Email-to-Load Automation Inside X-TMS
TED turns your inbox into an overnight dispatcher. Rate cons come in, ready-to-dispatch loads come out — all inside the X-TMS platform.
The morning rate-con backlog — and how X-TMS eliminates it
Every morning, dispatchers wade through dozens of overnight rate confirmations sitting in their inbox. PDFs from brokers, images from drivers, plain-text emails from customers. Each one has to be read, parsed, and re-typed into the TMS. Origin, destination, rate, weight, commodity, dates — over and over.
By the time the backlog is cleared, half the morning is gone. The first loads of the day are dispatched late. Drivers wait. Customers complain.
TED (Transport Email Dispatcher) eliminates the manual entry step entirely. Forward your rate cons to a dedicated inbox; TED reads them, extracts every field, and creates dispatch-ready loads — automatically, 24/7.
How it works
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1
Configure your dedicated TED inbox
X-TMS provisions a dedicated email address like loads@yourcompany.com (or you forward an existing inbox via SMTP / IMAP). TED monitors it continuously.
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2
Brokers send rate confirmations
Brokers attach PDFs, images, or paste rate-con text into emails — exactly as they always have. No workflow change required for the broker.
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3
AI parses every attachment
PDF, PNG, JPEG, plain text — all supported. AI extracts origin, destination, rate, weight, commodity, equipment, pickup/delivery windows, reference numbers, and contact info.
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4
Loads land in your dispatch board
Within 30–60 seconds, a fully populated load drops into your dispatch queue. Optional auto-assign rules can route it directly to a matching available driver.
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5
Dispatcher reviews + dispatches
Your dispatcher confirms the parsed data and assigns the driver. Total time per load: 30–90 seconds instead of 5–10 minutes.
By the numbers
What's included
- Dedicated inbox per tenant (loads@yourcompany.com)
- PDF + image (PNG/JPG) + plain text parsing
- All standard rate-con fields extracted: origin, destination, rate, weight, commodity, dates, references, contacts
- Automatic load creation in X-TMS
- Optional auto-assignment to matching drivers based on configurable criteria
- Dispatcher notification on every new load
- Updates existing loads on follow-up emails (amendments, status changes)
- Document sync — original PDF/image stored as load attachment
- Per-load confidence score so dispatcher sees which fields need review
- API webhook for custom downstream workflows
Best fit for
Eliminate the morning backlog — start dispatch with a clean board.
Overnight rate cons turn into loads automatically, ready for morning.
One dispatcher can handle 3–5× the load volume.
Same engine works for inbound carrier paperwork.
What to expect during rollout
Most teams underestimate the people-and-process work that surrounds any new technology. TED is technically straightforward to switch on, but a smooth rollout still benefits from a short coordinated effort across dispatch, IT, and ownership. Below is what we typically see in successful deployments.
Week 0 — Stakeholder alignment
Identify a single internal owner for the rollout. Confirm the metric you intend to improve (calls placed per day, hours saved per dispatcher, load-to-driver lead time, settlement cycle time — whichever applies). Align ownership, dispatch leads, and any affected drivers on what's changing and why. This step takes one or two short meetings, not weeks.
Week 1 — Pilot setup
Connect TED to a narrow scope first — one dispatcher, one lane, or a subset of customers. Validate that the integration behaves as expected on your real data. Capture any edge cases your operations have that the standard configuration didn't anticipate. X-TMS support is available throughout this phase.
Weeks 2–4 — Scale up gradually
Expand to more dispatchers, more lanes, or higher volume. Most teams scale to full production within 2–4 weeks of the initial pilot. Track the metric you committed to in Week 0; it's the honest signal of whether the deployment is doing what you bought it for.
Ongoing — Iterate
Review TED performance monthly with your team for the first quarter. Tune configuration (criteria, thresholds, weights) based on what's working and what isn't. This is normal — every AI-driven workflow benefits from a few iterations as it learns your specific operation.
Common pitfalls to avoid
Skipping the pilot. Teams that try to flip the switch globally on day one tend to discover edge cases at the least convenient moment — under live operational load. A one-week pilot prevents this.
No defined success metric. If you can't articulate what "good" looks like, you won't know whether the deployment succeeded. Pick one number and track it.
Treating AI as a black box. TED provides reasoning behind every recommendation. Take advantage of it during the first few weeks — your team learns the AI's logic, and the AI learns your team's preferences.
Frequently asked questions
What if the rate con is hand-scanned and blurry?
TED handles 90%+ of legible scans correctly. For poor scans, fields with low confidence are flagged for dispatcher review rather than dropped silently.
Can TED handle non-English rate cons?
Yes. TED has been tested with English, Spanish, German, French, and Polish rate confirmations. Other languages may need a brief tuning period.
What happens if two emails reference the same load?
TED detects amendments via load number, reference number, or content similarity and updates the existing load rather than creating a duplicate.
How long does setup take?
Most customers go live with TED in under 24 hours. Provisioning the inbox and configuring auto-assign rules takes about an hour.
Does TED replace my dispatcher?
No. TED removes the data-entry step so your dispatcher spends time on judgment calls (carrier choice, exception handling, customer communication) rather than typing.
Is the original rate-con stored?
Yes. Every parsed PDF/image is attached to the load record for audit trail and dispute resolution.