Recon is an AI-driven misinformation verification system that analyzes any text, article, or link and classifies it as Verified, Potential Misinformation, or Unclear with concise reasoning and source-backed evidence. The system extracts factual claims, reasons about context and consistency, searches for corroborating evidence from reputable outlets, and returns a structured verdict with supporting citations.

How it works
Claim extraction: Natural-language parsing isolates factual claims from input text or web pages.
Agent AI reasoning: The Agent AI evaluates claims for internal consistency, factual plausibility, and contradiction with known context.
Trusted-source aggregation: The system searches and ranks corroborating materials from reputable publishers and fact-checking bodies.
Verdict & explanation: Outputs a structured result — label, confidence score, claim list, concise explanation, and source list.

Tech stack
Frontend: Next.js + TypeScript + TailwindCSS + Framer Motion (interactive, animated chat-like UI).
Backend: Python (Flask) microservice architecture for orchestration and lightweight persistence.
Core ML/AI: Agent AI for claim detection, contextual reasoning, and confidence scoring.
Data processing: BeautifulSoup-like HTML parsing, text normalization, tokenization, and claim-segmentation modules.
Storage & infra: Lightweight datastore for session history and indexing; containerized deployment for scalability.

** Work flow**
Ingest: Accept text or URL → fetch and extract readable content.
Preprocess: Normalize text, remove noise, segment sentences, and identify candidate claims.
Claim scoring: For each claim, compute signals factuality features, temporal plausibility, internal consistency, and semantic novelty.
Evidence retrieval & ranking: Query the internal trusted-source aggregator, fetch matching passages, and rank evidence by credibility heuristics (source authority, recency, direct match score).
Agent reasoning: Combine claim scores and ranked evidence to produce a final classification and concise human-readable explanation.
Output: Return structured JSON-like result containing label, confidence, explanation, claims, and top evidence links.

Future scope & roadmap
Real-time social feed monitoring and alerting for emerging misinformation clusters.
Multilingual pipelines to detect region-specific misinformation (support for low-resource languages).
Confidence visualization: credibility heatmaps and per-claim scoring for intuitive risk assessment.
Editor / newsroom integration: lightweight plugin for journalists to verify claims during reporting workflows.
Federated verification network: combine signals from independent verifiers to improve robustness and reduce false positives.
Human-in-the-loop workflows: flagging, curator feedback, and continuous model fine-tuning with verified labels.

Impact
Recon delivers an operational-grade verification engine that helps teams rapidly identify and contextualize misinformation, improving decision-making and protecting information integrity.