Find the repeat work.
Walk through one task your team does regularly. Identify the lookups, matching and review it actually needs.
BUILT FOR FLORIDA. BUILT AROUND YOUR WORK.
Your next restaurant file.
The records, connected.
We automate the public-record checks behind Florida restaurant and liquor-license projects. Purpose-built software for the research your team keeps repeating.
Explore a real exampleFor restaurant licensing
and permitting teams.
ONE FILE. A CLEARER PICTURE.
Two real public-record examples.
Choose a business. Follow the evidence.
| Business | Research question | Evidence |
|---|---|---|
| Loading the source-backed examples… | ||
Official source records, selected and checked for this demonstration.
These businesses are examples, not customers. No current-operation or transaction determination.
EXPLORE FURTHER
Describe your work. Explore relevant records.
Search a source-dated license snapshot.
| License | Source details |
|---|
Publisher extracts dated September 14, 2026 · downloaded September 20, 2026.
An inspection date is not an opening date. Select any row to inspect the evidence.
A useful interface should also know
when the records cannot answer.
02 / A SMALL START. A USEFUL RESULT.
The first project is a focused automation.
We agree on the job, prove the result,
and build from there.
Walk through one task your team does regularly. Identify the lookups, matching and review it actually needs.
Run a scoped pilot on real examples. Compare time, missed records and incorrect matches with your current process.
Choose a research page or a connection to your existing tools. Agree on ongoing data refresh, monitoring and support.
A two-week pilot, scoped from $2,000. Implementation quoted separately.
Managed hosting, source refreshes and support if you choose to continue.
03 / SUBSTANCE UNDER THE SURFACE
Our foundation combines Florida data pipelines, source history and a specialized matching model. Each customer workflow still has to earn its place on real work.
Yes. We have trained and evaluated a small model for narrow permit-reference and filed-role decisions. That is one component of the system. The table offers hosted Jev request interpretation and an optional local exact-filter mode. Interpretation creates an allowlisted filter plan; code applies it to downloaded records. Our locally trained v5 model is evaluated separately and is not running on this public site. The two restaurant examples are curated source comparisons. This is a focused demonstration, not a statewide research agent.
Source dates and retrieval dates are shown separately. A newly downloaded file can contain older information. This explorer covers selected food-license extracts; broader historical ingestion is paused. Each pilot needs an explicit source-coverage and refresh plan.
We start with your sources, rules and output requirements. Additional training is considered only when reviewed examples reveal a repeatable weakness and a separate test demonstrates an improvement.
Only what the checked sources support. A filing is not proof a restaurant opened. A registered agent is not automatically an owner. A shared address is not proof of a shared project. Unresolved records stay visible for review.
START WITH THE WORK.
15 minutes with Max Abramsky, founder.
One real task. A useful next step.
I build Florida data pipelines and research software. Let’s compare what your team does today with what we could automate.
max@earlyco.ai ↗REQUEST RECEIVED
No meeting is booked yet. You can also reach Max at max@earlyco.ai.