Reactor

Logistics & last mile

The data is everywhere. The intelligence isn't.

Every shipment runs through a dozen systems and none of them agree. That dispersed data is exactly why logistics keeps falling behind on AI. Reactor closes the gap, from raw signal to governed data product ready for the model.

Talk to our team
TMSTelematicsWMSCarriersLast mile500+ connectors

The problem

One delivery, a dozen systems. None of them agree.

A package or a sofa moves through order, TMS, warehouse, carrier, last-mile, proof of delivery, returns, and comms. Each one has its own IDs, statuses, and timestamps.

TMSTelematics / ELDWMSCarriers & LTLLast-mile dispatchProof of deliveryReturnsOMSCustomer commsYard & dock
12+

Systems per shipment

Order, TMS, WMS, telematics, carrier, last-mile, POD, returns, comms.

0

Shared definitions

Every system has its own idea of a delivery, a stop, and a status.

Weeks

To onboard a lane

Each new carrier or partner is a brittle custom pipeline that breaks.

Stalled

AI initiatives

Models starve without unified, trusted, replayable history to learn from.

See it work

One pipeline for your whole network.

Reactor resolves shipment, stop, vehicle, driver, and exception meaning across every system in real time, then lands governed data products in Snowflake, BigQuery, and Databricks, ready for AI.

dashboard.reactordata.com/mappings
LIVE
Reactor
AAcme Logistics
KN
Production
44
project44 · ETAs
Sa
Samsara · fleet
DT
DispatchTrack
M
Manhattan · WMS
Es
Estes · LTL
Tw
Twilio · SMS
SCID: shipment
SCID: stop
SCID: vehicle
SCID: driver
SCID: exception
SCID: order
Model: delivery
Model: route
Model: asset
Model: exception
S
SnowflakeDELIVERIES
B
BigQueryASSET_TELEMETRY
Db
DatabricksROUTE_ML
S
SnowflakeEXCEPTIONS

Map at ingest

Resolve shipment, stop, and vehicle the moment data lands, across TMS, telematics, WMS, carriers, and last-mile.

Replay history

Re-interpret past deliveries through new logic without re-pulling from a single source system.

Warehouse-native

Land governed delivery products in Snowflake, BigQuery, or Databricks, shaped for your models.

So what can you do with it

What's at your dock, before the truck arrives.

Reactor lands every system as governed, real-time data products in your warehouse. The tools that run your operation build on them. Here is a control tower your team could stand up, reading live from the products you just saw Reactor produce.

control-tower.acmelogistics.com
Acme LogisticsReactor
Live from Reactorcommon.delivery v3common.stop v2common.vehicle v4logistics.exceptions v1
Inbound
Live · 2:04 PM
18
Inbound today
2
Arriving early
4
At risk
6/8
Crews staged
PRO 88421 is arriving 40 min early with 12 two-person units. Crew B isn't staged until 3:00 PM.
TruckPredicted ETAOn boardHandlingDoor · CrewStatus
Es
EstesPRO 88421
1:38 PM40 min early
12 units · 6 two-person
White-gloveTwo-person
Door 4 · Crew BCrew gap
X
XPOPRO 77310
2:14 PMon time · ±8m
8 units · 220 lbs
Appointment
Door 2 · Crew AOn time
Fx
FedEx FrtPRO 51902
2:45 PMon time · ±12m
22 units · 1,940 lbs
Two-person
Door 6 · Crew COn time
Es
EstesPRO 88455
3:10 PM25 min late
5 units · 2 install
Install
Door 4 · Crew BDelayed
RL
R+LPRO 33218
3:30 PMon time
14 units · 980 lbs
White-glove
Door 1 · Crew AOn time
OD
Old DominionPRO 90012
4:05 PMscheduled
9 units · 410 lbs
Appointment
Door 3 · Crew CScheduled

And when you need the why

Your team can just ask.

Claude · Connected to Reactor
M
Maya · Last-mile Ops
Why are we missing delivery windows in the Northeast this week?
Claude
On-time delivery in the Northeast fell from 94% to 81% this week. Three drivers, in order of impact:
  • 01A DispatchTrack appointment-window change Tuesday pushed 1,240 big-and-bulky stops into the PM block, where crew capacity is short. Net: 480 missed windows.
  • 02project44 shows two LTL lanes out of the Carlstadt DC running +6.5h over dwell. Estes capacity is constrained.
  • 03Samsara flags 9 vehicles out of service, concentrated on the two-person white-glove routes.
Sourced from Reactor
4 data products · 6 source systems
common.delivery v3
project44, DispatchTrack, Estes
common.stop v2
DispatchTrack, Manhattan
common.vehicle v4
Samsara
logistics.exceptions v1
6 sources
Freshness: 4 minutes ago
All pipelines healthy
Suggested actions
  • →Reassign the 9 out-of-service routes to the 3 nearest available crews and auto-notify customers via Twilio.
  • →Revert Tuesday’s appointment-window change and rebalance PM stops back to AM.
  • →Pre-book Estes overflow capacity on the two delayed Carlstadt lanes.
Ops
Acts in minutes.
Plain-English answers they can dispatch against this afternoon.
Planning
Trusts the numbers.
Every metric ties to a versioned, governed data product.
Customers
Hear it first.
Accurate, proactive updates the moment status changes.

Why logistics lags on AI

AI needs trusted data. Logistics has the opposite.

Predictive ETAs, dynamic routing, and exception detection all need one thing first: unified, governed, replayable data. Reactor builds exactly that, so your team can finally ship the AI the rest of the world already has.

  • 1

    Preserve the raw history

    Every event from every system, logged immutably, so you can replay and re-model as the network changes.

  • 2

    Resolve shared meaning at ingest

    Map carrier, TMS, and last-mile fields to common shipment, stop, and vehicle entities before downstream sprawl.

  • 3

    Ship governed data products

    Land trusted delivery data in your warehouse with lineage attached, ready for models and analytics.

Source field → common entity
project44.shipment.etacommon.delivery.eta
dispatchtrack.stop.windowcommon.delivery.window
samsara.vehicle.gpscommon.vehicle.location
manhattan.order.refcommon.shipment.id
estes.pro.statuscommon.shipment.status

What it unlocks

The AI use cases logistics has been waiting for.

Once your data is unified and governed, the rest gets easy.

Predictive ETAs

Arrival windows people can trust.

Real-time, accurate ETAs across every carrier, mode, and final-mile partner, not a static promise date.

Dynamic routing

Routes and appointments that adapt.

Re-optimize stops, crews, and delivery windows as traffic, weather, and exceptions change through the day.

Exception detection

Catch the miss before the call.

Spot failed deliveries, missed appointments, and damage the moment signals diverge, not when the customer complains.

Capacity forecasting

Plan trucks, crews, and labor.

Forecast demand against one unified view so you staff warehouses and final-mile teams to reality.

White-glove scheduling

The right crew for every stop.

Match two-person teams, install skills, and equipment to big-and-bulky deliveries automatically.

Proactive comms

Updates that are actually right.

Trigger accurate, branded notifications the instant status changes, from any system, in one voice.

Big & bulky

Built for the hardest last mile.

Appointment windows, two-person crews, white-glove and install, returns, and handoffs between retailer, 3PL, carrier, and final-mile provider. Big and bulky has the most scattered data in logistics, which is exactly where unifying it pays off the fastest.

Appointments

Windows and reschedules, reconciled across every system.

White-glove crews

Two-person teams, skills, and equipment matched to stops.

Install & assembly

Service steps tracked as first-class delivery events.

Returns & reverse

Pickups and exchanges modeled alongside outbound.

FAQ

Got questions?

How Reactor closes the data gap that keeps logistics behind on AI.

01Why is logistics data so hard to use for AI?
A single shipment passes through a dozen systems, each with its own IDs, statuses, timestamps, and definitions of a 'delivery'. There is no shared, trusted view, so models either starve or learn from contradictions. AI needs unified, governed, replayable data, which most logistics stacks do not have.
02What systems does Reactor connect to?
TMS, telematics and ELD, WMS, carrier and LTL feeds, last-mile dispatch, proof of delivery, returns, OMS, and customer-comms platforms, plus 500+ general connectors. Reactor maps each one to shared shipment, stop, vehicle, and customer entities at ingest.
03How is this different from a TMS or a visibility platform?
Visibility tools show you status. Reactor builds the governed data foundation underneath: it resolves meaning across every system, preserves raw history for replay, and lands reusable data products in your warehouse that power your own AI, analytics, and automation.
04Why does big and bulky matter here?
Big-and-bulky last mile is the hardest: appointment windows, two-person crews, white-glove and install, returns, and multiple handoffs between retailer, 3PL, carrier, and final-mile provider. That is where data is most scattered, and where unifying it pays off the fastest.
05How quickly can we get value?
Reactor maps and models your sources at ingest instead of building brittle custom pipelines, so you onboard new lanes and carriers in hours, not weeks, and reprocess history without re-pulling from source.

Ready when you are

Bring AI to your last mile.

Unify your logistics data and ship the AI use cases that have been out of reach. Let us show you how fast it can move.

Talk to our team