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

The problem in logistics and delivery companies

A delivery company’s support inbox is dominated by one question asked a hundred different ways: where is my order, why is it late, can I change the address. Industry benchmarks for delivery-heavy support volume put “status inquiry” at the majority of all inbound tickets, often well over half, and answering each one by hand means a support agent manually looking up the same order in the same warehouse system, over and over, for questions that have a factual answer sitting in a database already.

The second problem is exceptions that get buried in routine volume. A genuinely delayed or damaged shipment needs a human’s attention fast, but when ninety routine status questions arrive for every one real exception, the exception can sit in the same queue as everything else until a customer escalates loudly enough to be noticed. By the time that escalation happens, the customer has usually already messaged twice, called once, and formed an opinion about the company that no later apology fully undoes.

The third is inconsistency across channels: a customer who messages WhatsApp gets a different answer, or a slower one, than a customer who emails or uses a site chat, because different staff monitor different inboxes with different access to the same underlying warehouse data.

A fourth, specific to the busiest periods, is that support volume and delivery volume spike together. The days with the most packages moving are exactly the days with the most status questions, which is precisely when a support team is least able to add a human for the overflow.

What we build for logistics and delivery companies

An AI agent that reads every inbound message across WhatsApp, Telegram and your site chat, and 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, and says plainly when a status genuinely is not known yet rather than inventing a date.

When a message signals an exception, a shipment stuck past its expected window, a damage complaint, a wrong address, the agent collects the structured details a human needs (order number, what went wrong, any photos) and escalates immediately to your ops team instead of letting it sit in the same queue as routine questions. A daily digest keeps the team on top of unresolved exceptions without anyone having to scroll through every conversation.

For companies running their own fleet or partnering with third-party couriers, the same agent can be scoped to answer differently depending on which leg of the journey a shipment is on, so a question about a last-mile delay gets routed to the right partner instead of a generic apology with no real update behind it.

Typical integrations: your warehouse management system or ERP as the single source of truth for order status, WhatsApp Business API, Telegram Bot API and a site chat widget for the channels, and a daily digest or alert bot in Telegram for your operations team.

How it works in 2 to 3 weeks

1. Audit of support volume. We read a sample of your real support conversations and map what share is routine status questions versus genuine exceptions, and what data your warehouse system actually exposes.
2. Playbook. Status-lookup rules, exception-detection criteria, what the agent may and may not say about delivery dates, written with your ops lead and 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.

Typical results

Industry benchmarks for delivery-heavy support operations put automated status lookup as one of the largest levers on support cost: deflecting routine “where’s my order” questions typically reduces human-handled ticket volume in the 40 to 60 percent range, and faster exception detection usually shortens the time a genuinely delayed or damaged shipment sits unnoticed. Operations that also separate exceptions into their own escalation path, rather than mixing them into the same queue as routine questions, typically see complaint-to-resolution time improve by a meaningful margin simply because the genuine exception is no longer waiting behind ninety routine ones. These are typical ranges reported across the logistics support vertical, not a promise, since order volume and existing system quality move the number. Our own numbers are in the case studies linked below: a factory ERP recovered from a lost cloud account and rebuilt self-hosted, now used for daily operations and reporting, is detailed in the factory ERP recovery case study, and a two-brand analytics warehouse with an AI analyst answering operational questions in Telegram is in the analytics hub case study.

Why Senator Media

We build these agents the way we build our own: a closed list of tools, order and warehouse data checked on every answer, and a human escalation path for anything that is not a routine status question. The price is fixed once the plan is agreed, 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 tell you honestly that the data layer needs fixing first than build an agent on top of a shaky foundation and watch it quietly give customers confident wrong answers.

If your support team is answering 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.