A distributor with trucks on the road is already collecting an enormous amount of information about how the business physically runs. Where every vehicle is. How long the stop took. What the driver photographed at the dock. Which behaviours the system flagged. All of it exists, all of it is accurate, and almost none of it reaches the reporting the operations team actually uses to make decisions — because it lives in the telematics platform and the reporting lives somewhere else. The fleet team looks at fleet screens. Everyone else looks at everything else.
Live vehicle GPS, with speed, heading, and a reverse-geocoded location so a position reads as a place rather than a pair of numbers. Completed trips. Safety events. Driver vehicle inspection reports. And driver-captured documents — bills of lading, proofs of delivery, and the rest of what your drivers photograph and submit from the cab.
Those five together describe the physical execution of the business: where things went, how they went, what the vehicle's condition was, and what paperwork came back. Individually each is a fleet-team concern. Joined to your operational data, they answer questions nobody in the office can currently ask.
The connection is the Samsara REST API, authenticated with a bearer token you issue. No screen-scraping, no export files, no intermediary — a documented API used the way it is meant to be used.
Trips are pulled from the stream endpoint, batched by vehicle and paged by cursor until the pages are exhausted. That is worth stating plainly because it is where competent-looking implementations go wrong: paging is easy to half-implement, and a half-implementation behaves perfectly on a two-day window and silently drops data on a two-month one. Following the cursor to exhaustion means a long window returns everything in it, which is the only condition under which a backfill or a month-end pull means anything.
Vehicle, safety, DVIR, and document endpoints are pulled by explicit time window. Explicit windows make pulls reproducible and re-runnable — you can ask for the same period twice and know exactly what you asked for, which matters the day something upstream fails and you need to fill a gap without double-counting.
The health check deserves its own paragraph, because it addresses the actual failure mode in this category. An API token can be entirely valid and still be missing a single scope. Authentication succeeds. The request is accepted. One endpoint returns nothing, a downstream feature produces an empty report, and everything on the surface says the integration is working. Nobody notices for a month, and then nobody trusts the numbers again.
So the integration probes every endpoint it depends on and names each one the token cannot reach. A permissions gap surfaces as a specific instruction — this endpoint, this scope, add it — rather than as an empty report and a guess. It is a small piece of engineering with a disproportionate effect on whether people believe what they are looking at.
A Samsara API token carrying the scopes the integration names, issued to a service account rather than to a person's login. Tokens tied to an individual are the most common way a working fleet integration dies: someone changes role, the login is deactivated, and the data stops.
Run the health check immediately after issuing the token and give the scopes it asks for. That is the entire setup conversation, and doing it in one sitting avoids the slow version where a missing scope is discovered weeks later.
A view on what the data is for. Trip history joined to deliveries is a different build from live position on an operations screen, and knowing which matters more decides what we do first.
Because a token can be valid and still be missing a scope, and that combination is the quietest failure in this category. Authentication succeeds, the request is accepted, and one endpoint returns nothing — so the feature that depends on it produces an empty report while every indicator says the connection is fine. The integration probes every endpoint it depends on and names each one the token cannot reach, so a permissions gap arrives as a specific instruction about which scope to add rather than as a report someone slowly stops trusting.
Trips come from the stream endpoint, batched by vehicle and paged by cursor until the pages are exhausted. That last clause is the whole answer. An implementation that takes the first page and moves on will look correct on a short window and silently truncate on a long one, which is exactly when you care — the month-end pull, the incident review, the backfill after an outage. Following the cursor to the end is slower and it is right.
Both, and they arrive by different routes. Vehicle location is current position with speed, heading, and a reverse-geocoded place name rather than a coordinate pair. Trips, safety events, inspections, and documents are pulled by explicit time window, which makes a pull reproducible: the same window asked twice returns the same thing, which is what lets you backfill or re-run without wondering what you double-counted.
They are pulled in as records attached to the trip and vehicle context around them — bills of lading, proofs of delivery, and whatever else your drivers capture in the app. The value is not the image, it is the join: a delivery document that sits next to the trip that produced it, queryable, instead of living in a mobile app that only the fleet team ever opens.
No. The integration reads. Samsara stays the system your drivers and fleet managers work in, unchanged, and the data is copied outward into reporting and whatever sits above it. Read-only is also the easiest posture for your team to approve, and it means the worst case of a bug is a bad report rather than corrupted operational data.
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