🌐 中文文档 | English

🧠 AskPeer — On-Demand Academic Expert Network

Got a research problem? Ask a peer who's solved it before.

AskPeer connects researchers with domain experts for on-demand consultation. Not peer review. Not co-authorship negotiation. Just: you have a problem → we find the person who knows the answer → you talk.

---

Why

Three things broke in academic knowledge exchange:

Broken Thing	How It Works Now	AskPeer
Peer review	3-6 months, unpaid, random quality	Not our problem. We do consultation.
Methodology help	Cold-email a paper author, hope they reply	AI matches you to the right person in hours
Cross-domain expertise	Your PI's network. That's it.	Global expertise graph, no gatekeepers

The a16z-backed Ethos proved this model works for industry ($500/hr expert calls). AskPeer brings it to academia — with academic-appropriate incentives, not just cash.

---

What AskPeer Is

Researcher has a question
        │
        ▼
   AI analyzes question
   + searches expertise graph
        │
        ▼
   Top 3 expert matches
        │
        ▼
   Researcher picks one
        │
        ▼
   30-min consultation
   (call / async / in-person)
        │
        ▼
   Problem solved. Rate & thank.

Delivery modes — flexible, not just paid calls:

• 🎥 Live call — 30min Zoom / video
• ✍️ Async feedback — written response within 48h
• 🤝 Collaboration — if both sides want, turn into co-authorship

Incentives — academic-appropriate:

• Experts set their own rates (or pro-bono)
• Institutional credit / departmental recognition
• Co-authorship opt-in (if consultation leads to collaboration)

---

MVP — Phase 1

Minimal matching engine. No payment. No auth wall.

┌─────────────────────────────────────────┐
│  AskPeer MVP                            │
│                                         │
│  [ Describe your research problem...  ] │
│  [ Your field: _______ ]               │
│  [ Preferred mode: call / async / any ] │
│                                         │
│         [ Find an Expert ]              │
│                                         │
│  ── Results ──────────────────────────  │
│  Dr. A — cryo-EM, membrane proteins     │
│  Dr. B — MD simulation, force fields    │
│  Dr. C — statistical analysis, R        │
│                                         │
│  [ Connect with Dr. A ]                 │
└─────────────────────────────────────────┘

MVP delivers: problem → match → intro email. No platform calls, no payment — just connect two researchers.

---

Technical Approach

Layer	Technology
Expertise extraction	LLM reads papers/CV → structured expertise graph
Question understanding	LLM classifies domain + method + depth
Matching	Vector similarity over expertise embeddings
Frontend	Minimal web UI (Streamlit or plain HTML)
Backend	Python + SQLite (MVP)

---

Comparison

	Ethos (askethos.com)	AskPeer
Domain	Industry/business	Academic research
Users	Companies + consultants	Researchers + researchers
Price	$500/hr typical	Academic-appropriate (flexible)
Delivery	Paid calls only	Calls, async, collaboration
Vetting	AI voice interview	AI paper-analysis interview
Funding	a16z ($22.75M)	Bootstrapped / open-source

---

Development Status

• Concept & design
• MVP: matching engine
• MVP: web UI
• Expert onboarding pipeline
• Pilot with 10 experts, 10 researchers

---

Inspiration

• Ethos — AI expert network (a16z-backed)
• SciDAO — decentralized science
• The unbearable slowness of traditional peer review

---

License

MIT