An AI agent for logistics companies
that answers where's-my-order from real warehouse data

One question, asked a hundred different ways

A delivery company’s support inbox is dominated by one question: where is my order, why is it late, can I change the address. Industry benchmarks for delivery-heavy support volume put status inquiries at the majority of all inbound tickets, often well over half. Answering each one by hand means a support agent looking up the same order in the same warehouse system, over and over. The factual answer already sits in a database.

Exceptions get buried in that routine volume. A genuinely delayed or damaged shipment needs a human’s attention fast. But when ninety routine status questions arrive for every real exception, that exception can sit in the same queue until a customer escalates loudly enough to be noticed. By then the customer has usually messaged twice, called once, and formed an opinion no later apology fully undoes.

Channels add their own inconsistency. A customer who messages WhatsApp gets a different answer, or a slower one, than a customer who emails or uses a site chat. Different staff monitor different inboxes, with different access to the same warehouse data.

Support volume and delivery volume also spike together. The days with the most packages moving are exactly the days with the most status questions. That is precisely when a support team is least able to add a person for the overflow.

The agent we build for logistics and delivery companies

The agent reads every inbound message across WhatsApp, Telegram and your site chat. It answers status questions by looking up the real order in your warehouse or ERP system, not by guessing from a tracking number’s format. It tells a customer exactly what the system knows: current status, last scan location, expected delivery window if one exists. It says plainly when a status genuinely is not known yet, rather than inventing a date.

When a message signals an exception, say a shipment stuck past its window, the agent collects what a human needs: order number, what went wrong, any photos. It escalates straight to your ops team. Nothing sits in the routine queue. A daily digest keeps the team on top of unresolved exceptions without anyone scrolling through every conversation.

For companies running their own fleet or partnering with third-party couriers, the agent can be scoped to answer differently depending on which leg a shipment is on. A question about a last-mile delay then routes to the right partner, not a generic apology with no real update behind it.

Typical integrations: your warehouse management system or ERP as the source of truth for order status. The channels run on the WhatsApp and Telegram APIs plus a site chat widget. A daily digest bot in Telegram keeps your operations team current.

Two to three weeks, step by step

1. Audit of support volume. We read a sample of your real support conversations. We map what share is routine status questions versus genuine exceptions, and what data your warehouse system actually exposes.
2. Playbook. Status-lookup rules and exception-detection criteria, plus what the agent may and may not say about delivery dates. Written with your ops lead, reviewed before any code.
3. Build and connect. The agent is built against your real warehouse or ERP data, tested on recorded real conversations first.
4. Launch with guards. Rate limits per channel, a review queue for anything flagged as an exception, full conversation logging.
5. Tuning. Two weeks of reviewing real conversations with your support team and adjusting the script.

What it costs

Running cost is separate and usually $20 to $200 a month in model usage depending on message volume, with a hard budget cap set before launch.

What usually moves after launch

Automated status lookup is one of the largest levers on support cost for delivery-heavy operations, industry benchmarks show. Deflecting routine “where’s my order” questions typically cuts human-handled ticket volume by 40 to 60 percent. Faster exception detection usually shortens the time a genuinely delayed or damaged shipment sits unnoticed.

Separating exceptions into their own escalation path, instead of mixing them into the queue with routine questions, typically improves complaint-to-resolution time by a meaningful margin. The genuine exception no longer waits behind ninety routine ones. These are typical ranges reported across the logistics support vertical, not a promise. Order volume and existing system quality move the number.

Our own numbers sit in the case studies below. A factory ERP recovered from a lost cloud account and rebuilt self-hosted is detailed in the factory ERP recovery case study. It is now used daily for operations and reporting. A two-brand analytics warehouse with an AI analyst answering operational questions in Telegram is in the analytics hub case study.

Why work with us

We build these agents the way we build our own. A closed list of tools. Order and warehouse data checked on every answer. A human escalation path for anything that is not a routine status question. The price is fixed once we agree the plan. You get a working demo every week during the build, and the conversation logs and any system access stay in your name.

If your warehouse data lives in a system nobody can currently export cleanly, we will say so up front. We have recovered and rebuilt operational systems from exactly that state before, and that work sometimes needs to happen before an agent can answer anything reliably. We would rather fix the data layer first than build an agent on a shaky foundation that quietly gives customers confident wrong answers.

If your support team answers the same status question a hundred times a day, that volume is costing hours every week that could go to actual exceptions. Pair this agent with a proper data layer on the Analytics for logistics companies page. See the full package breakdown on the AI agents service page, or get a written plan with a fixed price for your operation.