UGC moderation and brand safety without the review backlog.
Classify reviews, images, Q&A, and community content against explicit policy, prioritize risk, and give moderators evidence for consistent decisions.
Nothing is finalized until a human approves it.
Where the time and money actually go.
Volume overwhelms manual moderation, policy is applied inconsistently, and false removals damage trust while harmful content remains visible.
Moderators, marketplace sellers, community and legal teams
Trigger to act: UGC volume is scaling, a marketplace expands categories/regions, legal or brand-safety incidents occur, or moderation backlogs exceed SLA.
The result you can model before you sign.
Illustrative only: 500,000 UGC items/month × 8% requiring review × 20 seconds removed × $30 loaded hourly cost ÷ 3,600 = $6,667 monthly capacity. Safety quality remains the primary gate.
The outcome, plainly: Classify reviews, images, Q&A, and community content against explicit policy, prioritize risk, and give moderators evidence for consistent decisions.
Inputs in. A cited, review-ready result out. Your expert decides.
A Multimodal moderation agent. Every material fact is grounded in an allowed source and returned with its identifier, no invented data.
Gemini 2.5 Flash, GPT-5.4 mini or an evaluated multimodal model for scalable generation/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.
- Text/image/video metadata
- User/account context
- Product/category
- Policy version
- Prior actions
- Reports/appeals
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.
Humans decide ambiguous removals, account sanctions, legal referrals, and appeals; high-impact classes require double review.
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
50,000 to 500,000 text/image moderation events per month, plus review queue.
Planning assumptions, not vendor quotations. Your UGC 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. Humans decide ambiguous removals, account sanctions, legal referrals, and appeals; high-impact classes require double review. The system drafts and recommends; a human approves every consequential action. Explicitly excluded: No opaque account ban; no political or protected-class profiling; no removal without appeal policy; no guarantee of catching all harmful content..
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 $850–7,800 per month (50,000 to 500,000 text/image moderation events per month plus review queue), plus a $2,200 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 Hive?
Tools like Hive, ActiveFence, and Spectrum Labs 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?
This is a specialist opportunity. We baseline harmful-content recall 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