Small AI models on warehouse handhelds

The worker approves what they saw

The proposed description has to be easy to read on the terminal screen. If checking it means scrolling a long answer or switching to a separate chat, the help starts to add work. For this use, a short text next to the typed observation is enough, with the option to correct it before approval.

Missing information should stay visible. When a worker does not give the number of damaged packages, the app should not guess it. If the form requires that number, the worker has to fill it in. In the same way, what happens next with the goods follows the warehouse's rules and the permissions of the person handling the case. Editing a note gives the model no grounds to take that decision.

The receipt ID is best taken from the open record in the warehouse app. The worker should not copy it into a conversation and later check whether the model kept it. Where the task is reading a code, the right starting point is the scanning function you already have.

“AI on a warehouse terminal” can mean different things

Zebra describes the AI Data Capture SDK as tools for working with images, including reading barcodes and recognising text in a photo. These features do not prove that a given terminal can run a language model that edits a note. Reading a label and writing a sentence from a typed observation are separate tasks.

Google provides LiteRT-LM for running language models on Android. According to Google's documentation, the older MediaPipe LLM Inference API is now only maintained, and LiteRT-LM is the recommended successor. That is a reason to confirm with the supplier the current support for the proposed solution and the specific terminal.

So the conversation about hardware should start with the devices workers actually carry: their exact model, the system version and the apps running during a shift. The words “small model” do not guarantee a short wait or negligible battery use. The memory needed and the app's behaviour have to be checked on the chosen device.

A finished note may still be waiting to be saved

A local model can prepare text without sending the task itself to an outside server, if the software and the model it needs are on the terminal. Saving the approved note in the WMS, the warehouse management system, may still need a connection to its server.

If the app keeps the note on the device during a connection break, the worker should see that the text is still waiting to be sent. Once the WMS has received it, a clear confirmation is needed next to the right goods receipt. The worker then knows whether the colleague handling the case can already read the description in the system. A general “saved” message would leave that question open.

We also discuss how the whole app depends on the network in the post on access to factory manuals without the internet. On a warehouse terminal the question is different: can the worker finish the event description and recognise the moment the warehouse system actually received it?

Is the help worth the room on the terminal?

The most reliable test is work with a few real types of notes on the hardware you use. The worker should be able to compare the app with the current way of recording. If they regularly delete added details, or wait for a sentence they would have written faster themselves, such a rollout is hard to justify. For repetitive reports, a list of categories and a short text field may be enough.

When you assess time, count the first start, and then normal use of the terminal together with the warehouse app. Google notes in the LiteRT-LM documentation that loading the model can take noticeable time. Smooth handling and battery use have to be judged over a period that matches planned use. The result of a single note does not tell you how the device behaves during a shift.

Syntalith can help build a custom AI app that tidies the typed observation, shows the worker the text to approve and passes it to the agreed place in the warehouse system. The proposed start is a small trial on the terminal used in the warehouse. After it you can decide whether to develop the solution and what scope of WMS connection you need. Pricing information is on the pricing page.

For the first call it is enough to describe what workers type today and who those notes help later. The name of the terminal is useful too. You do not need to share WMS data straight away or prepare a full specification.

Passing the approved note on is worth looking at as part of warehouse document automation. Access to the WMS and the way the save is confirmed need a separate agreement.

What a small model on a warehouse terminal costs and when it pays back

The model runs on the terminal, so the scenario has no token or server cost. What remains is the worker's time to check the description and the cost of the app.

Where to start

In the free process review we choose one process and count the team's volume and working time, and within 2 working days you get a written summary with a price. We build as part of AI apps, and we price maintenance separately.