DETERMINISTIC ANALYSIS VS GENERATIVE ANALYSIS
When an analysis has to replay,
generation is the wrong abstraction.
Generative models are excellent when you need flexible reasoning. Sentiqra is built for a different job: fixed, versioned analysis that produces the same result under the same pinned configuration and output profile.
Same input + same pinned version + same configuration + same output profile = same analysis.
Versioned taxonomy · Config-pinned results · Structured signals · 12 production analyzers
Compare the workflow, not the number of labels
LLMs can return multiple labels too. Top 3 is not an exclusive capability or proof of better detection. Evaluate whether your workflow needs Sentiqra's documented taxonomy, versioned analysis, structured outputs, and separation between detection and customer policy.
Keeping relevant secondary signals can support different routing or review tasks. Whether it improves your operation depends on taxonomy fit, false alerts, and reviewer workload. Compare those outcomes against your existing process on the same labeled cases.
Use both where the workflow calls for it
Sentiqra signals → Your policy → human or LLM review where needed
An optional customer-built workflow. Reviewers or an LLM can interpret complex context; your policy owns the final action. Signals and receipts do not prove that a classification is correct.
First: choose the comparison you're actually making.
Not every AI moderation product works the same way.
Prompted generative classifiers
A general-purpose LLM is prompted to decide which category applies.
Best when: you need flexible interpretation, reasoning, or rapidly changing instructions.
Dedicated moderation APIs
Purpose-built analysis services return predefined safety categories and scores.
Best when: a relatively compact safety taxonomy covers your requirements.
In-house analyzers
Models and policy logic developed and maintained by your own team.
Best when: the analysis logic itself is strategic IP and you have the engineering resources to own it.
Sentiqra
A deterministic, versioned analyzer with fixed indicators across 12 production analyzers.
Best when: taxonomy granularity, replayability, and structured signals matter.
The difference is control over the analysis.
Sentiqra
The taxonomy is part of the product. The analysis comes from a pinned, versioned detection configuration.
indicator dehumanization confidence very_high language en configuration sqcfg_ZZW77NM9 verification.fingerprint fp_a83f...91c
Replay the same input under the same pinned configuration and output profile and the analysis is reproducible.
Prompted generative classification
The taxonomy usually lives in the prompt, schema, application logic, or some combination of them.
The result can depend on:
- · the model selected
- · model version
- · system and user prompts
- · decoding/runtime configuration
- · surrounding context
- · application validation logic
That flexibility is useful. It is also a different architecture.
Six questions to ask any analysis provider.
| Question | Sentiqra | Prompted generative classifier |
|---|---|---|
| Is the taxonomy fixed and versioned? | Yes | Depends on implementation |
| Can I pin the detection configuration? | Yes | Provider-dependent |
| Can I replay historical analyses? | Designed for it | Architecture-dependent |
| Are labels generated dynamically? | No | Can be |
| Does every result include analysis metadata? | Yes | Application-dependent |
| Can it perform free-form reasoning? | No | Yes |
Neither architecture is universally better. The question is whether your workflow needs reasoning, analysis, or both.
Using Perspective API?
Perspective API is scheduled to shut down on December 31, 2026.
If Perspective is currently part of your moderation stack, the practical question is no longer simply whether to switch. It's what you replace it with. Sentiqra can be evaluated alongside other moderation options through a parallel run against your existing traffic and labeled evaluation set.
Use LLMs for reasoning. Use deterministic tools for fixed analysis.
There are many tasks where an LLM is the better tool:
LLMs are better at
- · Interpreting ambiguous context
- · Summarizing a case
- · Drafting a moderator response
- · Explaining policy in natural language
- · Synthesizing information from multiple sources
Fixed analyzers are better at
- · Assigning a documented policy indicator
- · Applying an immutable configuration
- · Producing machine-readable confidence values
- · Replaying a historical analysis
- · Routing a result through deterministic policy logic
You can also combine both. Use Sentiqra for the semantic analysis layer, then pass the structured result to an LLM for interpretation or response generation.
Explore MCP for AI agentsWhat about dedicated moderation APIs?
Purpose-built moderation APIs are different from prompting a general-purpose LLM. They can provide fixed categories, analysis scores, and low-cost or free moderation. For some applications, that's exactly what you need.
Sentiqra becomes relevant when you need more of the following:
Granularity
Hundreds of documented indicators rather than a small number of broad harm dimensions.
Domain coverage
One API spanning Trust & Safety, Cybersecurity, Crisis Intelligence, Market Intelligence, and Disinformation & Manipulation.
Version-pinned replay
Reproduce analyses against a documented historical detection configuration.
Indicator-level evidence
Preserve the specific indicator, confidence, language, and configuration ID behind the analysis.
Verify the difference instead of trusting a comparison table.
Send the same text to Sentiqra more than once.
Run 1
configuration sqcfg_ZZW77NM9 verification.fingerprint fp_a83f8e21...
Run 2
configuration sqcfg_ZZW77NM9 verification.fingerprint fp_a83f8e21...
Pin the same detection configuration and repeat the test later. Then run the evaluation you care about against your existing analyzer.
Measure:
Make the decision from your own workload.
Run the verification test →Migrating from a prompted LLM analyzer?
You do not need to replace the reasoning capability of your LLM. Move the parts of your workflow that require fixed analysis into Sentiqra and keep generative reasoning where it adds value.
Before
content ↓ LLM prompt ↓ generated analysis + explanation
After
content ↓ Sentiqra ↓ versioned analysis ↓ policy logic / optional LLM reasoning
This separates analysis from interpretation. In code, the change is small:
// Before: a prompt, re-interpreted each call
const r = await openai.chat.completions.create({
model: "gpt-4o",
messages: [{ role: "user",
content: `Is this harassment? "${text}"` }]
});
// After: a fixed analysis, replayable while the configuration remains supported
const r = await fetch(
"https://sentiqra.tech/v1/analyze", {
method: "POST",
headers: { "Authorization": "Bearer " + key },
body: JSON.stringify({
text, analyzers: ["trust-safety"] })
});
The code change is an afternoon. The real work is policy remapping - turning "is this harassment?" prompts into signal-level thresholds on your labeled set. Typical timeline: 1-3 weeks including a shadow-mode parallel run. The reference architecture walks through the full pattern, and the evaluation checklist covers threshold calibration.
View migration architectureCompare cost on your actual workload.
"Cost per call" is only meaningful when every provider is tested on the same workload. For generative APIs, price can depend on input tokens, generated output, selected model, service tier, and caching. For Sentiqra, use the applicable API-call pricing.
A useful comparison should therefore specify:
- · Number of analyses
- · Typical input size
- · Expected generated output
- · Selected competitor model
- · Retry behavior
- · Infrastructure assumptions
What reproducibility can support.
Versioned analyses can be useful in:
- · Internal reviews
- · User appeals
- · Model governance
- · Historical analysis
- · Backtesting
- · Investigation workflows
- · Compliance documentation
But reproducibility does not, by itself, make an enforcement decision legally correct or determine the regulatory analysis of your deployment. Those questions depend on the complete system and its intended use.
When Sentiqra is probably the better fit.
Choose Sentiqra when you need:
- ✓ Fixed, documented analysis categories
- ✓ Reproducibility against pinned versions
- ✓ Indicator-level confidence and metadata
- ✓ Multilingual analysis with documented evaluation
- ✓ More granular taxonomies than broad safety labels
- ✓ Detection across multiple risk domains
- ✓ Structured outputs for downstream policy logic
When another approach may be better.
Keep your existing solution when:
- · Free-form reasoning matters more than exact replay
- · A small general-purpose moderation taxonomy already covers your policies
- · A free dedicated moderation endpoint meets your accuracy and scale requirements
- · You're prototyping and do not yet need a production taxonomy
- · Your in-house analyzer is strategic IP worth maintaining
- · Migration cost exceeds the value of additional granularity and version control
The right analyzer is the one that fits the decision you're actually making.
Evaluate Sentiqra side by side.
For developers
Send the same request repeatedly and inspect the output yourself.
For engineering and Trust & Safety teams
Run Sentiqra in parallel with your current analyzer against your own labeled traffic.
For governance and procurement teams
Review the architecture, data-handling model, versioning, and documentation against your own requirements.
Positioning statements are informational and not legal advice. Buyers should conduct their own regulatory analysis for their specific deployment context. Competitive information references publicly documented product features and policies as of August 2026 - verify current details with the respective provider.