7 Ways Startup Apps Define Moderation Categories (Harassment to PII)

Short answer: define moderation categories for a startup app as seven risk checks, but do not let a category decide the outcome by itself; combine harassment, sexual content, self-harm, violence, illegal activity, spam, and PII with severity, confidence, and the proposed CRM action.

The least complex useful design is a two-lane pipeline: synchronous checks for content that can make an automated CRM action dangerous, and deferred checks for quality issues that can wait. The recommendation is deliberately boring. Keep one typed policy object, one decision function, and one audit event. Don't build seven unrelated filters.

1. How should a startup app define moderation categories before CRM actions?

A taxonomy should produce an operational decision, not merely a label. For this marketplace, the input is a transcript or summary of a sales call and the output may create a task, update an account, or draft follow-up text. A category such as violence says what appeared in the content. It does not say whether the system should redact a phrase, prevent a write, ask for review, or allow the summary unchanged.

That distinction matters because the same words can lead to different actions. A seller saying, “Our game contains fantasy violence,” is ordinary product context. A caller making a credible threat toward an employee is not. If both records become violence: true, the taxonomy has thrown away the detail needed by the workflow.

Use four fields for every finding: category, severity, confidence, and evidence span. Then evaluate those findings against the destination action. A summary entering a restricted trust queue has a different exposure than a sentence being copied into a broadly visible account note. The content stays the same; the consequence changes.

This is the first quality rule: labels describe content, while policy decides actions.

Seven top-level categories are enough to keep the interface legible. Teams can add internal subtypes, but those should clarify a decision rather than mirror every phrase a classifier might recognize. Config bloat starts innocently. Soon a harmless policy change requires touching a prompt, an enum, a database migration, an analytics query, and three dashboards. No thanks.

2. Trace the transcript before naming the risk

The primary trade-off is quality versus latency. Running every possible check before every CRM write can improve recall, yet it also puts transcription, classification, redaction, and policy evaluation on the user's critical path. Running everything later feels fast, but a dangerous follow-up task may already exist by the time the deferred job objects.

Split the work by reversibility.

Self-harm, credible violence, and illegal facilitation belong on the synchronous lane when the proposed action could amplify harm. A blocked draft is reversible. A sent message is not. Sexual content and harassment may also require that lane when the system would expose explicit or targeted material to a wider audience. PII redaction usually belongs before persistence because copying a secret into the CRM and deleting it later still widened access.

Spam and lower-severity record-quality checks are better candidates for deferred processing. They can merge duplicates, suppress repetitive tasks, or flag a record without delaying the call workflow. Batch processing is a useful execution pattern for work that does not need an immediate response.

The line is not “safety categories are slow, everything else is fast.” It is whether the next action is reversible and how far the content will travel. That gives engineers a rule they can test.

Latency needs an explicit measurement point too. Record time from transcript availability to policy decision, and report percentiles per lane. Do not blend synchronous and deferred jobs into one average; it hides the exact queue users are waiting on. I benchmark the whole path because a quick classifier behind a backed-up queue is still a slow feature.

3. How do seven risk checks become four CRM decisions?

The category list is the vocabulary. The decision set should be smaller: allow, redact, review, or block. Small decision sets are easier to document, observe, and preserve when the classifier changes.

Here is how the seven checks earn their place:

1. Harassment covers targeted abuse, intimidation, and degrading attacks. Preserve enough evidence for a reviewer, but avoid copying abusive language into routine CRM notes.
2. Sexual content covers explicit sexual material and sexual solicitation. Context and age uncertainty should increase review priority rather than disappear into a generic adult-content flag.
3. Self-harm covers intent, encouragement, instructions, and credible concern expressed about another person. It should interrupt automated outreach when a summary indicates immediate risk.
4. Violence covers threats, intent, encouragement, and instructions for physical harm. Fictional or descriptive mentions need a lower severity than credible intent.
5. Illegal activity covers requests that facilitate wrongdoing. Do not treat every mention of crime as facilitation; a customer describing fraud they suffered is reporting an incident.
6. Spam covers repetitive, deceptive, or unsolicited promotional behavior. Its normal consequence is suppression or deduplication, not a safety escalation.
7. PII covers identifiers that the CRM does not need for the stated workflow. Redaction should be based on necessity and destination, not the assumption that every name is forbidden.

These are policy definitions, not universal truths. I'm not sure any fixed subtype list survives contact with every marketplace; seller onboarding, regulated goods, and support escalation create different evidence needs. A labeled sample of the application's own calls resolves that uncertainty better than adding speculative config.

Keep overlap. A single span can be both harassment and violence, while another can contain illegal facilitation plus PII. Forcing one winning class loses information and creates brittle priority rules. The policy layer can resolve multiple findings deterministically: block outranks review, review outranks redact-only, and redaction happens before any allowed write.

4. A typed boundary keeps policy portable

The implementation boundary should accept findings from any classifier and return an action that the CRM adapter understands. That keeps model-specific labels out of business logic and makes the policy unit-testable without a network call.

type Category =
  | "harassment"
  | "sexual"
  | "self_harm"
  | "violence"
  | "illegal"
  | "spam"
  | "pii";

type Severity = "low" | "medium" | "high";
type Action = "allow" | "redact" | "review" | "block";

type Finding = {
  category: Category;
  severity: Severity;
  confidence: number;
  start: number;
  end: number;
};

type ProposedWrite = {
  destination: "account_note" | "follow_up" | "trust_queue";
  sendsExternally: boolean;
};

type Decision = {
  action: Action;
  reasons: Category[];
  redact: Array<{ start: number; end: number }>;
};

const highImpact = new Set<Category>([
  "self_harm",
  "violence",
  "illegal",
]);

export function decide(
  findings: Finding[],
  write: ProposedWrite,
): Decision {
  const accepted = findings.filter((finding) => finding.confidence >= 0.8);
  const reasons = [...new Set(accepted.map((finding) => finding.category))];
  const redact = accepted
    .filter((finding) => finding.category === "pii")
    .map(({ start, end }) => ({ start, end }));

  const dangerousWrite = accepted.some(
    (finding) =>
      highImpact.has(finding.category) &&
      finding.severity === "high" &&
      write.sendsExternally,
  );

  if (dangerousWrite) return { action: "block", reasons, redact };

  const ambiguous = findings.some(
    (finding) => finding.confidence >= 0.5 && finding.confidence < 0.8,
  );
  if (ambiguous) return { action: "review", reasons, redact };
  if (redact.length > 0) return { action: "redact", reasons, redact };
  return { action: "allow", reasons, redact };
}

The 0.8 and 0.5 values are example policy inputs, not claimed universal thresholds. Tune them on labeled marketplace data and version them. More important, never interpret a confidence score as severity. A classifier can be highly confident that a low-risk fictional reference exists.

Test the action boundary with compact fixtures. Include overlaps, quoted language, negation, multilingual calls, transcript gaps, and destination changes. Speech recognition is its own error source; the open-source Whisper repository documents a speech-recognition system and provides a useful reference point for separating transcription from moderation. Store the transcription version beside the policy version so a quality shift can be traced to the correct stage.

An audit event needs the policy version, content hash, proposed destination, findings, decision, and timing. Avoid raw content in general logs. Evidence spans can live in a restricted store with retention rules, while ordinary telemetry carries identifiers and counts. This keeps debugging possible without turning observability into a second CRM.

5. Measure reversibility before choosing the runner-up

The two-lane automated design is not suitable when the marketplace lacks representative labeled calls, when reviewers cannot appeal decisions, or when a false allow could trigger an irreversible external action. In those cases, stick with a human-first queue: automation may prioritize records and redact obvious PII, but a reviewer approves the CRM action.

It is slower. That is the catch.

Human-first processing is also the better choice for a new locale or a newly regulated seller category. The team needs examples before it can defend thresholds. Start by recording reviewer decisions and disagreement, then promote only narrow, well-measured cases to automatic handling. Your mileage may vary because call length, language mix, and review staffing all change the latency curve.

The opposite boundary matters as well. A full moderation pass is excessive for fields that cannot trigger messaging, cannot be shared broadly, and contain only system-generated identifiers. Skip work that has no plausible effect. DX improves when the pipeline exposes one decision contract and a short reason list instead of making every caller understand seven classifiers and a pile of provider-specific settings.

Before deployment, define the rollback as a policy-version change, not an emergency code release. Shadow the new version, compare decisions on the same inputs, inspect category-level disagreement, and then move one reversible destination at a time. Quality is the rate of correct actions, not the rate of emitted labels. Latency is the time until the action is safe to take. Measure both at that boundary.

That's enough machinery.

Further reading

• https://platform.openai.com/docs/guides/batch
• https://github.com/openai/whisper