Building Consumer-Grade Applications on Palantir Foundry: An Ontology-Native Real Estate Product

Every real estate product eventually runs into the same quiet problem: the same address means five different things depending on which screen you're looking at. The listing feed spells it one way, the school data groups it by a different ZIP boundary, the buyer dashboard and the seller console each keep their own copy - and someone has to reconcile all of it, usually with a batch job that runs overnight and occasionally breaks without telling anyone.

Our team decided the reconciliation step itself was the thing to eliminate, not just optimise. The result is a property intelligence product built entirely on Palantir Foundry, where a property is modeled once, as a single ontology object, and every surface reads from that same object. There's no database to run, no API layer to maintain and no sync code between screens, because there's no second copy of a property anywhere in the system.

SECTION 01
The problem we were actually solving

Property data for a single address arrives from multiple external feeds that each describe it differently - different spellings, unit formats, ZIP groupings. Matching them is real resolution logic, not a string comparison. On top of that, the buyer-seller journey - browse, visit, offer, counter-offer - is logically one thread, but nothing was tying its steps to the same record. Asking price had no independent benchmark, so neither side could tell if a number was fair. And any of the three feeds could change schema or start throttling without warning - the kind of failure that doesn't announce itself, it just quietly corrupts the data downstream.

SECTION 02
What we built

Where people act

- Buyer workspace

A sortable grid, colour-coded map, faceted filters, a wish list, visit scheduling, a live mortgage simulator and an embedded AI assistant. 

- Seller console
A visit-request queue with approve/decline, offer review with counter-offer tracking, and property add/edit - against the same record the buyer sees.

- Compare view
Any two properties side by side on price, classification, beds/baths, school rating and shopping index - driven by bookmark variables, not a copied dataset.

The buyer workspace - faceted search, bookmark actions and the card grid, from Workshop's own widget library. Demonstration deployment: addresses, cities, prices and valuations are invented, and listing photography is blurred.

Where the platform forms a view

- Fair Market Value classification
Every listing gets one of four verdicts - Overpriced, Underpriced, Fairly Priced, Insufficient Data - from how far its price per square foot sits from its ZIP-code peer median.

- Market Trends dashboard
Listing count, asking price against fair market value, school rating and the classification mix - live aggregations, not a materialised report.

The compare view. Each pane is an independent selection over the same object set, driven by a bookmark variable rather than a copied dataset - which is why the two sides can never disagree about a property.

SECTION 03
Why Foundry is the interesting part

We wrote none of this as bespoke application infrastructure - each capability is a platform feature configured against our ontology. The descriptions below are of Aavya's configuration of Palantir Foundry, not of Foundry's capabilities in the abstract.

Capability 06 as the platform renders it. Each feed enters through its own egress policy and lands as its own dataset; enrichment layers join downstream, and everything converges on the object the surfaces read. This view carries schema and dependencies only - no listing data appears in it.

SECTION 04
A special mention: this UI is entirely Workshop

Every screen in this product - the buyer grid, the map, the photo gallery drawer, the pricing panel, the seller console, the charts on the market trends dashboard - is built natively in Palantir Foundry's Workshop. No custom frontend framework, no separate design system shipped alongside it. That mattered to us because "built on Workshop" and "looks like an enterprise dashboard" are usually treated as the same thing. We wanted to prove they don't have to be.

The Market Trends dashboard - metric cards, the live classification mix and price distribution, computed as aggregations over the same object set. Counts, valuations, classification shares and ZIP labels are invented for publication.

The photo gallery drawer and map panel, opened over the grid without leaving - it both Workshop widgets bound to the same Property object. The address and figures are invented, photography is blurred, and the map is an invented locality rather than a real one.

SECTION 05
What the platform decides, and what a person always decides

Nothing about an actual transaction is automated. The platform resolves data and forms a pricing signal - every decision that costs someone money stays with a person.

THE PLATFORM DECIDES

- Which records across feeds describe the same address
- Which enrichment layers attach, and the refresh cadence
- The Fair Market Value verdict - or when to withhold one

A PERSON ALWAYS DECIDES

- The actual asking price and listing terms
- Whether to request a visit or submit an offer
- Whether to accept, counter or reject an offer

SECTION 06

THE HONEST CAVEATS
We did not build this as a commercial real estate product, and it isn't being sold as one. We built it to demonstrate a different way of building applications on Palantir Foundry, using real estate as the use case that let us stress-test a consumer-grade interface and a governed ontology at once.

Ingestion isn't continuous - feeds refresh roughly every two to three weeks, a cost decision rather than a technical ceiling, and we don't call it real-time. A small share of listings carry no verdict at all, because their ZIP code doesn't have enough comparable listings for a reliable median. We make no accuracy claim yet for entity resolution or Fair Market Value; a formal backtest against actual sale prices is the next validation step. A verdict is a pricing signal - not an appraisal, not a valuation opinion, and not a statement about any specific property.

Built by Aavya on Palantir Foundry. This post is published by Aavya and describes Aavya's own product and Aavya's own configuration of Palantir Foundry. It is not published on behalf of Palantir Technologies Inc., does not represent Palantir's views, and is not a statement about Palantir's products or compliance certifications generally. Screenshots come from a demonstration deployment on licensed third-party feeds; every address, city, price, valuation, school rating, listing count, classification share, ZIP label and chart axis value shown has been replaced with an invented value, listing photography is blurred, and the map is a wholly invented locality — no real property, place or person is depicted. Statements about ingestion, permissioning, resolution and auditing describe how Aavya has configured this deployment at the time of writing; they are not security, compliance, certification or capability claims about Palantir Foundry. Forward-looking statements describe Aavya's own product intentions only. Press, analyst and investor enquiries relating to Palantir are referred to Palantir. Palantir, Palantir Foundry, Foundry, Workshop and AIP are trademarks of Palantir Technologies Inc.