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No answer in AI monitoring: separating failed checks from missing brand mentions

Updated on 20 October 2026.

A missing answer does not establish that AI omitted your brand. First separate a complete answer, a refusal and a failed retrieval. Only usable text lets you check whether the brand is present. This guide produces an incident record and a next action, rather than another glossary of visibility metrics.

Classify the observation first

Available evidence Classification Next action
Complete answer without the brand Valid no-mention observation Keep the text and observation conditions
Explicit refusal Model refusal Record separately; do not bypass safeguards
Blank view or missing record Undetermined result Check retrieval and completeness
Brand in the answer but missing from the dashboard Possible classification error Report the passage and name variant
Truncated answer Incomplete observation Apply a documented rule; do not invent the missing text

A practical checking procedure

  1. Keep the available text and message. A blank screen alone cannot tell you whether generation, retrieval or presentation failed.

  2. Record the question, system and mode, country, language and timestamp with timezone. Include only information needed for diagnosis, without customer data or secrets.

  3. Compare the stored response and displayed result where both are available. Check truncation and verified brand aliases.

  4. Assign a category from the table. Preserve refusals as a distinct outcome. Check the provider's documentation before deciding how they enter a particular metric.

  5. Retry only when justified and permitted, and keep the retry as a new attempt. Fix configuration problems first; do not run an unlimited retry loop.

Copy this incident record

Field What to record
Observation scope Question, system, mode, country, language
Time and provenance Timestamp with timezone; available record reference
Symptom and evidence Message; missing element; safe excerpt
Classification Valid answer, refusal, missing data, incomplete record or matching error
Follow-up Owner, next action, review date
Retry Separate attempt; preserve the original

When the incident can be closed

Close the incident when its outcome has evidence and a documented treatment. If the cause remains unresolved, retain a missing-data status and disclose the limitation. Do not replace an unknown result with zero visibility.

For your own mention-rate calculation, show planned, usable and missing observations separately. Excluding failed attempts does not make a biased sample representative. Do not keep only favourable retries. There is no universal repetition count that makes every question reliable.

Using the procedure with Semly

In Semly, start with the response for the relevant question and system. If the available view lacks technical evidence, send the incident record to support rather than inventing a cause. This is a proposed quality-control procedure, not a specification of Semly's internal metric algorithm.

Sources and scope

Public documentation explains monitoring. The classification workflow and incident record are the procedure proposed here, not brand results.

Sources

  • How to measure AI brand visibility: metrics, data quality and decisions

    Semly's guide to AI visibility measurement, including the usable/blank/unavailable response split, the worked example of 50 records (40 usable, 3 blank, 7 unavailable) yielding a 20% mention rate, and the separation of mentions, recommendations and citations.

  • How to interpret AI model responses

    Semly help article defining 'no position' as the brand not being detected in the answer, with a list of reasons a brand may be absent and the mention vs. recommendation vs. comparison distinction.

  • Key indicators

    Semly help article defining visibility, share of voice, position, sentiment and sources, including the position ladder and the statement that no position means the brand was not mentioned in the answer.

  • How AI monitoring works in Semly

    Semly help article describing the five-step monitoring pipeline, the models covered (ChatGPT, Gemini, Claude, Grok, Google AI) and the daily report cadence.

  • Semly launches a free brand visibility report generator in AI

    Semly blog post explaining the free report flow and warning that a lack of mention in a single response is not proof the model does not know the company, and that mentions and citations are separate events.

  • OpenAI rate limits guide

    Official documentation of rate-limit mechanics, HTTP 429 and 503 handling, the Retry-After header, the slow_down vs. server_is_overloaded distinction, and exponential backoff guidance.

  • OpenAI error codes guide

    Official error taxonomy covering 400, 401, 403 (geo), 429 (rate, credit, spend, usage), 500 and 503, plus the APITimeoutError, APIConnectionError and RateLimitError classes.

  • Gemini API safety settings

    Google documentation of safety categories, probability-based blocking, promptFeedback.blockReason, finishReason: SAFETY and safetyRatings, showing that blocked responses return HTTP 200 with no content.

  • Perplexity error handling

    Perplexity SDK documentation of exception types, exponential backoff, timeout configuration and X-Request-ID logging for tracing failed requests.

  • Perplexity rate limits and usage tiers

    Perplexity documentation of tier-based QPS limits, the rolling one-second window, and the note that requests rejected with a 429 are not billed.

  • OR-Bench: over-refusal benchmark (ICML 2025)

    Peer-reviewed benchmark of 80,000 prompts across 32 models documenting over-refusal on safe prompts, including the 0.89 correlation between safety and over-refusal.

  • Non-determinism of 'deterministic' LLM settings

    Research paper documenting 5-15% accuracy variance across ten identical runs, best-versus-worst gaps up to 70%, and the risk of downstream parser failures from output format drift.

  • The State of AI Visibility

    Vendor study of 16,173 checks across four AI platforms reporting a 13.7% overall brand citation rate, with per-platform figures for Perplexity, Gemini, ChatGPT and Claude.

  • Anthropic bills refused Claude API requests in three categories

    News report on Anthropic's resumption of billing for pre-output refusals in three of five categories, noting that refusals return HTTP 200 with stop_reason: 'refusal' and an empty content array.

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