INDUSTRY // REAL ESTATE

Real estate software, stated honestly: a real client, no public case study yet.

We have one real-estate client, Polibit, and you will find their co-founder's testimonial on our homepage. What we do not have yet is a public case study in this industry, so this page stays in the know-how column until we can show the work. What backs the know-how up is close by: real-world-asset tokenization rails that processed real money, and an institutional platform that scores assets for banks.

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2 adjacent projects
4 questions answered
3 related services
Know-how

“Tokenized real estate is two hard problems wearing one buzzword: a regulated financial product and an asset platform institutions trust. We have shipped both halves separately.”

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What we build in this industry

Asset tokenization rails

On-chain ownership, fiat and crypto payment lanes, and the compliance-aware architecture EU tokenization requires. The LivLive rails, pointed at a different asset class.

Valuation & analytics platforms

Scoring engines and data platforms built to institutional standards, the discipline we proved on corporate debt.

PropTech MVP

A focused product shipped in months on a fixed-price SoW, for founders who need to show a working platform to investors and first customers.

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What makes this industry hard

Tokenization is a regulated product first

Fractional property ownership in the EU is a securities and MiCA conversation before it is a smart-contract conversation. Architecture decisions made before that analysis tend to get unwound expensively.

Institutions demand defensible models

A valuation algorithm that cannot explain itself does not survive an asset manager's due diligence. We have built rating logic that did.

Real estate data is fragmented and stale

Property data lives in registries, PDFs, and silos with no API. Most PropTech effort is data engineering, unglamorous and decisive.

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Where our experience actually comes from

Where this know-how actually comes from, named and checkable:

Polibit is a real-estate client we advise and build for in the Web3 space. Gabriela Mena, Polibit’s co-founder and CEO, wrote the long testimonial on our homepage. The engagement has no public case study yet, which is exactly why this page does not claim one.

LivLive is a real-world-asset platform whose rails we built end to end: on-chain ownership primitives, fiat and crypto payment lanes through one signed-webhook handler, and $2M+ raised through the product. The asset class was experiential rather than property, which is why it sits here as adjacent work.

The bond analytics platform is institutional asset scoring: a proprietary rating algorithm banks and asset managers use to price corporate debt. Property valuation models face the same bar: defensible methodology, auditable data, institutional scrutiny.

On the regulatory side, we have done the MiCA and FMA homework that EU tokenization projects stand on, from the engineering seat.

What we have not done: MLS/IDX integrations, property management systems, or transaction platforms for brokers. If that is your core, a PropTech-native shop may serve you better, and we will say so quickly.

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Adjacent work, honestly labeled

Adjacent work, not real-estate case studies. These are the projects the technical claims above come from.

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FAQs

Polibit is a real client, and their co-founder’s testimonial is public on our homepage. A case study needs outcomes we can publish with the client’s blessing, and that has not happened yet. Until it does, this page claims know-how, not a track record.
We can build the rails: we have shipped RWA tokenization that processed real money. Whether your structure is legally tokenizable in your jurisdiction is your counsel’s call, and we work alongside them rather than around them.
If your project is core brokerage tooling, MLS integrations or property management, the specialist is probably right and we will tell you so. If it is tokenization, valuation modelling, or an AI layer on property data, the hard parts are the parts we have shipped.
Not with property registries specifically. We have built data platforms on messy financial data to institutional standards, which is the same discipline against a different source. We state that distance instead of hiding it.
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