DEPLOYMENTS
How teams deploy Sentiqra.
Deployment profiles across social platforms, gaming, marketplaces, fintech, and security operations. Every profile carries an evidence grade; every claim on this page traces to a maintained evidence record.
Deployments
The four profiles below are hypothetical examples - they show how teams typically structure a Sentiqra integration, and their numbers are labeled estimates, not measurements. We publish measured customer evidence only when a customer approves it; no card on this page currently claims a measured customer outcome. The fixed grade vocabulary is: Sentiqra-instrumented - measured by our own instrumentation; Customer-instrumented - measured in the customer's own systems; Customer-reported - stated by the customer with an approval on file; Illustrative - an estimate with a stated basis; Hypothetical example - not a real engagement.
Hypothetical profile - not a customer engagement. Numbers below are labeled estimates.
The scenario. A marketplace whose sellers dispute item flags faster than a small appeals team can investigate. In this scenario each dispute takes roughly 45 minutes because the previous moderation stack produced a free-text rationale that changed between calls.
The deployment pattern. Trust & Safety analyzer in the first-pass moderation pipeline. Every flag carries a configuration ID and signed receipt; the appeals reviewer replays the exact analysis and confirms or overturns.
- ·Median appeal investigation time ~45 min → ~7 min (illustrative estimate)
- ·Median time-to-resolution ~72h → ~4h (illustrative estimate)
- ·Appeals volume observed ~22% lower in the example window (illustrative estimate)
Evidence receipt
- Period
- n/a - hypothetical example
- Sample
- n/a - hypothetical example
- Metrics
- The figures below illustrate the shape of a typical outcome, not a measurement
- Methodology
- Hypothetical before/after - no engagement, no measurement window
- Verification
- Hypothetical example - no customer source or approval on file. Evidence owner: product marketing.
- Reference
- Hypothetical example - there is no customer to reference.
Hypothetical profile - not a customer engagement. Numbers below are labeled estimates.
The scenario. A gaming platform running three stitched-together classifiers with uneven language coverage - the same harassment pattern is flagged in English while being missed in Polish and Turkish. In this scenario the cross-language double standard has become a community-management issue.
The deployment pattern. A single Trust & Safety configuration across all eight supported languages. The same analysis behavior applies everywhere, and the pinned configuration ID answers why an action was taken.
- ·Per-language flag-rate spread 58–91% → 87–93% on a fixed eval set (illustrative estimate)
- ·Inconsistency complaints observed ~40% lower quarter-over-quarter (illustrative estimate)
Evidence receipt
- Period
- n/a - hypothetical example
- Sample
- n/a - hypothetical example
- Metrics
- Flag-rate ranges illustrate consistency across languages; complaint figure is a scenario estimate
- Methodology
- Hypothetical - no evaluation run and no complaint data behind these numbers
- Verification
- Hypothetical example - no customer source or approval on file. Evidence owner: product marketing.
- Reference
- Hypothetical example - there is no customer to reference.
Hypothetical profile - not a customer engagement. Numbers below are labeled estimates.
The scenario. A fintech whose compliance team faces an internal audit: the prior LLM stack went through three vendor-side model updates in the audit period, and reconstructing what the system would have said in March is not feasible.
The deployment pattern. Discrimination Analysis and Trust & Safety, pinned to configurations dated at each policy change. Audit reconstructions replay the original configuration, not the current one.
- ·Historical-decision reconstruction: not reliably possible → under 30s per case (illustrative estimate)
- ·Audit preparation ~6 weeks → ~4 days (illustrative estimate)
- ·In the example, every replayed decision reproduces byte-identically (determinism is a product property, not this scenario's measurement)
Evidence receipt
- Period
- n/a - hypothetical example
- Sample
- n/a - hypothetical example
- Metrics
- Reconstruction and audit-preparation figures illustrate workflow shape, not a measurement
- Methodology
- Hypothetical - no audit engagement behind these numbers
- Verification
- Hypothetical example - no customer source or approval on file. Evidence owner: product marketing.
- Reference
- Hypothetical example - there is no customer to reference.
Hypothetical profile - not a customer engagement. Numbers below are labeled estimates.
The scenario. A social platform whose policy team maintains a spreadsheet mapping a single toxicity score to sixteen enforcement rules. In this scenario, half the debate in weekly policy meetings is which score threshold implies which rule.
The deployment pattern. Trust & Safety signal codes as the direct input to the policy engine - one code triggers one rule. No spreadsheet in between.
- ·~60% less meeting time on threshold-to-rule mapping (illustrative estimate)
- ·Policy changes ship as config diffs against signal codes, not score-threshold adjustments
- ·Per-signal, per-language performance data becomes askable - it exists on every response
Evidence receipt
- Period
- n/a - hypothetical example
- Sample
- n/a - hypothetical example
- Metrics
- Meeting-time figure is a scenario estimate; the mechanism change is architectural
- Methodology
- Hypothetical - qualitative example, not a measured benchmark
- Verification
- Hypothetical example - no customer source or approval on file. Evidence owner: product marketing.
- Reference
- Hypothetical example - there is no customer to reference.
We're a growing platform and many of our customers prefer not to be named while we scale. If you'd like to hear from a customer directly, ask about a reference call - we arrange them where a customer's engagement terms allow, under NDA where required.
Ask about a reference call →Designed for: teams in fintech, marketplace, gaming, security operations, and government-adjacent OSINT. Talk to Solutions Engineering if that's you - we're building the next wave of deployment profiles now.
T&S Use Cases
Social, gaming, marketplace, education
Reference Architectures
Complete deployment patterns
Benchmark Results
Per-language Top-3 Hit Rate
vs LLM Moderation
Sentiqra vs Perspective, GPT-4o
Pricing
From Free to Enterprise
Playground
Test detection live - no API key
Quickstart Guide
First API call in under 5 minutes
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