The five ALKARTIS products are the packaged version of a broader capability: taking fragmented data and connecting it into explainable intelligence. Sometimes the most valuable data isn't in a city's open portal — it's in your own spreadsheets, your internal database, or a public source we don't cover yet. Bring it, and we'll connect it to the public record and turn it into something you can decide on.
Everything ALKARTIS does packaged — resolving entities, cleaning and normalizing records, joining sources, and keeping a trail back to the origin of every figure — is a general capability, not a per-product trick. Bring Your Own Data simply points that engine at data you supply. Your list of properties, your roster of people, your internal event log, a niche dataset you scraped yourself: each becomes far more useful once it's connected to the surrounding public record and made explainable.
Your data usually isn't the whole picture — it's one layer of it. The value appears when it's connected to everything around it.
Enterprise accounts no longer wait for a kickoff call to get moving. Custom Data is built into the ALKARTIS app: upload a CSV or TSV and we parse it on the spot — inferring each column's type and showing you a preview — so you can see exactly how your data is read before anyone touches it. From there you choose what happens next.
Every dataset carries a full, auditable history — who uploaded it, when it was submitted, and every review decision — so nothing about how it's handled is a black box. The deeper connecting and verification work is still scoped with your team; the upload, the inferred schema, and the review status now live in the product. Open Custom Data in the app →
You don't need it to be clean or perfectly structured. Messy is normal — reconciling and normalizing it is part of the work.
Because every dataset is different, this is a custom engagement scoped case by case, not a self-serve product. It starts with a conversation: what you have, what you're trying to answer, and the decision the answer needs to support. From there we define the work — how your data connects to the public record, what the output looks like, and how you'll use it. You get an honest read early on about what's a strong fit and what isn't.
Two principles carry straight into custom work. First, the result stays explainable: findings trace back to the underlying records — yours and the public ones we connect them to — so you can audit, cite, and defend them rather than trust a black box. Second, how your data is handled — access, confidentiality, and treatment — is defined as part of the engagement before any work begins, so you know exactly how it's used.
A standalone dataset answers only the questions it was built to answer. The moment it's joined to the surrounding public record, new questions open up. A list of addresses becomes a picture of who owns them, what's been permitted on them, how they're assessed, and which carry violations. A roster of names or organizations can be checked against payroll, licensing, or property holdings. A hand-kept tracker stops being an island and starts carrying context it never held on its own. The lift isn't cleaning your data for its own sake — it's turning it into a vantage point onto everything the public record already knows.
None of that requires you to have solved the hard parts first. Reconciling inconsistent identifiers, resolving the same entity across sources, and normalizing formats so joins hold up is exactly the work the engine is built to do. You bring the data and the question; we handle the connecting.
Bring Your Own Data is the right path when the packaged products almost fit but your specific question needs your own data in the mix — a research project drawing on a dataset we don't publish, a diligence workflow that has to fold in your internal records, an analysis where the public record is context around a proprietary core. If instead you mainly need the connected public data itself, our public-records data provider and Philadelphia API pages are the better starting point.
A note on scope: our packaged public-records coverage is live in Philadelphia today and expanding city by city. The data you bring isn't bound by that footprint — a custom engagement is exactly where we work outside the packaged lines, case by case.
Spreadsheets, internal databases, exports from your own systems, or public records outside our current coverage. The common thread is that it describes people, places, or organizations that can be connected to the public record. In the initial scoping conversation we'll look at what you have and tell you honestly what's a good fit.
Both, now. Enterprise accounts can upload data directly in the ALKARTIS app — we parse it, infer its schema, and let you choose to match it into a private custom experience or submit it to be verified and published. The deeper work of connecting your data to the public record is still scoped case by case with your team, because every dataset is different — but getting started no longer waits on a call.
Yes. The explainability principle applies to custom work too. Findings trace back to the underlying records — yours and the public ones we connect them to — so you can audit, cite, and defend them. We don't hand back a black-box result you can't verify.
Handling of your data, including access and confidentiality, is defined as part of the engagement before any work begins. We'll walk through exactly how your data is treated during scoping. You can read more about our approach on the ALKARTIS trust page.
Our packaged public-records coverage is live in Philadelphia today and expanding city by city. Your own data isn't bound by that — the point of a custom engagement is to connect what you bring, and we scope each case on its own terms. Talk to our team about your specific data.
Enterprise accounts can upload it in the app right now — we'll parse it, infer the schema, and let you match it to a custom experience or submit it to be verified and published. Prefer to talk it through first? We'll scope the connecting work with you.