AI-native talent infrastructure. Ready to acquire.
A complete talent marketplace — intake to invoicing — with AI agents replacing the operations team. Seven years of iteration, standard stack, zero lock-in.
Why this is worth acquiring.
Instant White-Collar Talent Vertical
75,000 professionals, already indexed and matched. Marketers, designers, engineers, operators, and finance talent — a ready-made talent pool that expands any staffing or marketplace acquirer into white-collar categories on day one.
AI Agent Layer for Ops Automation
Replace hours of manual work at a fraction of the cost. Two production agents handle matching, job posting, and knowledge management today. The reusable pattern ships new agents in days — operational leverage that scales with your customer base, not your headcount.
Vertical-Agnostic Matching Engine
One engine, any talent vertical. Multi-signal scoring (semantic search + ELO ranking + LLM reranking) works on skill-to-requirement fit, incorporating domain context. The same scoring pipeline generalizes across verticals — from technical roles to industry-specific positions. Portable by design.
Full Lifecycle, Standard Stack
Contracts, payments, time tracking, invoicing — all native. Node.js, PostgreSQL, React, GCP. No proprietary dependencies, no vendor lock-in. Deploys on any cloud, integrates with any existing system.
Unit economics that compound with automation.
Backed by Y Combinator, Pangea optimized for unit economics and platform leverage — the metrics that compound post-integration. 24x revenue-per-placement growth and margins expanding to 27%, all before the full agent layer is deployed. Production numbers, not projections.
Want to see it live? Book a call with Adam — we'll open the product and the codebase.
Seven steps. One platform.
Every step from first form submission to final payment is built and running natively. AI agents layer on top to automate the highest-leverage workflows.
Meeting Agent
$0.13 / meetingReplaces ~2 hours of post-meeting ops per call. Extracts insights, updates the knowledge base, and files tickets — no human involvement. At scale, thousands of hours reclaimed annually.
Job Posting Agent
Minutes, not hoursA single sales call transcript becomes published job posts — company profile, requirements, and live listing. Scales job creation across every customer without adding headcount.
Match Agent
Hybrid search + LLMRuns BM25 + semantic retrieval across 75K profiles, applies performance ranking, then LLM-reranks to a 3–5 candidate shortlist — end to end in minutes.
Roadmap
ShippingJob Manager · Contract Manager · Sales Qualification — same proven pattern, new workflows.
75,000 profiles to 3–5 top candidates.
Hybrid search (BM25 + semantic) combined with performance ranking and LLM reranking. Three independent layers produce a single ranked shortlist — portable across any talent vertical.
Qualification Funnel
Three people built this. That's the point.
Three engineers own the full stack — backend, matching engine, agent layer, and infrastructure. That means fast integration, zero organizational overhead, and direct access to every decision that shaped the platform. All available to transition.
Brown University & MIT. Built Pangea from zero to a production platform serving enterprise clients including Bechtel and SecurityScorecard. Leads product, strategy, and go-to-market.
Applied Mathematics, Brown University. Architected the full technical stack — matching engine, agent infrastructure, and platform backend.
Leads creative direction and AI implementation across the platform, from product design to agent development.
Seven years of iteration. Ready to integrate.
Standard stack, no lock-in, no proprietary dependencies. Everything here is running in production today.
Profiles with embeddings
Already indexed and ready for matching on day one.
Production agents running
Meeting Agent and Job Posting Agent. Reusable pattern for building more in days.
Years of platform iteration
Contracts, payments, time tracking, invoicing — all running in production for 7+ years.
Proprietary dependencies
Standard stack: Node.js, PostgreSQL, React, GCP. Deploy on any cloud.
The moat is in the data. Seven years of placement history power the ELO ranking system. 75K profiles have vector embeddings tuned to actual hiring outcomes. Two production agents process real transactions daily. The matching engine improves with every placement — that feedback loop and the data behind it are what take years to build.
Backend
- Node.js / TypeScript
- Koa.js
- PostgreSQL + TypeORM
- Redis + RabbitMQ
Search & AI
- Elasticsearch 7.17
- Pinecone vector search
- Claude Agent SDK
- DSPy + OpenAI embeddings
Frontend
- React 18 + Vite
- MUI component library
- Redux
Admin
- Next.js 15 (App Router)
- React 19 + shadcn/ui
- React Query
Infrastructure
- GCP Kubernetes
- Railway
- Docker
Payments
- Stripe billing
- Invoicing + payouts
Analytics
- PostHog
- Customer.io
- Segment
Knowledge
- Markdown / Git KB
- Auto-updated by agents
- Grows with every interaction