ETA prediction and exception management that catches the miss before it costs you.
Predict arrival risk, explain the drivers, and trigger the right exception playbook before a shipment misses its commitment.
Nothing is finalized until a human approves it.
Where the time and money actually go.
Static ETA fields lag reality; teams react after milestones are missed and overwhelm customers with noisy alerts rather than prioritized action.
Planners, customer service, expeditors, account teams
Trigger to act: On-time performance is deteriorating, customer escalations are rising, or a control tower has data but lacks actionable prioritization.
The result you can model before you sign.
Illustrative only: 20,000 shipments/month × 1% additional early-resolved exceptions × $45 average internal/service recovery value = $9,000 monthly opportunity. Define value with finance before launch.
The outcome, plainly: Predict arrival risk, explain the drivers, and trigger the right exception playbook before a shipment misses its commitment.
Inputs in. A cited, review-ready result out. Your expert decides.
A Predictive model + decision copilot. Every material fact is grounded in an allowed source and returned with its identifier, no invented data.
Warehouse-native statistical or ML model (for example XGBoost, LightGBM, Prophet or a task-specific model); GPT-5.4 mini, Gemini 2.5 Flash or Claude Haiku 4.5 for explanations and planner interaction. Feature pipeline and model registry; back-testing and drift monitoring.
- Live location
- route
- dwell
- carrier history
- appointment windows
- stop sequence
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.
Operations staff approve customer commitments, expedite spend, appointment changes, and high-impact interventions; predictions expose confidence intervals.
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
Monthly retraining, warehouse compute, feature pipelines and 5,000–50,000 scored entities.
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. Operations staff approve customer commitments, expedite spend, appointment changes, and high-impact interventions; predictions expose confidence intervals. The system drafts and recommends; a human approves every consequential action. Explicitly excluded: no promise of exact arrival time; no autonomous expedite spend; external data availability and GPS coverage are explicit dependencies.
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 $1,150–8,400/month (monthly retraining, warehouse compute, feature pipelines and 5,000–50,000 scored entities), 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 project44?
Tools like project44, FourKites, Shippeo 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 “ETA error” 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