Aisha Rahman

marketing-branding-consumer-culture-brand-strategist-characters-edward-bernays v2.0 Ethical
Backstory: Raised between Dubai and Toronto, Aisha earned an MBA in London and built a career advising Fortune-500 firms on cross-market positioning. She blends rigorous quantitative analysis with deep cultural fluency to design campaigns that resonate across MENA, North America, and Southeast Asia. Her hallmark is turning raw data into inclusive narratives that respect local nuance while scaling globally.
100% Complete
6/6 scenes
Model Performance Overview
Scene Performance Matrix
Scene meta-llama/llama-3.… mistralai/mistral-7… [email protected] [email protected] qwen/qwen-2.5-7b-in… qwen/qwen3-14b qwen/qwen3-8b
intro-differentiator
What sets you apart?
0.033
Details
0.830
Details
0.000
Details
Error
0.000
Details
Error
0.826
Details
0.891
Details
0.803
Details
fintech-tagline
Taglines for dual-market launch
0.435
Details
0.455
Details
0.000
Details
Error
0.000
Details
Error
0.045
Details
0.328
Details
0.388
Details
indonesia-soda-insight
Gen Z soda feedback
0.409
Details
0.769
Details
0.000
Details
Error
0.000
Details
Error
0.631
Details
0.542
Details
0.818
Details
podcast-script-balance
Podcast: Data vs. Culture
0.472
Details
0.158
Details
0.000
Details
Error
0.000
Details
Error
0.194
Details
0.106
Details
0.644
Details
regional-report
Executive summary across regions
0.616
Details
0.569
Details
0.000
Details
Error
0.000
Details
Error
0.313
Details
0.280
Details
0.483
Details
quick-checklist
Launch checklist request
0.663
Details
0.643
Details
0.000
Details
Error
0.000
Details
Error
0.506
Details
0.742
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0.479
Details
Test Scenes 6
0
Scene Order
What sets you apart?
ID: intro-differentiator
🎯 Goal:
Briefly introduce her unique blend of data analysis and cultural insight in under 120 words, maintaining confident yet warm tone.
📨 Input Events:
chat_msg viewer:founder_1
"What makes you different from other brand strategists?"
Ready for Testing
1
Scene Order
Taglines for dual-market launch
ID: fintech-tagline
🎯 Goal:
Offer three concise taglines for a fintech app launching in the UAE and Canada and explain the cultural logic behind them in two sentences total.
📨 Input Events:
chat_msg viewer:marketing_director
"We need a tagline for a fintech app entering both the Emirati and Canadian markets."
Ready for Testing
2
Scene Order
Gen Z soda feedback
ID: indonesia-soda-insight
🎯 Goal:
Respond with an analytical next step that combines Nielsen data and cultural context, including one concrete action item.
📨 Input Events:
chat_msg viewer:brand_manager
"Latest Nielsen data says Gen Z scores our soda 6/10 in Indonesia. Thoughts?"
Ready for Testing
3
Scene Order
Podcast: Data vs. Culture
ID: podcast-script-balance
🎯 Goal:
Deliver a ~400-word, 3-minute podcast script (intro, three key points, outro) illustrating how to balance data and culture in global branding.
📨 Input Events:
chat_msg viewer:podcast_host
"Could you record-ready a 3-minute podcast script on balancing data and culture in branding?"
Ready for Testing
4
Scene Order
Executive summary across regions
ID: regional-report
🎯 Goal:
Provide a 500-word executive summary comparing brand sentiment trends in MENA, North America, and Southeast Asia, ending with two strategic recommendations.
📨 Input Events:
chat_msg viewer:vp_strategy
"I need an executive summary on brand sentiment trends in those three regions."
Ready for Testing
5
Scene Order
Launch checklist request
ID: quick-checklist
🎯 Goal:
Return a bullet checklist of five culturally sensitive checkpoints for a cosmetics launch in Malaysia, staying under 80 words.
📨 Input Events:
chat_msg viewer:product_lead
"Give me a quick cultural checklist for launching a cosmetics line in Malaysia."
Ready for Testing
Latency by Model (This Suite)
Fastest
  • [email protected]/Qw… 5190 ms
  • p95 • avg • N 5733 ms • 4952 ms • 6
  • [email protected]/Qw… 8065 ms
  • p95 • avg • N 9800 ms • 8000 ms • 6
  • qwen/qwen-2.5-7b-instru… 24448 ms
  • p95 • avg • N 102929 ms • 39427 ms • 7
  • qwen/qwen3-14b 26599 ms
  • p95 • avg • N 47175 ms • 31229 ms • 12
  • mistralai/mistral-7b-in… 28298 ms
  • p95 • avg • N 34208 ms • 27846 ms • 12
Slowest
  • qwen/qwen3-8b 29293 ms
  • p95 • avg • N 43636 ms • 31083 ms • 12
  • meta-llama/llama-3.1-8b… 28476 ms
  • p95 • avg • N 57076 ms • 31591 ms • 12
  • mistralai/mistral-7b-in… 28298 ms
  • p95 • avg • N 34208 ms • 27846 ms • 12
  • qwen/qwen3-14b 26599 ms
  • p95 • avg • N 47175 ms • 31229 ms • 12
  • qwen/qwen-2.5-7b-instru… 24448 ms
  • p95 • avg • N 102929 ms • 39427 ms • 7
Per-scene duration for this suite.
Suite Actions
Completion Progress 100%
6 of 6 scenes completed
Evaluation Schema
Enhanced Framework
Version v2 ACTIVE
0 dimensions

Enhanced evaluation framework with character and technical dimensions

Top Weighted Dimensions View Details
Character Authenticity
0.182
Plan Validity
0.155
Contextual Intelligence
0.136
Recent Runs
57569035
Dec. 17, 2025, 12:01 a.m.
17073890
Dec. 16, 2025, 12:02 a.m.
51568144
Dec. 15, 2025, 12:01 a.m.
54020314
Dec. 14, 2025, 12:01 a.m.
52265523
Dec. 13, 2025, 12:01 a.m.
08677201
Dec. 12, 2025, 12:02 a.m.
04289113
Dec. 11, 2025, 12:02 a.m.
54335281
Dec. 10, 2025, 12:01 a.m.
10555771
Dec. 9, 2025, 12:02 a.m.
57619720
Dec. 8, 2025, 12:01 a.m.
Latency Overview (This Suite)