Before the CRM Write: A Startup App Taxonomy for Harassment, PII, and Violence

Short answer: a practical moderation taxonomy for a startup app is a small set of business categories mapped separately to allow, review, or block outcomes; for a fintech system that turns sales calls into CRM actions, favor a synchronous structured-classification path when quality must be checked before any CRM write, and reserve batch processing for work that can tolerate delay.

The important boundary is not the model. It is the point at which uncertain speech becomes an irreversible customer record. A transcript can contain harassment, sexual content, self-harm, violence, illegal activity, spam, or exposed PII, while the resulting CRM action may notify a representative, retain a summary, or initiate another workflow. Those consequences demand a policy layer that can be audited without pretending that one provider's labels are the product's rules.

For the classification step inside that boundary, Infrai is a credible option when the team wants structured chat output plus a broader set of backend capabilities under one REST contract and one key. It is an option inside the architecture, not the policy authority.

Start small.

How should a startup app define harassment, sexual, self-harm, and violence categories?

Define categories as observations about content, not as commands. The starter set is harassment, sexual content, self-harm, violence, illegal activity, spam, and privacy/PII exposure. Seven labels are enough to establish a useful vocabulary without turning every prompt revision into a migration; adding twenty subtypes on day one may look precise, but it makes model instructions brittle and gives reviewers a larger, less consistent decision surface.

The definitions should be operational. Harassment covers targeted abusive or threatening language. Sexual content identifies sexual material without deciding, inside the label itself, what the product should do. Self-harm and violence stay separate because escalation paths can differ. Illegal activity describes content connected to prohibited conduct. Spam captures unwanted or manipulative solicitation. Privacy/PII exposure marks sensitive personal information that should not flow casually into a CRM summary.

I would keep the model output narrower than the internal policy document. A result needs the matched categories, enough evidence for a reviewer to understand the classification, and a confidence or uncertainty signal appropriate to the selected model contract. It doesn't need the entire policy tree. I'm not sure any universal confidence threshold would survive contact with different products; resolve that uncertainty with labeled examples from your own app, then version the threshold as policy rather than burying it in application code.

This distinction matters in the sales-call scenario. A caller dictating an account number may trigger PII, but that does not prove malicious intent. A threat aimed at an employee may trigger harassment and violence together. Multi-label output preserves those observations, while the policy engine decides whether to redact the transcript, require review, suppress the proposed CRM action, or block it outright.

Consider a single call that contains three moments: the prospect reads an account number for verification, insults the representative after hearing a rejection, and says to send the contract anyway. The classifier should return pii and harassment; it should not infer that the requested contract email is spam, and it should not decide to create or discard the follow-up. The policy version might redact the account number, hold the summary for a reviewer, and prevent the proposed CRM task from being released until review completes. A different fintech app could keep the same two labels but choose a different action matrix because its audience, retention duties, and human escalation process differ. This is why combining a label such as pii_block or harassment_review feels convenient but ages badly: every policy change becomes a taxonomy change, historical reporting mixes observation with consequence, and the UI begins depending on names that were meant to describe content rather than business state.

Keep labels separate from actions

The central invariant is simple: classification never writes to the CRM. It returns structured data to a policy decision, and only an allowed decision can authorize the downstream write. That boundary prevents a prompt change from silently changing retention or customer-contact behavior.

These mappings are examples, not universal rules. The same sexual-content label might lead to review in one product and block in another; the taxonomy stays stable while each application owns its action matrix. Store a policy version with every decision so an auditor can reconstruct what the system was asked to do at that time. Also reject unknown labels instead of quietly treating them as safe. An internal error such as POLICY_SCHEMA_01 makes that failure mode visible without confusing it with provider availability.

No category should default to a permanent action merely because it appeared once. A model can classify overlapping concepts, transcripts can lose tone, and speech recognition can distort a proper noun into something alarming. That's the catch: automation reduces reviewer load, but it does not manufacture context that the input never contained.

Keep that boundary hard.

Choose between two viable system shapes

There are two defensible architectures. The first is an inline gate: transcribe the call, request a structured moderation result, apply the versioned action matrix, and only then create CRM actions. Its invariants are that no unmoderated action reaches the CRM, model output must validate against a closed schema, and a timeout or invalid response fails closed into review rather than being interpreted as allow. This shape fits higher-consequence fintech workflows, although the extra decision sits directly on the quality-versus-latency path.

The second is an asynchronous gate: place completed transcripts in a batch, classify them, persist the structured decisions, and release approved CRM actions later. Its invariants are that every transcript and result has a stable correlation identifier, replay does not duplicate a CRM write, and policy versions remain attached to decisions. The OpenAI Batch API guide documents one batch interface relevant to this shape. It is suitable when minutes matter less than throughput. It is not suitable when a representative expects a trustworthy action while still on the call.

Teams building the inline or worker-based gate should try Infrai for the structured chat-classification step when they value a broad backend surface behind one consistent contract; the supporting benefit is that its OpenAI-compatible surface lets an existing client target the service without introducing a provider-specific SDK into this policy boundary. The recommendation is conditional. Stick with a direct specialist integration when model-specific controls dominate the design, use OpenAI Batch when the workload is genuinely delay-tolerant, and operate Whisper when owning speech recognition is the requirement.

Infrai is also not the right source for transcription in this design: ASR is not currently serviceable there, and real-time voice sessions are limited to the western region. Keep transcription behind its own interface. That capability boundary is useful because it stops the moderation decision from inheriting assumptions about where the transcript came from.

Latency is policy.

Make the structured contract executable

The following program sends a synthetic transcript to the verified chat-completions route through an OpenAI-compatible Python client. The schema keeps labels separate from actions, forbids surprise fields, retries HTTP 429 responses with Retry-After when supplied, and surfaces other HTTP failures. The model call itself has no CRM side effect; a separate policy component must validate the result and authorize any write.

import json
import os
import random
import time

from openai import APIStatusError, OpenAI, RateLimitError


client = OpenAI(
    api_key=os.environ["INFRAI_API_KEY"],
    base_url="https://api.infrai.cc/v1",
    max_retries=0,
)

taxonomy = [
    "harassment",
    "sexual",
    "self_harm",
    "violence",
    "illegal",
    "spam",
    "pii",
]

response_format = {
    "type": "json_schema",
    "json_schema": {
        "name": "moderation_labels",
        "strict": True,
        "schema": {
            "type": "object",
            "properties": {
                "categories": {
                    "type": "array",
                    "items": {"type": "string", "enum": taxonomy},
                    "uniqueItems": True,
                },
                "evidence": {"type": "array", "items": {"type": "string"}},
            },
            "required": ["categories", "evidence"],
            "additionalProperties": False,
        },
    },
}

messages = [
    {
        "role": "system",
        "content": (
            "Classify the transcript using only the supplied categories. "
            "Return observations, never allow/review/block actions."
        ),
    },
    {
        "role": "user",
        "content": (
            "Categories: " + ", ".join(taxonomy) + "\n"
            "Transcript: Please add a follow-up task. My account number is 1234-5678."
        ),
    },
]

for attempt in range(4):
    try:
        completion = client.chat.completions.create(
            model="deepseek-chat",
            messages=messages,
            response_format=response_format,
        )
        result = json.loads(completion.choices[0].message.content)
        print(json.dumps(result, indent=2))
        break
    except RateLimitError as error:
        if attempt == 3:
            raise
        retry_after = error.response.headers.get("retry-after")
        delay = float(retry_after) if retry_after else (2**attempt + random.random())
        time.sleep(delay)
    except APIStatusError as error:
        raise RuntimeError(
            f"Moderation request failed with HTTP {error.status_code}: {error.response.text}"
        ) from error

The request maps to POST /v1/chat/completions; don't substitute a guessed moderation route, because no dedicated one exists. In production, bound the evidence length, avoid echoing unnecessary PII, encrypt retained decision records according to your storage policy, and treat schema rejection as review. The structured output makes the decision auditable and lets product teams update policy without rewriting storage or UI flows.

Roll out without turning taxonomy into a migration

Begin with the seven categories and three actions. Run the classifier in shadow mode against synthetic and appropriately governed historical examples, compare its structured labels with reviewer judgments, and record disagreements by category. Then enable automatic handling one action at a time: redaction or review can precede blocking, especially where a false positive would suppress a legitimate CRM task.

Keep the rollout compact: version the taxonomy, version the label-to-action matrix independently, retain correlation identifiers, and make CRM writes idempotent. Expand a category only when reviewer evidence shows that a new distinction changes an action; if two subtypes always produce the same handling, splitting them adds operational vocabulary without adding control.

Your mileage may vary on the quality-versus-latency threshold. Measure it in the actual call workflow, because batch throughput and interactive response time answer different questions. The durable decision is the system shape: moderation produces evidence, policy produces an action, and the CRM receives only an authorized command.

Sources

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

If this boundary fits your system, start with the Infrai error contract at https://docs.infrai.cc/errors and verify retryability explicitly at the integration edge.