some maintenance or some compliance checks or insurance or maybe interior design changes or maybe even you have some robotics that you want to place there and it’s like a digital twin for it to use. So, let’s check the insurance. When you walk the building, when the policy starts, you have like a first initial state of the property and on based on this you can do a hyper-personalized insurance policy. And as you walk it again at renewal, you now

08:17 see the delta and the upkeep and so on and the insurance policy insurance company can actually measure the performance of the property for relevant facts against its policies and give a very hyper-personalized insurance policy, which means a very efficient premium to insure such property and insure it in the right way. Uh yeah, and these are the numbers behind that. So, the business case here for the insurance for example would be

08:47 that the insurance pays for what it can verify and so currently what insurers know is that one in three commercial buildings are on the books with the wrong construction class. 35% of the buildings in the specific case for example for the hurricane premium can actually get a deduction of of such percentage if it’s a certified building. Certified building means it has certain compliance standards and that you can check from such an AI digital

09:17 asset of the of the asset. And you also see that 70% 73% of the insurers agree that they claim fewer claims for hurricane damage if they have been checked and certified against the building. And just this is just one such certification you can do automatically with AI plus a lot of other certification and inside of the building that qualifies maybe for such programs.

09:48 But yeah, all three of them essentially depend on somebody knowing the building and and the state of the building. Um and that’s it. That’s essentially the talk. I think that the real estate class is very big and it really needs to be transparent and there’s a huge inefficiency happening day that I think with the new insights of what AI is capable to do we can make much more transparent and create a really new hyper-customized market around around assets.

10:20 Thank you very much. Um if you want to find out more, please visit my website spranik.com and thank you for your attention.