Demand forecasting for 3PL and distribution without the guesswork.
Produce SKU/lane/customer forecasts with uncertainty, driver explanations, and planner-approved scenarios for labor, space, and transport capacity.
Nothing is finalized until a human approves it.
Where the time and money actually go.
Forecasts are spreadsheet-heavy, customer signals arrive late, promotions and seasonality are inconsistently modeled, and one number hides uncertainty.
Planners, operations managers, labor and capacity teams
Trigger to act: Capacity surprises create overtime or service failures, customer forecasts are unreliable, or a network needs scenario planning before peak.
The result you can model before you sign.
Illustrative only: $250,000 monthly overtime and spot-capacity spend × 2% avoidable reduction = $5,000 monthly opportunity. Prove with historical back-testing and a holdout period.
The outcome, plainly: Produce SKU/lane/customer forecasts with uncertainty, driver explanations, and planner-approved scenarios for labor, space, and transport capacity.
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.
- Historical volume
- SKU/customer/lane hierarchy
- promotions
- seasonality
- lead times
- service constraints
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.
Planners approve the production forecast and capacity action; the model does not hide uncertainty or auto-commit labor/space/transport.
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 Data warehouse 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. Planners approve the production forecast and capacity action; the model does not hide uncertainty or auto-commit labor/space/transport. The system drafts and recommends; a human approves every consequential action. Explicitly excluded: No autonomous procurement or labor commitment; no forecast guarantee; external shocks and sparse new-item data are explicit limitations..
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 Blue Yonder?
Tools like Blue Yonder, Kinaxis, o9 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 “WAPE/MAPE by segment” 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