One warehouse for dispatch, orders and delivery data
and an AI analyst who answers in numbers
Dispatch numbers sit in one system. Orders in another. Warehouse stock in a third. Delivery-partner data nobody has reconciled against any of them. We put it all in one warehouse and build dashboards on on-time rate and cost per delivery. Then we give you an AI analyst in Telegram who answers ops questions with real numbers.
Why the same numbers never agree
A logistics operation generates data everywhere and a single picture nowhere. Dispatch assigns and tracks routes in one system. Orders live in a store or CRM. Warehouse stock and fulfillment status sit in an ERP. Delivery partners report back in whatever format they use, often a spreadsheet or a weekly email. Industry benchmarks for multi-system logistics operations suggest a meaningful share of real delivery exceptions, delays, damage, wrong addresses, never surface in a dashboard at all. No single system owns the full picture end to end.
Cost visibility is another gap. Cost per delivery varies by route, partner, package size and time of day. Without joining dispatch, warehouse and partner billing data in one place, that variation stays invisible. The routes or partners quietly eating margin never get flagged until someone manually reconciles an invoice months later. By then the pattern has usually repeated for a full quarter, real money a weekly check would have caught in week one.
Speed is the third gap. Take a question like which route had the most exceptions this week, or which partner’s cost per delivery crept up. Answering it usually needs someone who knows SQL and has access to three different systems. The answer takes a day instead of a minute, if anyone asks at all.
A fourth problem shows up when a company adds a second city or delivery partner. The ad hoc spreadsheets and manual checks that just barely worked for one region stop working once there is a second to compare against. Nobody notices until the numbers for both regions quietly stop agreeing with each other.
What the warehouse actually joins
A single PostgreSQL warehouse joins your dispatch system, order data, warehouse or ERP stock and fulfillment records, and delivery-partner reports into one place. It syncs daily with backfill, so history is never lost. Each source gets a raw layer first. Then come marts built around the questions that actually matter operationally: orders, routes, cost per delivery, on-time rate, and exception rate by route and partner.
Dashboards surface what a dispatcher or an ops manager needs at a glance. On-time rate trending by route or region. Cost per delivery broken out by partner and package type. Exception rate, so a route or partner quietly degrading gets caught before it becomes a pattern of complaints. An AI analyst sits on top in Telegram. Ask it which route had the worst on-time rate last week. It answers with the SQL query it ran shown alongside, so nothing is a black box.
Typical integrations are your dispatch or route-planning system, your warehouse management system or ERP, and delivery-partner data feeds by API, CSV or email. Telegram handles alerts and the AI analyst interface.
For operations juggling several delivery partners, the warehouse normalizes each partner’s reporting format into one schema. A comparison between partners becomes an actual apples-to-apples query, not someone lining up three differently structured spreadsheets by hand once a month.
How the build runs
- Audit. What exists across dispatch, orders, warehouse and delivery-partner systems, what is already tracked, and what the real data quality looks like. One week.
- Model. Which operational questions the business must answer. Marts are designed around on-time rate, cost per delivery and exception tracking from the start.
- Connectors and sync. A raw layer for each source, daily jobs, backfill, and reconciliation checks against what dispatch and the warehouse actually report.
- Dashboards and alerts. Built with the ops team who will use them day to day, with anomaly alerts to Telegram.
- AI analyst. A read-only database role, guarded SQL with forced limits and timeouts, tested on real recorded questions before going live.
What it costs
| Package | Price | What it covers | Timeline |
|---|---|---|---|
| Tracking audit | from $800 | What your current systems actually see and miss, with a fix list | 1 week |
| Warehouse + dashboards | from $2,500 | Connectors, daily sync, marts, dashboards on on-time rate and cost per delivery | 3 to 6 weeks |
| AI analyst + monitoring | from $5,000 | Everything above plus the AI analyst in Telegram and delivery-partner anomaly alerts | 6 to 10 weeks |
What the numbers usually look like
Industry benchmarks look at logistics operations reconciling dispatch, warehouse and partner data for the first time. They typically find that 15 to 30 percent of real delivery exceptions were invisible to existing dashboards, because no single system had the full picture. Operations with a working on-time rate and cost-per-delivery dashboard commonly catch route or partner cost drift within weeks instead of months. That happens once the numbers are checked daily instead of reconciled quarterly. Companies running more than one delivery partner usually find the comparison itself is where the first real savings surface. That happens once partner data sits in one normalized schema instead of three incompatible reports.
These are typical ranges reported across multi-system logistics operations, not a guarantee. Existing data quality and system count move the number for any single company. Our own numbers are in the case studies linked below. One warehouse we built for two brands ran 1,025 automated tests. Its AI analyst caught 184 of 218 price-undercut events with zero false alarms. Details are in the analytics hub case study. A factory ERP migrated off a third-party cloud and rebuilt self-hosted, with cost accounting and plan-versus-actual reporting. See the factory ERP recovery case study.
Why Senator Media
We build these warehouses the way we build our own. A guarded, read-only SQL layer for the AI analyst, with forced limits and timeouts. We audited our own guard before trusting it with live data, and that audit found and closed several gaps on a comparable project. The price is fixed once the plan is agreed. You get a working demo every week during the build. The warehouse and all access stay in your own account.
If nobody can say which route or partner is quietly costing you money this month, that gap compounds every week it goes unmeasured. We would rather start with the tracking audit and show you exactly where the blind spots are. That beats selling a full warehouse build before either of us knows what the real data looks like. Pair this with an AI agent for logistics companies, so customer-facing status questions get answered from the same clean data. See the full package breakdown on the analytics service page, or get a written plan with a fixed price for your operation.
FAQ
What does it cost to start?
A warehouse plus dashboards build starts at $2,500 and takes 3 to 6 weeks. You get connectors to dispatch, orders and warehouse systems, plus marts and dashboards on on-time rate and cost per delivery. Want to see the gaps first? A lighter tracking audit alone starts at $800 and takes a week.
How long until we see real dashboards?
Three to six weeks for the full warehouse and dashboards, depending on how many systems need connecting and how clean the existing data is. We tell you honestly if a source system's data quality will slow things down.
Can the AI analyst answer questions about a specific route or driver?
Yes, as long as that data exists in your dispatch or warehouse system. It runs guarded, read-only SQL against a prepared data mart, one query at a time, and shows the query it ran so you can verify the answer.
Does this work with our existing ERP or warehouse management system?
We connect to most ERPs and warehouse systems that expose an API or a reliable export, including custom and self-hosted systems. We have recovered and rebuilt a factory ERP from a lost cloud account before, so imperfect source systems do not scare us.
Is our delivery and customer data kept private?
Yes. The warehouse lives in your own database, under your account. The AI analyst runs on a read-only role with no write access and no way to export raw customer data outside the guarded query layer.
Can it monitor delivery partners or third-party couriers?
Yes, if the partner exposes any data feed, even a simple CSV or email report. We have built monitoring that catches cost and performance anomalies across multiple partners automatically.