SourceAI — Autonomous Candidate Sourcing Agent That Fills Your Recruiting Pipeline While Your Team Sleeps
Sourcing managers spend 60% of their week on LinkedIn boolean searches, manual InMails, and copy-paste outreach that could be done by a well-prompted agent in 20 minutes. SourceAI is an AI agent that takes a job description, searches GitHub, LinkedIn, and AngelList, scores candidates against your rubric, and fires personalized outreach sequences — all without a human touching a keyboard. Your sourcing team wakes up to a qualified pipeline.
Difficulty
intermediate
Category
AI Agents & RAG
Market Demand
Very High
Revenue Score
8/10
Platform
AI Agent
Vibe Code Friendly
No
Hackathon Score
6/10
Validated by Real Pain
— sourced from real community discussions
Sourcing teams are running repetitive manual searches and outreach workflows that an AI agent could automate, yet no self-serve tool exists under $1,000/month that handles sourcing, scoring, and first-touch outreach end-to-end.
What is it?
Recruiting teams at Series A to Series C companies spend $8k-$15k/month on RPO firms or extra sourcers for a workflow that is 90% pattern-matching and templated outreach. SourceAI accepts a job description, extracts skill signals, searches GitHub contributions, LinkedIn profiles, and AngelList via Apify scrapers, scores candidates using Claude against a configurable rubric, and triggers personalized first-touch emails via Resend. Results populate a Kanban pipeline in the app where recruiters review, approve, or reject candidates. The agent re-runs daily, keeping the pipeline fresh. Why buildable now: Apify GitHub and LinkedIn scrapers are stable and cheap at $0.10-0.50 per 1,000 results, and the May 2026 AI agent wave means tech recruiting teams are actively budgeting for autonomous sourcing tools.
Why now?
LangChain agent executor stabilized in late 2024, Apify LinkedIn scraping cost dropped 60% in 2025, and the May 2026 talent market tightening has recruiting teams urgently looking for autonomous sourcing that replaces $8k/month RPO retainers.
- ▸Paste a job description and get 50 scored candidates sourced from GitHub and LinkedIn within 2 hours.
- ▸Claude scores each candidate 1-10 against skill, experience, and seniority rubric with a one-paragraph rationale.
- ▸Automated first-touch personalized email sent via Resend with recruiter name and relevant project mention.
- ▸Kanban pipeline where recruiters approve, reject, or move candidates to next stage.
Target Audience
In-house recruiting and sourcing teams at Series A to B startups, roughly 15,000 companies in the US hiring engineers actively.
Example Use Case
Hiring manager at a 40-person startup opens SourceAI Monday morning to 35 scored senior React engineer candidates with personalized outreach already sent, saving the sourcer 18 hours of manual work.
User Stories
- ▸As a recruiting manager, I want 50 scored candidates sourced automatically from a job description, so that I start every week with a full pipeline instead of an empty spreadsheet.
- ▸As a sourcer, I want personalized outreach sent automatically to top candidates, so that I spend time on conversations not copy-paste InMails.
- ▸As a head of talent, I want a Kanban pipeline showing candidate scores and outreach status, so that I can see recruiting velocity at a glance without a weekly status call.
Done When
- ✓Sourcing: done when 20+ scored candidates appear in the pipeline within 2 hours of submitting a job description.
- ✓Scoring: done when each candidate card shows a numeric score and one-paragraph Claude rationale the recruiter can read in 10 seconds.
- ✓Outreach: done when recruiter approves a candidate and a personalized email appears in the test inbox within 60 seconds.
- ✓Pipeline: done when recruiter drags a candidate card to Interview Scheduled column and the status updates without page reload.
Is it worth building?
$299/month x 30 teams = $8,970 MRR at month 3. $299/month x 150 teams = $44,850 MRR at month 9. Conservative given the RPO alternative costs $8k+/month.
Unit Economics
CAC: $80 via LinkedIn DM to heads of talent. LTV: $3,588 (12 months at $299/month). Payback: under 1 month. Gross margin: 84%.
Business Model
SaaS subscription
Monetization Path
$299/month per role (50 candidates sourced, 20 outreach emails), $799/month unlimited roles. 14-day free trial.
Revenue Timeline
First dollar: week 3 via beta conversion. $1k MRR: month 2. $5k MRR: month 5. $15k MRR: month 10.
Estimated Monthly Cost
Apify: $50 at 100k results, Claude API: $30, Resend: $10, Vercel: $20, Supabase: $25, Stripe fees: $25. Total: ~$160/month.
Profit Potential
Full-time viable at $8k–$20k MRR. Strong upsell to ATS integration and multi-team plans.
Scalability
High — add ATS integrations (Lever, Greenhouse), multi-role campaigns, and sourcing analytics by role type.
Success Metrics
Week 2: 5 beta teams with first automated pipeline live. Month 2: 25 paying teams. Month 3: average 40 candidates sourced per job per week.
Launch & Validation Plan
DM 20 recruiting managers on LinkedIn offering to run a free 50-candidate sourcing batch for one open role in exchange for a 30-minute feedback call.
Customer Acquisition Strategy
First customer: post in r/recruiting and r/humanresources with a short video of the pipeline populating in real time, offer free first role. Ongoing: LinkedIn outreach to heads of talent at Series A companies, partnership with 5 recruiting consultants who resell.
What's the competition?
Competition Level
Medium
Similar Products
Gem for sourcing CRM, SeekOut for talent intelligence, hireEZ for outreach automation — all enterprise-priced at $15k-$50k/year with no self-serve option.
Competitive Advantage
Gem and SeekOut cost $20k+/year with enterprise contracts. SourceAI is self-serve at $299/month with a working pipeline in under 2 hours.
Regulatory Risks
LinkedIn Terms of Service restrict automated scraping — use only public profile data via Apify compliant scrapers. GDPR requires candidate data deletion on request. EEOC rules apply if scoring inadvertently creates disparate impact.
What's the roadmap?
Feature Roadmap
V1 (launch): sourcing agent, scoring, outreach, Kanban pipeline. V2 (month 2-3): daily re-run cron, reply tracking, multi-role. V3 (month 4+): Greenhouse ATS sync, diversity analytics, team accounts.
Milestone Plan
Phase 1 (Week 1-2): LangChain agent, Apify scraping, Claude scoring working end-to-end. Phase 2 (Week 3): Resend outreach, Kanban UI, Stripe billing. Phase 3 (Month 2): 15 paying teams, daily cron live.
How do you build it?
Tech Stack
Next.js, Claude API, Apify for LinkedIn and GitHub scraping, Resend for outreach emails, Supabase, Stripe — build agent orchestration with Cursor, pipeline UI with Lovable.
Suggested Frameworks
LangChain for agent orchestration, Apify SDK for scraping, Supabase pgvector for candidate embedding and similarity search.
Time to Ship
3 weeks
Required Skills
LangChain agent orchestration, Apify scraping, Claude scoring, Resend email, Supabase pgvector, Kanban UI.
Resources
Apify LinkedIn scraper docs, LangChain agent executor docs, Claude rubric scoring prompts, Resend transactional email docs.
MVP Scope
app/page.tsx (landing), app/dashboard/page.tsx (active roles and pipeline), app/api/source/route.ts (agent trigger), app/api/candidates/route.ts (CRUD and scoring), app/api/outreach/route.ts (Resend send), lib/agent.ts (LangChain orchestrator), lib/scorer.ts (Claude rubric evaluator), lib/db/schema.ts (roles + candidates + outreach schema), lib/apify.ts (scraper helper), .env.example.
Core User Journey
Paste job description -> configure rubric -> click run -> review scored candidates in pipeline -> approve outreach -> candidates reply to recruiter email.
Architecture Pattern
Job description submitted -> LangChain agent fires Apify scrapers -> candidate profiles collected -> Claude scores against rubric -> results stored in Supabase -> Resend fires personalized outreach -> pipeline Kanban updated -> daily cron re-runs agent.
Data Model
User has many Roles. Role has many Candidates. Candidate has score, rationale, source URL, and outreach status. Role has one Rubric. Candidate has many OutreachEvents.
Integration Points
Apify for LinkedIn and GitHub profile scraping, Claude API for candidate scoring and rationale, Resend for personalized outreach emails, Supabase pgvector for candidate embeddings, Stripe for billing.
V1 Scope Boundaries
V1 excludes: Greenhouse and Lever ATS sync, multi-recruiter team accounts, video screening, diversity filtering, mobile app.
Success Definition
A recruiting manager at a company the founder has never met runs a full sourcing campaign, approves 10 candidates, and books interviews without any founder help.
Challenges
LinkedIn scraping is legally grey — must use only Apify public profile scrapers and avoid bulk scraping behind login walls or face a cease-and-desist risk that kills the product overnight.
Avoid These Pitfalls
Do not scrape LinkedIn behind authentication — only use public profile data or Apify will get your account banned within a week. Do not build ATS integration before 20 paying customers exist.
Security Requirements
Supabase Auth with Google OAuth, RLS on all candidate and role tables, Apify API key stored server-side only, rate limit 5 sourcing runs/day per account, GDPR candidate deletion endpoint.
Infrastructure Plan
Vercel for Next.js and API routes, Supabase for Postgres and pgvector, Vercel Cron for daily agent runs, GitHub Actions CI, Sentry for errors, ~$160/month total.
Performance Targets
30 DAU at launch, sourcing agent run under 2 hours for 50 candidates, pipeline load under 1.5s, outreach send under 60s per candidate.
Go-Live Checklist
- ☐Apify scraper tested at 100-profile batch.
- ☐Resend email deliverability verified.
- ☐Stripe 14-day trial gate tested.
- ☐Sentry live.
- ☐GDPR candidate deletion endpoint tested.
- ☐Custom domain with SSL set up.
- ☐Privacy policy with candidate data handling published.
- ☐5 beta recruiting teams ran full sourcing cycle.
- ☐Launch post drafted for r/recruiting and LinkedIn.
First Run Experience
On first run: a demo role for Senior React Engineer is pre-loaded with 25 pre-scored sample candidates in the Kanban pipeline. User can immediately read candidate cards, click approve to see a sample outreach email draft, and explore the full workflow without connecting any accounts. No API keys or LinkedIn credentials needed for the demo.
How to build it, step by step
1. Define schema: Role, Candidate (score, rationale, source, email, status), OutreachLog. 2. Scaffold Next.js with Supabase auth and Drizzle schema. 3. Build LangChain agent in lib/agent.ts that accepts job description, calls Apify GitHub and LinkedIn scrapers, returns profile list. 4. Build Claude scorer in lib/scorer.ts that evaluates each profile against extracted rubric and returns score 1-10 plus rationale. 5. Implement POST /api/source that triggers agent, stores candidates in DB with scores. 6. Build Kanban pipeline UI with approve, reject, and move-to-interview columns using Lovable. 7. Implement POST /api/outreach that generates personalized email via Claude and sends via Resend. 8. Add Vercel Cron job that re-runs agent daily for active roles. 9. Wire Stripe billing with role-count gate and 14-day trial middleware. 10. Verify: paste a real React engineer job description, confirm 20+ scored candidates appear in pipeline within 2 hours and one outreach email lands in a test inbox.
Generated
May 31, 2026
Model
claude-sonnet-4-6
Disclaimer: Ideas on this site are AI-generated and may contain inaccuracies. Revenue estimates, market demand figures, and financial projections are illustrative assumptions only — not financial advice. Do your own research before making any business or investment decisions. Technology availability, pricing, and market conditions change rapidly; always verify details independently.