Show it before you sell it.
Every dataset has a public schema, fill rates and free sample rows. If it doesn't hold up in a preview, it doesn't ship.
ABOUT DEVINTEL
Companies, the places they occupy, the people moving between them, the goods they trade and the markets that price them. We turn each layer into clean, verified rows that line up with the others.
Every registered business with its type, status, city and state, pinned to the building it occupies.
Points of interest on real building footprints: cafés, grocers, clinics, gyms and offices, each with a category.
Anonymized, aggregated visits and dwell times showing how people move between places.
Shipments between ports with commodity codes, volumes and declared values.
Prices, funding rounds and hiring signals that put a number on activity.
HOW WE BUILD IT
Watch one record travel through the pipeline. Every row in every dataset takes the same path.
Raw records arrive from state registries and open data sources.
Names, addresses and states are parsed to one standard.
Geocoded to a building and checked against other sources.
Delivered as a clean CSV, one schema for every state.
companies, places, movement, trade and markets, all on shared IDs
with more shipping every quarter
records cross-checked against state registries and open map data
or faster, depending on the dataset
a single state, never the whole catalog
WHAT WE BELIEVE
Every dataset has a public schema, fill rates and free sample rows. If it doesn't hold up in a preview, it doesn't ship.
Shared IDs, normalized states and WGS84 coordinates, so companies, places, movement and trade join without cleanup.
One state at a time. Coverage grows with your question, not with our price list.
WHO IT'S FOR
Pull every active company in the states you sell into, with coordinates to plan routes and coverage.
SELECT entity_name, city
FROM companies
WHERE state_code IN ('GA','AL')
AND status = 'Active'
Match applicants to a verified entity, its parents and its physical location in one lookup.
SELECT e.entity_id, r.parent_id
FROM companies e
JOIN relationships r USING (entity_id)
WHERE e.entity_name ILIKE 'northwind%'
Combine businesses, foot traffic and rental prices around any building footprint.
SELECT entity_type, COUNT(*)
FROM companies c
JOIN foot_traffic f USING (entity_id)
WHERE ST_DWithin(c.geometry, :site, 500)
Track openings, trade flows and prices over time with consistent IDs across every dataset.
SELECT state_code, year, SUM(value_usd)
FROM trade
GROUP BY state_code, year
ORDER BY year
OUR STORY
Data teams were buying whole countries of data, then spending weeks fixing names, addresses and duplicates.
We verify every record against a physical footprint, not a ZIP code centroid.
Shared IDs and normalized states so every dataset joins with the next.
16.3M companies across 12 states, 14 columns, sold one state at a time.
Places, movement, trade and markets on the same IDs.
LET'S BUILD ON IT
Tell us what you're trying to answer. We'll show you which layers, states and columns get you there.