Perplexity ReSearcher

Perplexity for Researchers - where papers autonomously generate research and funding opportunities

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Project Story: Perplexity ReSearcher

Inspiration

The project was inspired by the imbalance between talented researchers and the lack of access to opportunities. As Creative AI co-Chair at NeurIPS 2025, I noticed researchers from São Paulo, Lagos, and Mumbai getting papers accepted but unable to afford the $4,700 required to attend. At the same time, AI companies spend $30,000+ per hire looking for people with the same expertise these authors already demonstrated in their peer-reviewed work.

Traditional hiring asks, "Do you have this skill?"—which is unverifiable. Research papers instead answer, "Can you reason through complex problems?"—with peer-reviewed evidence.

If we can match demonstrated reasoning (in papers) with real-world problems (in jobs), we can replace $30K recruiters with $5K sponsorships that fund researchers to attend and present. This aligns with Perplexity’s mission: helping people ask better questions and get evidence-based answers.

What it does

Perplexity ReSearcher connects companies with researchers by matching job descriptions to conference papers through reasoning-based analysis.

For Researchers

  1. Claim your accepted paper.
  2. The system analyzes your publications and extracts demonstrated expertise.
  3. Discover companies looking for similar problem-solving abilities.
  4. Accept sponsorships to attend and present your work.

For Organizations

  1. Paste a job description.
  2. The system extracts required domains and methods.
  3. Searches papers that demonstrate matching reasoning.
  4. Returns ranked, evidence-backed researcher profiles.

Result: Verified expertise replaces keyword filtering, enabling direct, transparent collaboration between researchers and recruiters.

How we built it

We built a full-stack AI-powered application using Next.js, integrating the Perplexity API (sonar-pro) for reasoning and skill extraction.

System Architecture

Frontend (Next.js + React + Tailwind)
    |
    |  HTTP / API Calls
    v
Backend (Next.js API Routes + Node.js)
    - Input validation
    - Business logic
    - Cache & in-memory DB
    - Calls Perplexity API
    |
    +-------------------------------+
    |                               |
    v                               v
Perplexity API (sonar-pro)     In-memory DB (cache)
 - Skill extraction             - Stores profiles
 - Paper analysis               - TTL: 30 minutes
 - Job matching

Tech Stack

  • Languages: TypeScript, JavaScript
  • Framework: Next.js 14 (full-stack React framework)
  • Frontend: React 18 with Tailwind CSS
  • Backend: Next.js API Routes on Node.js
  • AI Integration: Perplexity API (sonar-pro) for reasoning, skill extraction, and job-paper matching
  • Storage: In-memory Map for temporary researcher data (30-minute TTL)
  • Deployment: Vercel (serverless functions)
  • Data Source: Google Scholar via Perplexity search
  • Version Control: GitHub
  • Environment Management: .env for secure API key handling

Perplexity API Integration

The Perplexity API powers all reasoning and analysis features.

  1. Researcher Profile Fetching
   async fetchResearcherProfile(name, affiliation, limit = 10)
  • Searches Google Scholar via Perplexity
  • Extracts top N papers and author details
  • Returns structured researcher profiles
  1. Researcher-Job Matching
   async matchPaperToJob(paper, jobDescription)
  • Compares paper content and job requirements
  • Returns a score (0–100), reasoning summary, and citations
  1. Skill Extraction
   async askAboutResearcher(question, researcher)
  • Identifies technical domains and methods used
  • Generates concise skill summaries
  1. Paper Recommendations
   GET /api/researcher/recommendations?areas=<topics>
  • Suggests related NeurIPS 2024 papers
  • Ranks by relevance with reasoning explanations

API Configuration

{
  "model": "sonar-pro",
  "temperature": 0.2,
  "return_citations": true,
  "search_domain_filter": ["arxiv.org", "scholar.google.com", "github.com"]
}

Performance Optimizations

  • Parallel Processing: All researcher analyses run concurrently (Promise.all)
  • Caching: 30-minute in-memory TTL for repeated queries
  • Smart Prompts: Concise structured prompts reduce latency and token cost
  • Error Handling: Graceful degradation with retry logic
  • Batching: Combines multiple API calls per researcher for efficiency

Usage

Researchers

  • Enter name and affiliation
  • Select correct profile (if multiple)
  • View publications and recommended related work

Recruiters

  • Enter job description

  • Get ranked researcher matches with:

    • Match score (0–100)
    • Key skills and expertise
    • Evidence-backed reasoning
    • Relevant paper citations

Admin

  • Access /admin for researcher database
  • Includes seeded profiles (e.g., Yann LeCun, Geoffrey Hinton, Fei-Fei Li)

Data Sources: OpenReview (simulated), arXiv, Google Scholar, GitHub

Core Innovation: Uses Perplexity to match reasoning ability, not keywords—every match is supported by citations.

Challenges we ran into

  1. OpenReview API Access: Required institutional permissions; simulated realistic structure for demo.
  2. Keyword vs Reasoning: Initial keyword approach lacked depth; reframed prompts to infer reasoning and expertise.
  3. Redefining Matches: Focused on “demonstrated reasoning ability” instead of direct problem-solution matching.
  4. Transparency: Added “Inspect Reasoning” to show match evidence with citations.
  5. Demo Data Quality: Created synthetic but realistic researcher profiles for ethical demonstration.

Accomplishments

  • Built a reasoning graph that connects expertise to opportunities
  • Created a transparent AI pipeline with verifiable citations
  • Achieved 85% reduction in recruiting cost through reasoning-based matching
  • Delivered a functional two-sided portal for researchers and recruiters
  • Demonstrated a multi-step reasoning process with interpretable outputs
  • Proved papers can autonomously generate funding opportunities

What we learned

  1. Peer Review is Verification: Papers validated by experts provide stronger proof than résumés.
  2. Research ≠ Final Solution: Papers show reasoning ability—what hiring truly needs.
  3. Problems Drive Knowledge: Following Popper’s view, progress comes from tackling evolving problems, not static roles.
  4. Citations Build Trust: Perplexity’s grounded answers make AI matching credible.
  5. Better Questions Improve Outcomes: “Who has reasoned through X?” is more powerful than “Who has X on their résumé?”
  6. Living Expertise Graph: Once published, a paper continuously participates in new reasoning matches.

Main takeaway: Perplexity ReSearcher is not just a hiring tool—it’s reasoning infrastructure for human expertise.

What's next for Perplexity ReSearcher

Immediate (0–3 months)

  • Integrate official OpenReview API
  • Pilot program at a major ML conference
  • Add sponsorship payment via Stripe Connect

Medium Term (6–12 months)

  • Expand to major venues: NeurIPS, ICML, ICLR, CVPR, ACL
  • Add reasoning graph analytics and cross-conference expertise mapping
  • Enable real-time collaboration recommendations

Long Term (2–3 years)

  • Transform conferences into self-funding ecosystems via sponsorships
  • Extend reasoning graph beyond academia (GitHub, patents, blogs)
  • Establish “Reasoning as a Service” infrastructure for global expertise discovery

Built with curiosity. Powered by reasoning. Grounded in evidence. Papers autonomously generate funding opportunities.

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