Poyo helps small teams add free AI checks to user-generated content moderation. You can assess posts, comments, reviews, and static images with Omni Moderation, then connect the category results to the rules and review tools your team already uses.
The practical benefit is a repeatable first assessment: submit supported content, track the task, and retrieve the result or receive a completion callback. Your team can use those signals to route routine submissions and focus human attention on cases that need context.
Start with one content surface and a few representative examples in the Playground. This guide shows how to connect free checks with publishing rules, review ownership, and user appeals so the process remains manageable as submissions grow.
Identify the content and decisions you actually own
User-generated content, or UGC, includes material people submit to your product: community posts, comments, profile photos, product reviews, listing descriptions, and uploaded images.
Make an inventory before choosing a tool. Record where each type appears, who can see it, whether users can edit it, and how quickly it needs a decision. A profile image shown publicly has different consequences from a draft visible only to its author.
Choose a surface connected to your existing publishing tools, such as comments in your application or product photos in your store. Poyo's assessment can feed those tools through task results and callbacks. If you also manage content on an external social platform, use that platform's authorized integration to carry out publishing or removal actions.
The Trust & Safety Professional Association describes moderation as policy enforcement supported by operations, product, and engineering. In a small company, one person may cover several of those functions. The responsibilities still need to be explicit.
Start with rules people can apply
A useful rule names the behavior, explains its scope, and states the available action. “Keep the community safe” expresses an intention but does not resolve a difficult submission.
For example, a product could write: “Direct threats against another member are prohibited. Hold suspected threats for review, and use the escalation process when the case requires urgent attention.” That gives reviewers a specific category and a next action.
Keep your rules separate from model category names. A community can prohibit repetitive advertising even though a harmful-content model returns no relevant flag. A news community can allow appropriate discussion of violence while still prohibiting threats against members.
| Policy area | What the team should define |
|---|---|
| Threats and harassment | Which behavior violates the rules and how it is escalated |
| Sexual or graphic content | What is allowed on each surface and what requires review |
| Spam and advertising | Limits on repetition, links, or promotion |
| Personal information | What may be shared and how exposure is handled |
| Off-topic or low-quality posts | Whether to move, edit, decline, or request changes |
| Disputed decisions | Who reviews appeals and what can be corrected |
Combine Omni Moderation's supported harmful-content categories with the checks your product already uses. Spam, personal information, post quality, and appeals need dedicated rules, tools, or reviewers. Assigning each requirement to a specific step makes the overall process easier to operate.
Test the wording with a few examples. If two reviewers apply the same rule differently, clarify the rule before expecting automation to make the decision consistent.
Choose when moderation happens
Timing affects both exposure and user experience. Define it for each content surface.
Before publication. Content remains pending until required checks and decisions complete. This can suit public image uploads or submissions that your team has chosen to inspect first. It also means users need a clear explanation of the wait.
After publication. Content appears and is assessed afterward. This reduces initial friction, but prohibited material can be visible until action is taken. The team needs a response process that matches that exposure.
After a report. Users flag a problem and the team reviews it. Reports help surface context and missed cases, but content that nobody reports may remain unexamined.
A mixed process can be practical. For example, a team could hold new profile photos before publication, run routine text checks on comments, and continue to accept reports on both. This is a design example, not a universal policy.
For an overview of these approaches, see Stream's content moderation guide. Choose based on your audience, exposure, and available review capacity.
Add free AI checks to your moderation workflow
Poyo brings multilingual text, a single HTTPS image, or text with an image into one task flow. Text supports 13 risk categories; images support six covering sexual content, violence, and self-harm. Category flags and scores give your application signals for deciding which submissions can follow routine rules and which need a closer look.
Use the category overview to map each check to its supported input. Text-only categories do not assess image pixels. If your policy depends on embedded text, audio, video, or specialized detection, add an appropriate separate processing step. For image coverage and child-safety handling, see the image moderation guide.
A practical sequence is:
- Receive a submission and record its content ID and version.
- Validate the text or uploaded file for your application.
- Submit the supported text or image to Poyo and save the task ID.
- Retrieve the completed result or receive the completion callback.
- Apply your rules to publish, request changes, reject, or assign a reviewer.
- Record the decision and retain routes for reports and appeals.
Poyo's task ID helps associate an assessment with the right submission. Completion callbacks can trigger the next step in your existing application or review tool. Configure how each supported signal maps to a business action, with human review for cases that need context or escalation.
Design a review queue a small team can manage
For a pilot, your existing support or admin tool may be sufficient. Each review item should contain enough information for a decision without forcing the reviewer to reconstruct the entire history.
Include the content or a controlled way to inspect it, relevant context, the rule being considered, the model signals, the content version, and any prior decision needed for this case. Add an owner and a status so two people do not unknowingly work on the same item.
Useful statuses include pending assessment, pending human review, approved, rejected, and appealed. Track technical failure separately, because “could not retrieve the image” is not a content-policy finding.
Set priorities your team can actually maintain. A potentially serious threat may need faster escalation than an uncertain product-photo decision. Define the responsible person and backup, including what happens when neither is available.
If the queue grows, examine the cause before loosening the rules. There may be too many false flags, unclear policies, duplicate tasks, or a new content pattern the team has not evaluated. Each problem needs a different response.
Keep context and appeals in the process
An automated signal may miss why a user posted something. Quoted harassment in a complaint, a discussion of a historical event, and an educational image can require context that the first assessment does not fully capture.
When a reviewer makes a decision, record the applied rule and reason. User-facing language should explain what happens next without needlessly reproducing harmful material.
For example, “Your image is awaiting review because it may fall under our graphic-content rule” describes a pending case. “We couldn't complete the image check; your upload remains private while we retry” describes a technical delay. These messages give users different, useful expectations.
Offer an appeal route for decisions that can be challenged. The reviewer should see the original content version, relevant context, the previous reason, and any new explanation from the user. Where staffing allows, use another person for the appeal.
If the decision changes, update the content's actual visibility and any related account action. Sending an apology without restoring the intended state leaves the underlying error unresolved.
Avoid treating criticism as a policy violation merely because it is negative. For review-based products, Bazaarvoice's UGC moderation discussion highlights the importance of retaining authentic negative feedback. Your rules should distinguish a critical customer experience from abuse or prohibited disclosures.
Measure decision quality and workload
Start with measures that help the team find a specific problem. Submission volume, completed assessments, processing failures, and pending reviews describe the workload. Review turnaround and appeal outcomes describe what users experience.
To evaluate classification and routing, label a permitted sample with the decisions your policy expects. Include both content flagged by the workflow and content it allowed through; looking only at the review queue cannot reveal everything the system missed.
The following example is entirely illustrative and does not describe Poyo or model benchmark performance:
| In a sample of 100 submissions | Sent for review | Not sent for review | Total |
|---|---|---|---|
| Human-labeled policy violations | 18 | 2 | 20 |
| Human-labeled acceptable content | 8 | 72 | 80 |
| Total | 26 | 74 | 100 |
Three different questions follow from this sample:
- How much of the queue was relevant? Of 26 flagged items, 18 were labeled violations: 18 ÷ 26, about 69.2%.
- How many violations were caught? Of 20 labeled violations, 18 were flagged: 18 ÷ 20, or 90%.
- How often was acceptable content flagged? Of 80 acceptable items, eight were flagged: 8 ÷ 80, or 10%.
These measures have different denominators and should not be combined into a single claim of “accuracy.” Review the cases behind them. The team may need a clearer rule, a different threshold, a separate check, or more context.
For live operations, also count unresolved appeals and look at the age of the oldest waiting item. A high completion count can hide a small set of cases that have been waiting far too long.
Connect Poyo to your existing review tools
Use the Omni Moderation Playground to test the languages and image styles your users submit. Sign in, run representative examples, and inspect the category results without writing code or manually creating an API key.
For application requests, use your Poyo API key with POST /api/generate/submit, model: "omni-moderation-latest", and the supported text or image fields. The same Poyo credentials can access other supported models, helping you keep generation and moderation within a shared platform integration.
Save the returned task ID with the content version. Query its status, or configure an optional callback_url so a completed assessment can update a record in your publishing system, support tool, or review queue. Your application defines the routing and carries out the publishing or reviewer-assignment action.
Read data.output.results from status responses and output.results from callbacks, which omit the outer wrapper. A finished task contains a completed assessment; flagged: true identifies potential content risk. Keep failed tasks on a separate technical-retry path.
Reassess edited posts and replaced images as new versions. Make callback handling safe to repeat so duplicate deliveries update the same review record, and ensure an older result cannot override a newer decision.
Set access permissions and retention rules for review records, and check applicable service terms before sending production data. The Poyo reference explains the request contract; the webhook guidance covers callback integration.
Frequently asked questions
What is user-generated content moderation?
User-generated content moderation is the process of reviewing material submitted by users, such as posts, comments, reviews, and photos, against a platform's rules. It includes detecting potential violations, deciding how to handle them, and resolving reports or appeals.
How does Poyo help a small moderation team?
Poyo provides a free first assessment of supported text and images, with category signals, task tracking, and optional completion callbacks. Your team can use those results to route submissions through existing tools and focus reviewers on cases that need context. Begin with one content surface and rules your team can apply consistently.
Which moderation steps can we automate?
Your application can submit content, save task IDs, retrieve assessments, handle callbacks, and update review records. After testing rules on your own examples, you can also automate defined publishing or routing actions. Poyo supplies assessment results and task handling; your team configures the business rules, escalation, and appeals.
How can we reduce repetitive manual review?
Keep one record per content version, associate it with the Poyo task ID, and make callback processing safe to repeat. This helps your application avoid duplicate review entries. Use labeled examples to tune category routing and examine why acceptable submissions enter the queue, while continuing to check missed violations. Measure the effect before expanding automation.
Can Poyo connect to our existing review tools?
Use status queries or completion callbacks to send classifications to your application's publishing system, support tool, or review queue. Include the content ID, version, category signals, and rule to apply in the record. Your integration handles reviewer assignment and publishing actions, so the team can continue using its established tools.
How do we start for free?
Choose one surface, define the relevant rules, and test representative text or images in the Poyo Playground. No code or manually created API key is needed for this trial. Omni Moderation calls cost zero credits; when the routing is ready, connect the API to your existing workflow and plan storage and reviewer time separately.
Use the AI content moderation introduction to interpret the signals and the cost guide to plan the work around free calls.