Dispatch optimization that increases revenue miles.
Recommend feasible load-driver assignments using hours, equipment, location, service, and business rules, then let dispatchers approve.
Nothing is finalized until a human approves it.
Where the time and money actually go.
Dispatchers juggle changing constraints in spreadsheets and tribal knowledge; suboptimal assignments create empty miles, service failures, and driver dissatisfaction.
Dispatchers, load planners, driver managers
Trigger to act: Fleet utilization is uneven, empty miles are material, dispatch turnover is high, or network growth has outpaced manual planning.
The result you can model before you sign.
Illustrative only: 1 million monthly loaded miles × 0.3 percentage-point reduction in empty-mile share × $1.85 variable cost/mile = $5,550 monthly opportunity. Validate through shadow-mode simulation.
The outcome, plainly: Recommend feasible load-driver assignments using hours, equipment, location, service, and business rules, then let dispatchers approve.
Inputs in. A cited, review-ready result out. Your expert decides.
A Optimization engine + approval copilot. Every material fact is grounded in an allowed source and returned with its identifier, with no invented data.
Deterministic optimization solver or constrained ranking model; the LLM explains options but does not own the objective; GPT-5.6 Terra or Claude Sonnet 4.6 for exception handling and natural-language interaction. OR-Tools or commercial solver when needed; Constraint test suite.
- Available loads
- driver location and HOS
- equipment
- appointments
- route time
- home-time commitments
AI-Native, not autonomous. Judgment stays with your people.
The machine does the work; the human’s role narrows to the one thing that matters, judgment. That constraint is what makes it safe to deploy.
Dispatcher approves every assignment; safety, HOS, labor, maintenance, and driver-preference constraints are hard rules, not optional prompt guidance.
A scorecard, not a demo. We baseline what breaks in production.
Every deployment ships with an evaluation suite. These are the numbers we baseline before launch and monitor after.
The category is crowded. Most of it isn’t built for your workflow.
Fixed-scope, tuned to your systems and rules, grounded in your data, with the human gate and audit trail built in from day one. A price you own, not a subscription you rent.
It plugs into the stack you already run.
No rip-and-replace. Access is scoped to the minimum data necessary, isolated per tenant, and fully logged.
Transparent by design. The build price buys the workflow and the proof.
A fixed implementation fee plus a monthly bill that scales with volume and governance. No hidden seats.
- ✓ One process / scope
- ✓ Live workflow on your data
- ✓ Baseline evaluation suite
- ✓ Measured vs. current process
- ✓ Full scope & integration
- ✓ Human-review UI & audit trail
- ✓ Write-back to your systems
- ✓ Production evals & monitoring
- ✓ Multi-facility rollout
- ✓ Advanced security & compliance
- ✓ Custom control & escalation
- ✓ Dedicated evaluation program
optimization jobs, market/external data, warehouse compute and approval workflow.
Planning assumptions, not vendor quotations. Your TMS and other platform licenses are separate and owned by you. Figures confirmed during scoping.
“His vast knowledge of technologies and a natural problem-solving mindset consistently lead us through complex challenges with clarity and confidence.”
Questions serious buyers ask.
Does the AI act on its own?
No. Dispatcher approves every assignment; safety, HOS, labor, maintenance, and driver-preference constraints are hard rules, not optional prompt guidance. The system drafts and recommends; a human approves every consequential action. Explicitly excluded: No autonomous dispatch; no HOS override; no unsafe route recommendation; no optimization objective that hides service or driver constraints..
How do you stop it inventing facts?
Every material claim is grounded in an allowed source record and returned with its source identifier. The system separates observed facts, model inference, and missing information, and routes to a human whenever confidence is low, evidence conflicts, or an adverse outcome is possible.
What does it cost to run each month?
A usage bill of roughly $1,150–9,100/month (optimization jobs, market/external data, warehouse compute and approval workflow), plus a $2,800/month managed retainer for evaluation, monitoring and maintenance. Your existing platform licenses are separate and already yours. Exact figures are confirmed during scoping.
Do we need a ChatGPT or Claude subscription?
No consumer ChatGPT or Claude subscription is required for the production workflow. The client needs an approved API/cloud billing account. Workspace seats are optional for internal prototyping and administrator access.
How is this different from McLeod?
Tools like McLeod, Trimble TMW, Samsara are broad platforms you adapt to. This is a fixed-scope implementation tuned to your systems and rules, grounded in your data, with the human gate and audit trail built in, and a transparent price instead of a seat subscription.
How long until it’s live, and how do we prove it works?
This is a specialist opportunity. We baseline “Empty miles” first, then measure against that baseline. You see the scorecard before expanding scope, the evaluation suite ships with the system, not as an afterthought.
Bring your real numbers. Leave with a fixed-scope plan.
A 30-minute engineering-led working session, no slideware. You leave with a sized opportunity estimate, a fixed-scope pilot plan, and the integration & human-review path mapped.
VP of Growth at ViitorCloud · senior delivery owner confirmed before paid work