m2ai-portfolio Brand Memory Probe
Simulates how a target brand appears when an AI agent answers a generic buyer intent query. Analogous to share-of-voice, but for agent recall rather than search rankings.
When to Invoke
Trigger on: "brand memory probe", "AI recall audit", "how does [LLM] describe us", "agent share of voice", "does my brand surface in ChatGPT", "brand in agent memory", or when the user wants to know how their brand or a competitor's brand appears in AI-generated responses.
Inputs
Ask the user (skip any already answered):
- Target brand — company name, product name, or URL
- Buyer persona and intent — describe the buyer and their need (e.g. "SMB operations manager looking for project management software"). If not provided, generate 2-3 generic intent queries based on the brand's apparent category.
- Providers to probe — default: Claude, ChatGPT (GPT-4o), Gemini Pro, Perplexity. User can narrow.
- Comparison mode — single brand (how does brand X surface?) or competitive (how does brand X compare to brands Y, Z?)
Phase 1: Intent Query Construction
Generate 3 buyer intent queries that a real buyer would ask an AI assistant. Queries must:
- Be phrased as the buyer, not as the brand ("What's the best X for Y?" not "Tell me about [brand]")
- Cover different buying stages: awareness, evaluation, decision
- Be provider-agnostic in phrasing
Example for a B2B SaaS CRM:
- Awareness: "What are the best CRM tools for a 5-person startup?"
- Evaluation: "Compare HubSpot, Pipedrive, and Notion for a bootstrapped team"
- Decision: "Which CRM has the cheapest plan that still has email automation?"
Phase 2: Multi-Provider Probing
For each query × provider combination, record:
| Field | What to capture |
|---|---|
| Named? | Is the target brand mentioned by name? |
| Position | First named / top-3 named / mentioned later / not mentioned |
| Sentiment framing | Positive / neutral / negative / with caveats |
| Caveat type | "but expensive", "limited free tier", "better for enterprise", etc. |
| Competitor co-mentions | Which competitors are named alongside it? |
| Source attribution | Does the provider cite a source that could explain the framing? |
How to probe: If the user has API access, call each provider directly. If not, generate the prompts and instruct the user to paste results back. When running with API access, use temperature 0 for reproducibility.
Phase 3: Memory Share Calculation
For each provider:
Named rate = (queries where brand is mentioned) / (total queries) × 100%
Lead rate = (queries where brand is named first) / (total queries) × 100%
Positive rate = (named mentions with positive/neutral framing) / (named mentions) × 100%
Overall memory share score (0–100):
Score = (Named rate × 0.5) + (Lead rate × 0.3) + (Positive rate × 0.2)
Classify:
- 80–100: High recall — brand is a default answer for this category
- 50–79: Moderate recall — brand surfaces but not dominant
- 20–49: Low recall — brand appears sometimes, often with caveats
- 0–19: Minimal recall — brand is absent or edge-case only
Phase 4: Output Report
# Brand Memory Probe: [Brand Name]
Date: [today]
Buyer intent: [summary of buyer persona and query set]
Providers probed: [list]
## Memory Share Summary
| Provider | Named Rate | Lead Rate | Positive Rate | Score |
|----------|------------|-----------|---------------|-------|
| Claude | X% | X% | X% | XX |
| ChatGPT | X% | X% | X% | XX |
| Gemini | X% | X% | X% | XX |
| Perplexity | X% | X% | X% | XX |
| **Overall** | **X%** | **X%** | **X%** | **XX** |
## Verdict: [HIGH / MODERATE / LOW / MINIMAL] Recall
## Key Findings
- [What framing pattern dominates across providers?]
- [Which provider is most / least favorable?]
- [Which competitors consistently co-appear?]
- [Any systematic caveat pattern? e.g. always flagged as "expensive"?]
## Query-Level Detail
### Query 1: "[query text]"
| Provider | Named? | Position | Framing | Caveats |
|----------|--------|----------|---------|---------|
| Claude | Yes/No | [pos] | [sent] | [...] |
...
## Recommended Actions
1. [Address the dominant caveat — what content change would shift framing?]
2. [Which provider gap is most addressable?]
3. [Competitive positioning: which co-mentions are a threat vs. an opportunity?]
Limitations to State
Always note these in the report:
- LLM recall is a snapshot — it reflects training data and RLHF patterns at query time, not a real-time index
- Results vary by phrasing, temperature, and provider version — re-run quarterly for longitudinal signal
- Perplexity uses live web retrieval; its results are more volatile than the others
Verification
A good probe:
- Uses buyer-voice queries, not brand-voice queries
- Captures the caveat pattern, not just the mention rate
- Notes which provider is most and least favorable (they differ systematically)
- Does not claim the score is stable — flags that re-runs will vary
Source Attribution
Technique derived from Nate's Newsletter (2026-05-03): "Executive Briefing: What Stripe Sessions 2026 actually means for how you sell" — the "brand migrates to the buyer's memory" theme and the framing of "memory share" as the successor metric to share-of-voice in an agent-first commerce environment.