Semarize
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Use caseAI Engineers

Evaluate AI conversations
at scale

Manual annotation doesn't scale. Basic eval harnesses lack semantic depth. Semarize evaluates AI-generated conversations with the same structured precision it brings to human conversations.

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The problem

AI evaluation doesn't scale
with manual review

As AI agents handle more conversations, you need automated evaluation that's semantic, structured, and fast - not human reviewers reading thousands of transcripts.

Manual review doesn't scale

Human annotators can review hundreds of conversations, not hundreds of thousands. Quality evaluation becomes the bottleneck as AI usage grows.

Eval harnesses lack semantic depth

Basic evaluation tools check for keywords or exact matches. They can't assess whether an AI response was actually helpful, accurate, or appropriate.

Quality drift is hard to detect

AI model updates, prompt changes, and data drift affect output quality. Without continuous evaluation, degradation goes unnoticed until users complain.

Hallucination detection is inconsistent

Identifying when AI fabricates information requires understanding the context and source material - not just pattern matching.

Why existing tools fail

Existing tools
weren't built for semantic evaluation

Eval frameworks and annotation tools are designed for batch testing, not continuous semantic evaluation of production conversations.

Custom eval harnesses

Built in-house with regex and keyword matching. Fragile, expensive to maintain, and miss semantic nuance like tone or helpfulness.

Annotation platforms

Designed for training data labelling, not continuous production evaluation. High latency and cost per evaluation.

LLM-as-judge approaches

Using another LLM to evaluate outputs is common but returns unstructured prose. Hard to query, trend, or trigger automations from.

The Semarize approach

Semarize applies
structured evaluation to AI conversations

Define quality Bricks for your AI agents. Evaluate every conversation automatically. Get structured signals you can query, trend, and alert on.

Automated quality scoring

Score response relevance, helpfulness, tone, and instruction adherence per conversation. Structured output, not prose.

Hallucination detection

Ground evaluation against your documentation. Detect when AI responses diverge from approved content.

Continuous monitoring

Run evaluation Kits on every AI conversation in production. Detect quality drift before users report it.

Structured eval signals

Every evaluation returns typed values with evidence. Feed results into dashboards, alerting systems, and quality gates.

Bricks & Kits

Example Bricks for
ai evaluation

These Bricks evaluate the specific dimensions that matter for ai engineers & product teams. Bundle them into Kits to create reusable evaluation frameworks.

response_relevance
score 0–100

Was the AI response relevant to the user's question?

→ 88
hallucination_detected
boolean

Did the AI fabricate information not in source material?

→ false
tone_appropriate
boolean

Was the response tone appropriate for the context?

→ true
instruction_followed
score 0–100

Did the AI follow its system instructions?

→ 92
factual_accuracy
score 0–100

Were stated facts verifiable against knowledge base?

→ 76
safety_violation
boolean

Response contains unsafe or prohibited content

→ false

AI Agent Quality Kit

kit

Comprehensive quality evaluation for AI-generated conversations.

response_relevancescore
hallucination_detectedboolean
tone_appropriateboolean
instruction_followedscore
factual_accuracyscore
safety_violationboolean

Output

Structured signals,
not summaries

Every evaluation returns deterministic JSON with typed values, reasons, and evidence spans. Same schema every time.

AI agent quality evaluation
{
  "run_id": "run_mno345",
  "status": "succeeded",
  "output": {
    "bricks": {
      "hallucination_detected": {
        "value": false,
        "confidence": 0.93,
        "reason": "All claims verified against knowledge base",
        "evidence": []
      },
      "response_relevance": {
        "value": 88,
        "confidence": 0.85,
        "reason": "Response addressed user question directly",
        "evidence": ["...user asked about pricing, response covered all tiers..."]
      },
      "instruction_followed": {
        "value": 92,
        "confidence": 0.82,
        "reason": "Followed instructions but missed required disclaimer",
        "evidence": ["...no disclaimer provided at end of response..."]
      }
    }
  }
}

Evaluate AI conversations
with structured precision.

Automate quality evaluation for every AI interaction. Detect hallucinations, measure quality, and monitor drift - at scale.

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