Software no longer waits to be operated.
It reasons, acts, and learns.
Locus is built for this era. Eight specialized AI agents coordinate all-mile, all-channel orchestration in real time across every domain of your delivery network. Your team defines policies, sets guardrails, and governs outcomes. The system handles the rest.
Recognized by leading industry analysts and customers
TRUSTED BY
360+ Enterprises
to run complex transportation networks
What Locus does that first-gen routing tools cannot.
Legacy platforms can plot a route on a map. Locus reasons, acts, and learns across every delivery decision in real time, across every domain of your network.
The Capacity agent predicts volume spikes before they cost you a missed SLA, matching demand to fleet across captive, contracted, and outsourced providers before gaps form.
As your network adds depots, drivers, and delivery zones, the operating model scales with you. The Orchestrator ensures every agent adapts automatically when one sees a constraint.
Route intelligence tied to the driver, not the truck. The Dispatch agent builds routes reflecting the capability of the person executing them, across delivery drivers, installation specialists, and field service technicians.
Unexpected conditions and constraints are automatically evaluated and the optimal decision is executed to keep the network moving. Live carrier APIs, traffic, weather, and regulatory signals feed every agent's reasoning in real time.
Four eras. One ceiling each.
Then the model broke.
Logistics software has evolved through four distinct eras. Each solved real pain. Only the fourth closes the gap between the complexity of modern delivery and the intelligence required to run it.
On-Premise
Fixed routing rules. Manual dispatch. Systems installed on-site, updated rarely. Planners own every decision — the software just stores data.
SaaS
Cloud-based. Better maps, better UI. Faster tools — planners still manually adjust every route. Every insight needs a human who knows where to click.
SaaS + AI Bolt-On
Agents layered onto legacy platforms. Real intelligence — but constrained by the data models underneath. The architecture was never designed for it.
Agentic AI Native
Agents orchestrate planning, routing, execution, and payment against live signals. Humans define policy. The system handles the rest.
What breaks at enterprise scale.
Four forces that grind against yesterday's architecture every peak, every reverse logistics cycle, every carrier renegotiation.
Quadrillion-combination routing
A single DC with 40 trucks and 800 drops has more route permutations than atoms in your building. Planners pick "good enough" — and leave margin on every run.
Exception load during peak
Volume triples. Exceptions don't scale linearly — they compound. The team that absorbed Tuesday is drowning by Black Friday.
Institutional knowledge walking out
The planner who "knows the routes" is retiring. The playbook is in their head. Bolt-on AI can't ingest what was never written down.
Store–DC–DTC network fragmentation
Store-fulfilled orders, DC trunk lines, and DTC carriers each run on their own tools. Every seam is a point of failure and a renegotiation.
Five structural differences
— not five features.
When AI is native to the operating model, the architecture looks different from the ground up. Each of these is a property of how the system is built, not a module you can buy.
Agents act, not just advise.
Every routing, scheduling, and execution decision flows through AI agents — evaluated in real time against live operational signals, not surfaced to a dashboard for a human to action later.
Every delivery makes the model smarter.
Driver behavior, route performance, carrier SLAs — all feed the model automatically. The longer you run, the wider the gap.
Shared context, no hand-offs.
A constraint in Dispatch automatically updates Carrier priorities and pre-alerts the Customer agent. No silos. No manual relays.
Every action, explainable.
Every decision carries a natural-language reason and a full audit trail. Simulation, shadow mode, and instant rollback built in.
World signals, woven in.
Weather, traffic, freight lane rates, tariffs, demographics, regulatory signals — native context for every agent's reasoning, not a separate product.
Human talent elevated, not replaced.
Your planner's role shifts from execution to judgment — setting policies, establishing guardrails, and governing outcomes. The agents absorb the combinatorial work they were never going to win.
Eight agents. One decision surface.
Policy flows down from your team. Intelligence surfaces up from the agents. At every level, humans define the rules, review the exceptions, and govern outcomes. Hover any agent to see what it does in your operation.
Orchestrator
Capacity
Carrier
Dispatch
Hub
Customer
Settlement
Copilot
Agents earn autonomy.
Trust is built in stages, not assumed.
Every agent starts by recommending — your team decides. As confidence builds, agents graduate. Your policies govern where agents act autonomously and where a human reviews — per agent, per domain, per threshold.
Advise
Agent recommends. Your team decides. Every action logged with full reasoning. Every agent starts here — no exceptions.
Guardrails
Agent auto-acts within configured thresholds. Escalates outside them. Teams review exceptions, not every decision.
Autonomous
Agent orchestrates routine decisions using ML trained on your decision-outcome history. Your team governs network outcomes.
Configurable per agent, per domain, per threshold. Simulation, shadow mode, and instant rollback are built in — not bolted on. You are never committing to more automation than you've approved.
How to measure the opportunity
in your operation.
Before you can quantify the opportunity, you need a clear picture of your operation as it runs today. With our experience powering 1.5B+ deliveries and service appointments, we can offer you one.
What we'll look at
- 1 Decisions your team makes that agents should own
How many routing and exception decisions your team makes each day that an agent could handle.
- 2 How many systems your operation touches
ERP, OMS, WMS, fleet, carrier APIs, customer promise, settlement — the more fragmented today, the greater the opportunity.
- 3 How your operation responds to the unexpected
When a constraint hits, how long does it take to resolve? Agents surface options in seconds with full context.
- 4 What your network knows today vs. could know
After 12 months of every delivery feeding the model automatically, what becomes decidable that isn't today?
- 5 How much growth your existing team can absorb
When agents handle routine decisions, supply-chain talent shifts from execution to strategy.
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Typical turnaround: a dedicated solutions engineer will reach out within one business day.