Merchandising that grows margin-safe conversion.
Build and explain collection, ranking, and campaign recommendations using inventory, margin, demand, brand, and customer signals.
Nothing is finalized until a human approves it.
Where the time and money actually go.
Merchandisers manually curate many pages and campaigns; fast-moving inventory and trend signals make static rules stale while black-box ranking undermines control.
Merchandisers, content, search, inventory and marketing teams
Trigger to act: Site refresh workload is high, campaign performance is inconsistent, assortment is expanding, or merchandising teams need more test capacity.
The result you can model before you sign.
Illustrative only: $2 million monthly collection-page GMV × 0.15% incremental conversion × 42% contribution margin = $1,260 monthly contribution. Build a testing cadence, not a lift promise.
The outcome, plainly: Build and explain collection, ranking, and campaign recommendations using inventory, margin, demand, brand, and customer signals.
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, 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.
- Catalog attributes
- Inventory
- Margin
- Sales
- Search and browse behavior
- Campaign theme
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.
Merchandisers approve every collection, rank rule, exclusion, and campaign; sponsored placement and personalization rules remain transparent.
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 commerce platform 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. Merchandisers approve every collection, rank rule, exclusion, and campaign; sponsored placement and personalization rules remain transparent. The system drafts and recommends; a human approves every consequential action. Explicitly excluded: No undisclosed sponsored rank; no autonomous assortment or brand decision; no personalization without consent/legal basis; no guaranteed conversion gain..
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 $1,100 to $8,400 per month (optimization jobs, market/external data, warehouse compute and approval workflow), plus a $2,400 per 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 Nosto?
Tools like Nosto, Bloomreach, Constructor 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 is live, and how do we prove it works?
We baseline the “Revenue per session” figure 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