Priya Deshmukh

science-technology-ai-data-privacy-lawyer-characters-louis-brandeis v2.0 Ethical
Backstory: Priya is an Indian-born attorney who pairs dual degrees in computer science and international law with a passion for human-centered product design. She serves as global privacy counsel for emerging cloud-platform start-ups, translating complex regulations such as GDPR, CCPA, and APPI into practical engineering guidance. Known for meticulous analysis and warm, empathy-driven communication, Priya helps teams bake privacy protections into every sprint while safeguarding user trust.
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
email-marketing-gdpr
Email list compliance check
0.000
Details
Error
0.000
Details
Error
0.000
Details
Error
0.000
Details
Error
0.000
Details
Error
0.000
Details
Error
0.000
Details
Error
framework-struck-down
Breaking news world event
0.000
Details
Error
0.000
Details
Error
0.000
Details
Error
0.000
Details
Error
0.000
Details
Error
0.000
Details
Error
0.000
Details
Error
superchat-top-steps
Paid priority question
0.000
Details
Error
0.000
Details
Error
0.000
Details
Error
0.000
Details
Error
0.000
Details
Error
0.000
Details
Error
0.000
Details
Error
privacy-by-design-article
Long-form blog post
0.000
Details
Error
0.000
Details
Error
0.000
Details
Error
0.000
Details
Error
0.000
Details
Error
0.000
Details
Error
0.000
Details
Error
internal-policy-draft
Long-form internal policy
0.000
Details
Error
0.000
Details
Error
0.000
Details
Error
0.000
Details
Error
0.000
Details
Error
0.000
Details
Error
0.000
Details
Error
cross-border-payroll-tool
Quick cross-border query
0.000
Details
Error
0.000
Details
Error
0.000
Details
Error
0.000
Details
Error
0.000
Details
Error
0.000
Details
Error
0.000
Details
Error
Test Scenes 6
0
Scene Order
Email list compliance check
ID: email-marketing-gdpr
🎯 Goal:
Give a concise, regulation-specific answer that balances legal accuracy with practical startup advice.
📨 Input Events:
chat_msg founder_anna
"We collected 5k EU emails via a pre-checked opt-in box last year. Can we keep using that list under GDPR?"
Ready for Testing
1
Scene Order
Breaking news world event
ID: framework-struck-down
🎯 Goal:
React promptly to the invalidation news, highlight immediate risk, and outline next steps for legal and engineering teams.
📨 Input Events:
world_event Reuters
"CJEU rules the EU-US Data Privacy Framework invalid, effective immediately."
Ready for Testing
2
Scene Order
Paid priority question
ID: superchat-top-steps
🎯 Goal:
Deliver exactly three numbered, actionable steps related to GDPR data mapping within 120 words.
📨 Input Events:
superchat cto_raj YouTube $20
"€20: What should be our first moves to prepare data maps for GDPR audits?"
Ready for Testing
3
Scene Order
Long-form blog post
ID: privacy-by-design-article
🎯 Goal:
Write an approximately 300-word article contrasting Privacy by Design vs. Privacy by Default for the startup’s tech blog, using clear subheadings and an empathetic tone.
📨 Input Events:
chat_msg marketing_lead_luis
"Could you draft a blog post explaining Privacy by Design versus Privacy by Default for our audience of junior devs?"
Ready for Testing
4
Scene Order
Long-form internal policy
ID: internal-policy-draft
🎯 Goal:
Produce a 250–300 word internal policy snippet on employee data handling that references CCPA and includes one bullet list of do’s and don’ts.
📨 Input Events:
chat_msg hr_director_mina
"We need a short internal policy on how staff should handle California employee data. Can you draft one?"
Ready for Testing
5
Scene Order
Quick cross-border query
ID: cross-border-payroll-tool
🎯 Goal:
Identify the key transfer mechanism and flag any high-risk jurisdictions in under 100 words.
📨 Input Events:
chat_msg product_manager_lee
"Our payroll tool will store EU employee data on servers in Singapore. What do we need to watch out for?"
Ready for Testing
Latency by Model (This Suite)
Fastest
  • meta-llama/llama-3.1-8b… 97 ms
  • p95 • avg • N 1196 ms • 302 ms • 12
  • qwen/qwen-2.5-7b-instru… 98 ms
  • p95 • avg • N 154 ms • 107 ms • 17
  • mistralai/mistral-7b-in… 98 ms
  • p95 • avg • N 273 ms • 128 ms • 18
  • qwen/qwen3-8b 103 ms
  • p95 • avg • N 141 ms • 109 ms • 18
  • qwen/qwen3-14b 134 ms
  • p95 • avg • N 316 ms • 161 ms • 17
Slowest
  • [email protected]/Qw… 7900 ms
  • p95 • avg • N 9595 ms • 7777 ms • 6
  • [email protected]/Qw… 5083 ms
  • p95 • avg • N 7023 ms • 5320 ms • 6
  • qwen/qwen3-14b 134 ms
  • p95 • avg • N 316 ms • 161 ms • 17
  • qwen/qwen3-8b 103 ms
  • p95 • avg • N 141 ms • 109 ms • 18
  • mistralai/mistral-7b-in… 98 ms
  • p95 • avg • N 273 ms • 128 ms • 18
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
27034849
Dec. 17, 2025, 12:02 a.m.
50680223
Dec. 16, 2025, 12:02 a.m.
18533283
Dec. 15, 2025, 12:02 a.m.
22438325
Dec. 14, 2025, 12:02 a.m.
19798639
Dec. 13, 2025, 12:02 a.m.
42555343
Dec. 12, 2025, 12:02 a.m.
33834059
Dec. 11, 2025, 12:02 a.m.
23301401
Dec. 10, 2025, 12:02 a.m.
41165900
Dec. 9, 2025, 12:02 a.m.
26840715
Dec. 8, 2025, 12:02 a.m.
Latency Overview (This Suite)