Approach

The applications aren't the moat. The intelligence engine is.

Public records are usually treated as separate silos — one office publishes permits, another publishes assessments, another publishes parcels. Alkartis treats them as one connected layer, because in the real world, they already are.

The layers
01

Open Data Connectors

Ingests live public records, city by city.

02

Knowledge Graph

Links parcels, permits, and people into one connected structure.

03

AI & Prediction Models

Reads relationships across records to surface what's likely next.

04

Applications

Each app is a purpose-built lens onto the same underlying engine.

The intelligence model

Data alone isn't intelligence. This is the journey every dataset takes.

Data Connected information Context Insights Patterns Predictions Recommendations Decisions Action

The datasets change from application to application. This journey doesn't. Most platforms stop at the first step or two — a dashboard, a report, a search box. Alkartis keeps going, all the way to the decision in front of you.

What makes this different

Most platforms organize data.

Alkartis organizes understanding.

Most platforms tell you what happened.

Alkartis explains why it happened.

Most platforms provide reports.

Alkartis provides context.

Most platforms end with information.

Alkartis begins there.

Why relationships matter

A single data point rarely tells the full story. A building permit can be an early signal of new housing. New housing can shift who's registered to vote nearby. A transit upgrade often precedes new development. None of these facts are useful alone — they're useful because they're connected.

That's the actual product: not a dataset, and not even a single application — but a growing, explainable map of how public records relate to one another, and what that means for the decision in front of you.

"Every design decision should answer one question: does this increase trust? If it increases excitement but decreases trust, we remove it."

Why now

Open data mandates created the supply. AI created the ability to use it.

The data already exists

Open records laws already require cities to publish payroll, assessments, permits, and more. The supply problem is solved — almost no one has solved the usability problem.

AI makes it tractable

Reconciling messy, inconsistent civic schemas at scale used to require a large in-house data team per city. Modern AI models make that reconciliation fast enough to run as a platform, not a custom project.

The model is already validated

Compensation Atlas and TaxAssessmentLab aren't concepts — they're shipped, live, and built on the same engine. The question isn't whether this works. It's how fast it extends.

The expansion playbook

Every new city and dataset makes the next one cheaper to add.

01

New city

A new open-data connector plugs into the existing engine — no rebuild required. Los Angeles, Boston, New York City, and Chicago are next on the roadmap.

02

New dataset

Permits, parcels, elections — each new category enriches the knowledge graph for every city already connected.

03

New application

A new lens ships against infrastructure that already exists, rather than starting from zero.

The long-term vision

In time, people won't say "I use Compensation Atlas."
They'll say "I asked Alkartis."

The applications aren't the destination. The Intelligence Engine is — a single, explainable map of how public records relate to one another, growing city by city and dataset by dataset.

Talk to the team