proprty.ai

Not every condition assessment has to start with a site visit. Sometimes a colleague was at the building anyway, and the photos land in your inbox. For Tily, the AI agent built on top of the domain-specific AI in proprty.ai, that's enough. In the video, you can see it turn that batch of photos into updated condition assessments, in bulk. Nobody had to go out and run the ordinary inspection workflow. And this is just one of the many jobs Tily can do. It's live in open beta, so if you want to see it on your own portfolio, reach out. proprty.ai knows your buildings. Tily works your way.

Henlæggelser er sjældent årets mest populære emne. Men som KAB skriver her, er de grundlaget for god vedligeholdelse af bygningsmassen. Og beslutningerne bliver kun gode, hvis dialogen bygger på et solidt databillede. Det er her, vi kommer ind. 50.000 boliger i KAB-fællesskaber er nu tilsluttet Bygningsservice, hvor vi leverer AI-genererede tilstandsvurderinger, kvalitetssikret af KABs egne specialister med Jonatan Michelsen i spidsen. Når driftsfolk og beboerdemokrati kigger på det samme opdaterede billede af bygningernes tilstand, bliver samtalen om henlæggelser lettere. Ikke fordi data træffer beslutningen, men fordi prioriteringen bliver gennemsigtig. Tak til KAB for samarbejdet. Og for at tage den svære snak op.

Datadrevet vedligeholdelse er ved at ændre spillereglerne for ejendomsbranchen. Vi har data på mere end 50.000 bygninger og 12 millioner m². For hver bygningsdel ved vi, hvilken type der er tale om, hvornår den er indsat, mængden, vurderet stand og planlagt vedligeholdelse. Derudover har vi faktiske, realiserede omkostninger fra over 6 millioner m². Alt det har vi brugt til at træne en AI-model, der vurderer nuværende stand og forudsiger restlevetid på tværs af 46 bygningsdele, både på komponent- og bygningsniveau. Det giver ejendomsejere og asset managers et markant bedre beslutningsgrundlag for prioritering af vedligeholdelse og investeringer. Hør Anders Holm Jorgensen sætte ord på, hvordan vores datagrundlag bliver til konkrete indsigter 👏

One person gets to build our Norwegian market. The tech is proven, our first Norwegian customers are on board, and the seed round is closed. What we don't have yet is someone on the ground in Oslo. And there's plenty of ground. Norwegian municipalities alone own around 27 million m² of public buildings with an estimated technical deficit of nearly NOK 300 billion, and three out of four are not on track to reduce it. You'll sit at Construction City in OBOS's offices, with OBOS opening doors and our Founder & CEO Anders as your sparring partner on strategy and the big deals. The rest is yours to build, with a genuine partner alongside you rather than a manager looking over your shoulder. Full role:https://lnkd.in/e2Yd3Zu3 Who's the best seller you know in Oslo? Tag them.

We've never met a portfolio with clean data. Not for lack of data. Bigger portfolios usually have plenty: an FM system, consultant reports, energy data, spreadsheets in every corner of the organisation and Henrik, who knows everything but retires next year. So the instinct is to tidy everything up before getting serious about maintenance planning. We get why. But the tidying tends to become a project of its own, and 18 months later you're still arguing about naming conventions in a shared folder while the roofs keep ageing at exactly the same speed as before. That's the problem we built proprty.ai around: you start with what exists. The system pulls public registers and whatever you already have into a first picture of condition and cost across the portfolio. Every estimate carries a confidence score, so you can see where the picture is solid and where it's guesswork, and send inspections to the buildings that genuinely need eyes on them. From there it only gets sharper. Every registration your team makes in the field strengthens the picture. Where does the knowledge about your buildings actually live today?

Nobody celebrates the roof that didn't leak. That is the strange thing about preventive maintenance. When it works, nothing happens. There is no emergency call, no urgent line in next year's budget, no story to tell at the Monday meeting. The new facade gets photographed and the fixed boiler gets a thank you, so the money quietly drifts toward whatever people can see. Meanwhile the work that extends lifetimes and protects budgets gets postponed, and technical debt grows in silence. The paradox is that prevention is where the maintenance budget works hardest. It can create 4x greater value, yet only the failures ever make the agenda. We don't think the answer is a bigger budget. It is making the invisible measurable: how long components will actually last, what postponing them will cost and where debt is building up. Once prevention has numbers behind it, it becomes much easier to defend. What is the best maintenance decision your team made that nobody ever noticed?

The property industry has enough standards. What it lacks is consistent adoption. Industry organisations have spent years developing shared structures, definitions and methods. But many property companies, advisers and tech providers still end up building their own version anyway. Every homemade version means another mapping exercise and more manual translation. The data loses a bit of value each time it changes hands. Good standards are not bureaucracy. They are infrastructure, the shared language that lets you compare buildings, connect systems and work across organisations. This shapes how we build proprty.ai. We're plugging into national building registers, established lifetime tables and the FM systems our customers already use instead of inventing a new language and asking everyone to learn it. Which standard do you wish the rest of the industry actually used?

Your maintenance budget won't cover everything. Now what? Most plans handle this badly. Something gets postponed, usually whatever shouts the least. The bill shows up later as technical debt. Our cost optimisation prioritises differently: Safety and legal requirements come first. Balconies at risk of collapse get fixed, full stop. Then damageability. A leaking roof damages the structure, ceilings and everything below it, so it jumps the queue. A worn-out kitchen hurts nobody but the cook, so it waits. Windows deteriorate fast without preventive maintenance, and replacement costs far more than paint. So the painting gets high priority. And when preventive maintenance costs almost as much as replacement? Skip it and replace earlier. Enter your actual budget, and the plan keeps only what fits. You also see the technical debt piling up from everything that got cut. How do you decide what gets cut when the budget is short?

Meet Scott Steenberg, the newest face on the proprty.ai team 👋   As we expand into new markets and new countries, more is required from us, so we're constantly growing and optimizing our team. That's why we're excited to welcome our very first student assistant on board. Scott will be working across marketing and RevOps, helping us get the word out and keeping our data and systems in shape.   He is currently studying computer science and brings hands-on experience from an early-stage startup, a mindset we value as a startup ourselves.   Outside of work, Scott is a serious endurance athlete. With a background as a professional triathlete competing at the highest level in Ironman, he still spends a lot of his time training. (We're also quietly counting on him as a huge asset for our potential future DHL Stafetten København ambitions 🏃)   Welcome aboard, Scott, we're glad to have you.

AI makes experience more valuable, not less. That sounds backwards. If less experienced employees can suddenly deliver work that used to take years of practice, why would deep expertise matter more? Because someone has to qualify the output. We see it in housing organisations rolling out proprty.ai to all property officials, including those without a building engineering background. They can now carry out condition assessments and draft proposals for maintenance work. At the same time, a central function with technical and economic expertise qualifies those proposals and prioritises the efforts. Knowledge becomes available to many. Experience ensures the right decisions get made. Anders Holm Jorgensen wrote about this shift on the blog: https://lnkd.in/esj_ut6w Who qualifies the AI output in your organisation, is that a defined role or whoever has time?