More reach, without more headcount. Product Content Engine.
Generate fact-checked, channel-specific product copy and attributes from approved product data, with brand and compliance controls before publishing.
Nothing is finalized until a human approves it.
Where the time and money actually go.
Catalog teams rewrite the same facts for PDPs, marketplaces, ads, and regions; thin or inconsistent content delays launch and creates factual risk.
Merchandisers, copywriters, SEO, marketplace and localization teams
Trigger to act: SKU count or marketplace expansion is growing, product launches are late, SEO coverage is thin, or localization consumes disproportionate effort.
The result you can model before you sign.
Illustrative only: 5,000 SKUs × 18 content minutes removed × $42 loaded hourly cost ÷ 60 = $63,000 one-time catalog capacity. Conversion lift must be A/B tested, not promised.
The outcome, plainly: Generate fact-checked, channel-specific product copy and attributes from approved product data, with brand and compliance controls before publishing.
Inputs in. A cited, review-ready result out. Your expert decides.
A Generative content system + verifier. Every material fact is grounded in an allowed source and returned with its identifier, with no invented data.
Gemini 2.5 Flash, GPT-5.4 mini or an evaluated multimodal model for scalable generation and classification; Claude Sonnet 4.6 or GPT-5.6 Terra for high-value exceptions and quality review. Rules engine; brand/policy verifier; human review queue.
- Approved specifications
- Images
- Category taxonomy
- Brand voice
- Claims/legal restrictions
- Target channel
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.
Merchandising/brand/legal reviewers approve claims, comparative language, regulated categories, and final publication; source facts cannot be invented.
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
5,000 to 40,000 content assets per month with generation, fact checks and media/feed validation.
Planning assumptions, not vendor quotations. Your PIM 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. Merchandising/brand/legal reviewers approve claims, comparative language, regulated categories, and final publication; source facts cannot be invented. The system drafts and recommends; a human approves every consequential action. Explicitly excluded: no invented specifications, certifications, ingredients, efficacy, or compatibility; no auto-publish of regulated claims; no keyword stuffing or duplicate-content guarantee.
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 $350–3,100 per month (5,000 to 40,000 content assets per month with generation, fact checks and media/feed validation), plus a $1,600 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 Salsify?
Tools like Salsify, Akeneo, and Jasper 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 “Time to publish” 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