Automatic Opportunity Discovery
AI-powered opportunity ingestion from community posts, email forwards, and trusted web sources
The challenge & our answer
The Problem
Valuable opportunities—funding calls, partnership requests, volunteer needs—are scattered across email inboxes, community forums, and partner websites. Organizations miss opportunities because they're buried in noise, arrive too late, or never reach the right people. Manual aggregation is unsustainable at scale.
The Solution
Action Network Worldwide automatically discovers, extracts, and imports opportunities from multiple sources using AI-powered classification and structured extraction. Every imported opportunity carries trust signals (source reliability, extraction confidence, verification status) so users can make informed decisions. The system learns from human feedback to improve accuracy over time.
Key Capabilities
Multi-Source Discovery
Scans community posts, shared email inboxes (IMAP), and configured web sources. Each source has its own trust level and processing rules.
AI Classification & Extraction
Large language models read arbitrary content and extract structured opportunity fields: type, title, description, tags, eligibility, deadline, funding amount, contact info.
Trust-Aware Moderation
Every opportunity gets a composite trust score (0-1) based on source reliability, extraction confidence, content completeness, and freshness. High-confidence entries auto-publish; lower-confidence entries queue for human review.
Smart Deduplication
Detects exact duplicates (same URL/ID) and semantic duplicates (same opportunity described differently) using vector similarity. Prevents flooding from cross-posted content.
Provenance Tracking
Every imported opportunity stores its full processing history: raw content, extraction model, classification scores, review decisions, and human verification. Nothing is lost in the pipeline.
Configurable Pipeline
Admins control which sources are active, set per-source trust levels, adjust confidence thresholds for auto-publish vs. review, and toggle approval requirements—all at runtime, no code changes.
How It Works
Source Polling
The system checks configured sources (community posts, email inbox, web feeds) on a schedule. New content is detected and queued for processing.
AI Classification
A large language model reads the content and decides: is this an actionable opportunity? If yes, it extracts structured fields (type, title, description, tags, eligibility, deadline). If no, the item is rejected with a reason.
Trust Scoring
The system computes a composite trust score from four signals: source trust (known portal vs. random blog), extraction confidence (how sure the AI is), completeness (how many fields were filled), and freshness (recent content scores higher).
Deduplication Check
Before importing, the system checks for exact duplicates (same URL or external ID) and semantic duplicates (vector similarity above threshold). Duplicates are flagged, not imported.
Routing Decision
Based on the trust score and admin-configured thresholds: high-confidence opportunities auto-publish as active; medium-confidence entries go to draft for review; low-confidence entries are rejected but tracked for audit.
Human Verification
Admins review queued opportunities in a dedicated dashboard. They can approve (sets verification status to human-verified), reject (removes from public view), or flag for further investigation. Feedback from approvals/rejections improves future extraction.