Support the Consumer AI Meta‑Analysis Project
A Public‑Benefit Research Initiative by AskAFriend Publishing
Every day, millions of people turn to AI systems for help with legal problems, insurance disputes, home repairs, medical bills, and consumer fraud. These are high‑stakes decisions — and the guidance people receive can shape their finances, their rights, and their safety.
Yet no independent, cross‑domain evaluation exists to measure how well AI models actually perform in these real‑world consumer scenarios.
We’re changing that.
What We’re Building
The Consumer AI Meta‑Analysis Project is the first structured, multi‑domain evaluation of AI reasoning across six critical areas of everyday life:
- Legal Literacy
- Insurance Navigation
- Home Repair & Contractor Issues
- Home Buying & Real Estate
- Consumer Protection & Fraud
- Health Advocacy
This project will produce:
- A registered research protocol
- A public dataset of AI responses
- A transparent scoring rubric
- A cross‑model comparison of leading AI systems
- A consumer‑facing knowledge base to help people make safer decisions
This work is being developed by AskAFriend Publishing LLC, an independent educational publisher with a mission to improve consumer literacy and empower people to navigate complex systems.
Why This Matters
Consumers rely on AI for guidance in situations where mistakes can be costly:
- A tenant facing eviction
- A patient disputing a medical bill
- A homeowner dealing with a contractor dispute
- A parent navigating insurance appeals
- A worker facing discrimination
- A buyer trying to understand a real estate contract
This project helps answer a simple but urgent question:
“Can AI give people safe, accurate, actionable guidance when it matters most?”
Your support helps make that possible.
How Sponsorship Helps
Sponsorship funds are used to:
- Run large‑scale evaluations across multiple AI models
- Cover API usage costs
- Support data cleaning and scoring
- Build the public dataset
- Develop the consumer‑facing website
- Publish the final meta‑analysis
- Expand the project into additional domains
This is a public‑benefit research project, not a commercial product. Your support directly enables independent evaluation that helps consumers, educators, policymakers, and the AI community.
Ways to Sponsor
You can support the project in several ways:
1. API Credits
AI companies and developers can donate API credits to help run the evaluation pipeline.
2. Micro‑Grants ($100–$500)
Perfect for individual supporters, LinkedIn angels, and small organizations.
3. Project Sponsorship ($1,000–$5,000)
Ideal for companies or foundations that want to support independent AI evaluation.
4. Institutional Sponsorship ($10,000+)
For organizations that want to be recognized as core supporters of consumer AI safety.
Sponsor Benefits




Depending on your level of support, sponsors may receive:
- Name or logo placement on the project website
- Acknowledgment in the published protocol
- Acknowledgment in the final meta‑analysis
- Early access to datasets and findings
- A “Supporting Responsible AI” badge
- Optional collaboration opportunities
This is a chance to support meaningful, independent research that benefits millions of consumers.
Why Support This Project Now?
AI is moving fast. Consumer protection is not.
This project fills a critical gap by evaluating AI systems where people actually rely on them — not in academic benchmarks, but in real‑world consumer scenarios.
Your support helps ensure that:
- AI guidance is safer
- consumers are better informed
- policymakers have better data
- the public has access to transparent evaluation
If you believe in responsible AI, consumer empowerment, and independent research, your sponsorship makes a real difference.
Become a Sponsor
If you’d like to support the project, collaborate, or learn more, reach out:
AskAFriend Publishing LLC Grand Junction Colorado / Clearwater, Florida / Guadalajara, Mexico
Email us at: research @ askafriend.com
Let’s build something that protects consumers, improves AI safety, and leaves a meaningful legacy.