Migrating high-volume moderation from prompted LLM classification
See how a multilingual social platform could use Sentiqra to move from prompted generative classification to structured, reproducible moderation signals.
Illustrative workload: 8M analyses/month · 12+ languages · real-time moderation
Reference scenario: This is an illustrative architecture, not a named customer case study. Workload assumptions, cost calculations, and modeled outcomes are provided to demonstrate how the architecture can be evaluated. Actual results will depend on your traffic, content distribution, languages, thresholds, and existing stack.
The scenario
A consumer social platform needs to analyze multilingual user content in near real time. Its existing moderation pipeline uses a prompted generative model for first-pass analysis before applying platform policy.
The team wants to reduce analysis cost while making disputed analyses easier to replay and investigate.
Before / after architecture
Before: prompted LLM classification
The system can preserve the original analysis for audit. But independently replaying the same analysis later depends on the provider's model behavior, version availability, prompts, parameters, and surrounding application configuration.
After: versioned Sentiqra analysis
Stored for replay:
- · returned indicators
- · confidence values
- · language
- · pinned detection configuration
- · classification fingerprint
- · downstream policy action
For the same normalized input evaluated against the same supported pinned detection configuration, Sentiqra returns the same canonical analysis result.
Verify reproducibilityPolicy stays with the platform
Sentiqra supplies structured signals. The platform decides what happens next.
High-confidence, low-impact
Customer-defined automation
Ambiguous cases
Human review
High-impact decisions
Additional review or evidence
Thresholds should be validated by indicator and policy against the platform's own labeled data. Illustrative thresholds only - validate against your own labeled data before deployment.
Illustrative economics
Both systems are calculated from explicit assumptions. Do not treat these as guaranteed savings - model them against your own workload.
Existing analyzer (modeled)
- 8M calls/month
- × average input tokens
- × average output tokens
- × published provider pricing
- + retries / orchestration where applicable
= Modeled annual analysis cost
Human-review costs excluded unless explicitly included in both scenarios.
Sentiqra (modeled)
- 8M messages/month
- × 1.15 analyzers/message (conditional routing)
- = 9.2M analyzer runs
- × current Sentiqra pricing
= Modeled annual analysis cost
HTTP requests × average selected analyzers = analyzer runs. Conditional routing controls cost - not every message needs every analyzer.
Open cost calculatorPricing assumptions should be dated and linked to current public rates. See current pricing.
Failure behavior
Enterprise architects care about what happens when analysis is unavailable almost as much as accuracy. Define these states explicitly.
Sentiqra available
Normal routing - signals flow to your policy engine.
One analyzer fails
Partial signal set - retry the failed analyzer or route to review.
Sentiqra unavailable
Customer fallback policy - your system decides whether to allow, queue, or hold.
Quota / spend cap reached
Explicit operational state - not a silent failure. Your policy decides the fallback.
Queue backlog
Degrade async enrichment before the critical path - synchronous safety decisions take priority.
Illustrative operational impact
Reproducible appeals
A disputed analysis can be evaluated again using the same supported pinned detection configuration. This provides replayable evidence, rather than relying only on a stored historical output.
Structured policy mapping
The Trust & Safety taxonomy provides documented indicators that can be mapped to customer-owned moderation rules. No generated label needs to become policy automatically.
Multilingual coverage
The same integration can be used across supported Trust & Safety languages - all supported languages are enabled on every plan. Review analyzer language coverage before deployment.
Real-time analysis
Sentiqra is designed for real-time API workflows. Report actual latency as a percentile under documented benchmark conditions - for example, p95: 480 ms in the benchmark environment - rather than promising every response under a fixed threshold.
What to measure in your evaluation
Before replacing an existing analyzer, run both systems against the same labeled production sample. Compare:
The right migration decision should come from your workload - not a generic savings percentage.
Comparison
Sentiqra vs moderation APIs and LLMs
T&S Use Cases
Social, gaming, marketplace, education
Benchmark Results
Per-language Top-3 Hit Rate
Pricing
From Free to Enterprise
Playground
Test detection live - no API key
Quickstart Guide
First API call in under 5 minutes
Benchmark Methodology
How we measure Top-3 Hit Rate
Verify reproducibility
Replay against the same configuration
Build the reference architecture
Start with 5,000 free analyzer runs and evaluate Sentiqra against your current moderation pipeline.
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