SOLUTION · LOGISTICS
Logistics AI: Smarter Planning, Steadier Delivery
Collapse the decisions scattered across your TMS, WMS, fleet, and dispatch into one unified operations dashboard. AI handles the routine; humans and AI collaborate on the exceptions. Regional pilot live in weeks.
In logistics, the problem isn't missing data — it's deciding too late
The bottleneck in logistics operations is rarely visibility — it's reaction time. Orders live in the OMS, vehicles in the TMS, inventory in the WMS, and exceptions in a dispatch inbox or a driver chat group. A single dispatcher hops across six or seven systems a day, closing gaps by experience and phone calls. When traffic, a vehicle shortage, a last-minute order change, or a bad last-mile address hits, the team usually finds out after the fact — and the cost to recover is several times what it would have been at the planning stage.
Tenten AI's approach isn't to hand you yet another system. We connect your existing TMS, WMS, and OMS through APIs into one unified operating view, and run AI continuously in the background for route planning, freight estimation, document matching, and exception detection. Routine, repeatable decisions get automated. Anything that touches customer relationships, cost tradeoffs, or safety escalates into a human-AI collaboration ticket — AI assembles the context, options, and a recommendation, and the dispatcher reviews, adjusts, and approves.
We're an FDE (Forward-Deployed Engineer) team. Our engineers embed in your operation and ride a real shift of schedules and exceptions alongside your dispatchers, instead of guessing from a requirements doc. We start with one region, one lane, or one warehouse, go live within weeks, prove ROI against real volume, and then replicate across the network.
Capabilities
01
Unified operations dashboard
Signals from your TMS, WMS, OMS, and fleet telematics merge into a single view — orders, vehicles, inventory, and exceptions on one screen, so dispatchers stop reconciling across systems.
02
AI route and load optimization
It factors in time windows, vehicle constraints, backhaul, live traffic, and fuel cost to generate optimal routes and consolidations — and recalculates automatically, pushing adjustments when conditions change.
03
Shipping-document automation
AI extracts fields from bills of lading, waybills, customs, and proof-of-delivery documents, cross-checks them against orders and freight, and flags mismatches — cutting manual data entry and reconciliation to a minimum.
04
Exception management workflow
Delays, vehicle shortages, order changes, bad addresses, and damage are auto-classified and prioritized. AI prepares context and a recommended action; the dispatcher approves with one click, and every decision leaves an auditable trail.
05
Driver-side AI assistant
Drivers use plain language to check the next stop, report exceptions, and upload proof-of-delivery photos. AI writes it back to the system and updates the ETA automatically, cutting phone tag and information gaps.
06
Native TMS/WMS integration
We connect to your existing TMS, WMS, and ERP via API — no core-system replacement, no data migration. It deploys inside your VPC so data never leaves, meeting enterprise security and audit requirements.
Use cases
Multi-temperature delivery routing
For fleets mixing cold-chain and ambient loads, AI plans routes and consolidations around time windows, temperature constraints, and backhaul — recalculating instantly when traffic hits or orders are added, reducing empty miles and late drops.
Customs and shipping-document reconciliation
On cross-border freight, AI extracts fields from bills of lading, invoices, and packing lists and cross-checks them against orders and customs data, flagging mismatches in description, quantity, or HS code to speed clearance.
Real-time last-mile exceptions
Incomplete addresses, recipient-not-home, and reschedule requests are detected and triaged automatically. AI recommends a reassign or rebook, and once the dispatcher approves, both the driver and the customer are notified.
Vehicle-shortage and capacity dispatch
At peak or on a sudden shortage, AI weighs in-transit vehicles, outsourced capacity, and cost to produce a coverage plan — turning the hidden hours of 'calling around for a truck' into a few minutes of review.
Driver field reporting and ETA updates
Drivers report unloading delays or damage in plain language from a chat app; AI classifies it, writes it back to the system, and recomputes downstream ETAs so dispatch and customers see the latest status immediately.
Delivery cadence
WEEK 1
Embed and map the operation
Engineers embed, ride a real shift of schedules and exceptions with your dispatchers, inventory the TMS/WMS/OMS data sources, and pin down the most painful exception types and the ROI baseline.
WEEK 2–3
Pilot MVP goes live
We pick one region or lane, connect the APIs, and ship the unified dashboard, route optimization, and exception workflow — validating against real volume in shadow mode.
WEEK 4–6
Tune the human-AI loop
We adjust confidence thresholds and escalation rules from field feedback so routine decisions automate and complex ones stay under human review — measuring on-time rate and hours saved.
WEEK 6+
Replicate across the network
Once ROI is proven, we replicate the pilot to other regions, warehouses, and fleets, and establish a sustainable cadence of model monitoring and iteration.
Weeks
to a live regional pilot, not months
TMS/WMS
native API integration, no rip-and-replace
VPC
deployed in your environment, data stays in
FAQ
Will AI-planned routes hold up against real-world disruptions like traffic and last-minute changes?
Yes — that's the whole point of the design. The system ingests live traffic and order-change signals and recalculates routes automatically when conditions shift, instead of running one static plan at dawn. Anything it can't safely resolve on its own escalates to a dispatcher as an exception ticket, so AI never improvises on high-risk decisions.
The last mile is so variable — can AI actually help there?
We don't chase 100% automation on the last mile; we automate the predictable part and put a workflow around the exceptions. High-frequency issues like incomplete addresses, recipient-not-home, and reschedules are detected automatically with a recommended action, so dispatchers review instead of deciding from scratch. AI carries the routine, people handle real judgment calls, and overall throughput and on-time rate climb steadily.
We already run a TMS and WMS — do we have to replace them?
No. We connect to your existing TMS, WMS, and ERP via API and merge their signals into a unified view, with AI planning, matching, and managing exceptions on top — your core systems stay exactly as they are. That means a shorter rollout, lower risk, and no large-scale data migration.
How do you handle data security and compliance?
The system can deploy inside your own VPC, so operational and customer data never leave for a third party. Every AI recommendation and human decision keeps an auditable trail to satisfy internal controls, and we can align with frameworks like SOC 2 and GDPR as needed. Model access and permissions are governed by role.
Why weeks to results instead of months?
Because we're an FDE team and we run a regional-pilot strategy. Engineers embed on-site, focus on one region or lane, connect the most critical data sources, and ship the one or two most painful scenarios — proving ROI against real volume in weeks, then replicating. Tight scope pulls value forward instead of waiting for one big system to land all at once.
After go-live, do we need to staff a data-science team to maintain it?
You don't have to build a team from scratch. As part of delivery we stand up model monitoring and an iteration cadence and transfer the operational know-how to your ops and IT staff. You can take it in-house, or have us continue with managed operations and optimization — whatever fits your organization's capacity.
A new era of
AI-native products
Ship your first AI use case in weeks, not quarters.