AI Development · Logistics & Supply Chain

AI in Logistics Software Development

AI built for demand forecasting, predictive ETAs, and freight document extraction, positioned as support for dispatchers, who keep the final call on routing.

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NDA before discovery

You own the code and IP on payment

Code in your repository from day one

A working demo every two weeks

Senior engineers do the work

How we work

Logistics generates the kind of high-volume, structured-but-messy data that AI handles well: shipment histories, telematics feeds, and a steady stream of freight paperwork that still gets keyed in by hand at most operations. The opportunity is real, but the failure mode is also specific: a dispatcher who stops trusting a predictive ETA after a few bad misses will simply ignore the tool going forward.

We build demand forecasting models that inform inventory positioning ahead of a peak season, predictive ETA models trained on your actual lane and carrier history rather than a generic distance calculation, and document extraction pipelines that pull structured data out of bills of lading, customs paperwork, and proof-of-delivery scans. Anomaly detection flags shipment exceptions (a stalled truck, a missed appointment) early enough for a dispatcher to act, rather than after the delivery is already late.

Every model is built as a decision-support layer for the humans running dispatch and planning, not a replacement for their judgment, since full route automation removes the flexibility an experienced dispatcher brings when a real-world exception (weather, a closed dock, a driver running behind) does not match the model's assumptions.

The problem

What makes logistics & supply chain projects different

Predictive ETA accuracy dispatchers will actually trust
An ETA model that misses badly a few times in a row gets ignored regardless of its average accuracy, so the model needs uncertainty bounds and needs to be trained on your specific lanes and carriers, not a generic distance-and-speed calculation.
Document extraction from inconsistent freight paperwork
Bills of lading and customs forms arrive as scans, faxes, and photos in varying formats across carriers and brokers, and an extraction model tuned on clean samples fails on the real documents that show up in daily operations.
Demand forecasting during volatile periods
Forecasting models trained on stable historical patterns tend to miss badly around peak seasons or demand shocks, which is exactly when accurate forecasting matters most for inventory and capacity planning.
Anomaly detection without alert fatigue
A model that flags every minor deviation as an exception trains dispatchers to ignore alerts entirely, so detection thresholds need tuning against what actually constitutes an actionable exception in your operation.
Data-sharing agreements with carriers and partners
Models trained on shipment and telematics data that spans multiple carrier or partner relationships need clear data-sharing agreements in place, since this data often has contractual restrictions on use and disclosure.

Scope

Features we commonly build

Demand forecasting
Forecasting models trained on historical shipment and order data, supporting inventory positioning and capacity planning ahead of demand shifts.
Predictive ETA modeling
ETA predictions trained on your actual lane, carrier, and seasonal history, with uncertainty bounds surfaced alongside the estimate.
Freight document extraction
Structured data extraction from bills of lading, customs forms, and proof-of-delivery documents, handling scanned and photographed inputs.
Shipment anomaly detection
Early flagging of shipment exceptions such as stalled transit or missed appointments, tuned to your operation's actual exception thresholds.
Dispatcher decision support
Model output surfaced inside the dispatcher's existing workflow as a recommendation with reasoning, not an automated routing decision.
Route and capacity signal integration
Weather, traffic, and carrier capacity signals folded into ETA and routing recommendations without removing dispatcher control over the final decision.

Compliance and standards

customs and trade documentation accuracy expectations
FMCSA data handling (where trucking is involved)
carrier data-sharing agreements

Common integrations

existing TMS/WMS systems
Samsara
Geotab
carrier EDI feeds
weather and traffic data APIs
Snowflake

Approach

How we run these projects

We start by identifying which decisions the model will inform, an ETA prediction a dispatcher reviews before customer communication is a different build than an automated re-routing system, and confirm data access and sharing terms with any carrier or partner data involved before model development begins.

We build the forecasting or extraction model against your historical shipment data first, validating accuracy against real outcomes rather than a synthetic benchmark, and pilot the dispatcher-facing interface with a small group of actual dispatchers before wider rollout. Code and infrastructure for these models remain in your environment from the first commit, and we sign an NDA before discovery starts.

Typical stack

Systems in this space

FAQ

Frequently asked questions

How accurate are predictive ETA models compared to standard distance-based calculations?

Accuracy depends on training the model against your specific lanes, carriers, and seasonal patterns rather than a generic distance and speed formula, and we build in uncertainty bounds so dispatchers see a range rather than false precision. Models trained this way generally outperform static calculations meaningfully once enough historical data is available.

Can AI extract data reliably from scanned bills of lading and customs paperwork?

Yes, though reliability depends on testing against your actual document mix, since formats and quality vary significantly across carriers and brokers. We validate extraction accuracy against real historical documents from your operation rather than a curated clean sample before considering the pipeline production ready.

Will an AI dispatching tool replace human dispatchers?

We build these tools as decision support, surfacing predictions and flagging exceptions for a dispatcher to act on, rather than automating routing decisions outright. Full automation removes the judgment an experienced dispatcher applies when a real-world exception, like a closed dock or a driver delay, does not match the model's assumptions.

How does demand forecasting handle unusual periods like a peak season or a demand shock?

Forecasting models trained only on stable historical patterns tend to underperform exactly when volatility hits, so we incorporate seasonal and event-based features and validate specifically against past peak periods, not just an overall accuracy average across the full year.

How long does it take to build an AI logistics forecasting or document extraction tool?

A focused pilot on one use case, ETA prediction for a specific lane group or document extraction for one document type, typically takes a couple of months including data preparation and validation. Expanding to additional document types, carriers, or forecasting scope extends the timeline based on data availability.