AI infrastructure for logistics

Logistics runs on documents that never stop arriving and context that's constantly changing. Manifests, invoices, bills of lading, and shipping documents flow in continuously, tracking data updates by the hour, and the operational picture spans many shipments, carriers, and routes at once. If you're building operations tools for supply chain and logistics, the model isn't the hard part. The infrastructure is: extraction that keeps up with the document flow, a way to keep changing data current, and memory that holds context across shipments and carriers.

This page is for teams building logistics and supply chain tools. Exabase gives you document processing through Extract, autonomous upkeep through Workers, operational memory across shipments and carriers, and per-client or per-route isolation through Bases. The continuous flow gets processed without manual runs, and an operations agent works from a current picture rather than a stale one.

What you can build

Logistics tools tend to be one of a few shapes, each on infrastructure that already exists.

A document processing pipeline for manifests, invoices, and shipping paperwork that turns the continuous flow into structured data, document extraction at scale and invoice and receipt processing with Workers on the stream.

An operations agent that holds context across shipments and carriers, what's where, which carrier handles what, what's gone wrong before, long-term memory scoped to the operation.

A self-updating tracking knowledge base that re-processes changing data so the operational picture stays current, the self-maintaining knowledge bases pattern.

A per-client or per-route operations tool that keeps each client's or route's data and context isolated, Bases scoped to whatever unit the operation uses.

Logistics problems, solved

The problems logistics builders run into are specific, and each has an answer.

A continuous flow of documents. Manifests, invoices, and shipping documents arrive constantly, in every format. Extract turns them into clean, structured text through one API, including scans, and Workers process the stream on a schedule, so the pipeline keeps pace without a manual run for each batch. It's the scheduled-processing pattern from invoice and receipt processing.

Data that's always changing. Tracking and status data updates constantly, and a static snapshot is out of date fast. Workers re-process changing data on a schedule, so the operational picture stays current, the self-maintaining knowledge bases pattern applied to logistics data.

Context across shipments and carriers. The operational picture spans many moving parts. Memory holds context across shipments and carriers, what's happened before with a carrier, the state of a shipment, and keeps it current through contradiction resolution, so an agent reasons from the current operational reality.

Keeping clients or routes isolated. A logistics operation serves many clients and runs many routes, and their data should stay separate. Bases make isolation structural, per client or per route, from a single API call. It's the multi-tenant memory pattern, scoped to whatever unit the operation needs.

The infrastructure underneath

Four primitives carry most logistics tools. Extract turns manifests, invoices, and shipping documents into structured data at scale. Workers keep the document flow processed and tracking data current. Memory holds operational context across shipments and carriers. Bases isolate per client or per route from a single API call. Deep Search makes the processed documents findable by meaning. One API key, rather than building and operating extraction, scheduling, and isolation separately.

Built to keep pace with the operation

A logistics tool on this foundation scales with the document flow and stays current with the operation, which is the combination hand-built pipelines struggle with. Extraction and scheduled processing handle continuous volume that would overwhelm a manual setup, and Workers keep the operational picture current as tracking and status data changes, so an agent isn't reasoning from yesterday's snapshot. Adding a client or a route is one API call to create its isolated environment, so the separation holds whether the operation runs dozens of routes or thousands, and each client's or route's context compounds within its own environment as shipments accumulate. The undifferentiated work, parsing every document format, queueing, keeping data fresh, isolation, stays the platform's problem while the operation grows.

Get started

Start with the getting started guide, then the use-case pages that match what you're building: document extraction at scale and invoice and receipt processing for documents, self-maintaining knowledge bases for keeping data current, long-term memory for any agent for operational context, and multi-tenant memory for SaaS for per-client or per-route isolation. There's a free tier to build against.

FAQs

Can it process manifests, invoices, and shipping documents at scale?

Yes. Extract turns logistics documents into clean, structured text through one API, handling scans and varied formats, and Workers process the continuous stream on a schedule.

How does the operational picture stay current as data changes?

Workers re-process changing data on a schedule, so tracking and status stay current rather than freezing at a snapshot. It's the self-maintaining knowledge bases pattern applied to logistics data.

Does an agent hold context across shipments and carriers?

Yes. Memory holds operational context across shipments and carriers and keeps it current through contradiction resolution, so the agent reasons from the current operational reality rather than a stale picture.

Can I isolate per client or per route?

Yes. A Base isolates whatever unit you scope it to, a client, a route, an operation, so their data and context stay separate. It's the multi-tenant SaaS pattern.

Can processed documents be searched?

Yes. Stored as searchable content and indexed for Deep Search, manifests and shipping documents stay findable by meaning, so finding a specific document or shipment is a query.

Is this a finished logistics product or something I build on?

Something you build on. Exabase is the infrastructure, document processing, scheduled upkeep, operational memory, and isolation, and you build the operations or supply chain tool on top.